2.026 Ofertas de Aprendizaje Automático en Argentina

Buscamos un Machine Learning / AI Engineer Senior con un sólido enfoque en MLOps y Python que también pueda desempeñarse como AI Engineer para soluciones de IA generativa. Será responsable registrar experimentos, métricas y modelos, construir pipelines reproducibles para entrenamiento, validación o inferencia asi tambien como mantener versiones de datos, artefactos y modelos, garantizando trazabilidad y colaboración.

Conocimientos requeridos

Lenguaje y frameworks de ML & Generative AI

• Python 3.xx (tipos, Pydantic, AsyncIO) con ecosistema científico (Pandas, NumPy,Polars).
  Frameworks de deep learning: PyTorch- 2.x, TensorFlow 2.x/Keras, JAX/Flax.
• Frameworks de agentes y orquestación GenAI: LangChain, LangGraph,LlamaIndex, CrewAI, AutoGen.
  
  Plataforma MLOps & Experimentación
• Tracking y registro de modelos: MLflow Tracking + Model Registry
• Orquestación de pipelines: Kubeflow Pipelines, Argo Workflows, AWS, SageMaker Pipelines, Vertex AI Pipelines.
• Versionado de datos/artefactos: DVC, LakeFS.
  
  Observabilidad y monitoreo
• Métricas y trazabilidad: Prometheus, Grafana, OpenTelemetry.
• Calidad de datos/modelos: Evidently AI, WhyLabs.
  
  Vector DB, RAG & búsqueda semántica
• Bases vectoriales: Pinecone, Weaviate, Milvus, pgvector.
• Toolkits RAG: LangChain Retrievers, LlamaIndex integrations.
  
  Entorno Cloud & Serverless GenAI
• Servicios managed GenAI: AWS Bedrock, Azure OpenAI Service, Google Gemini, Anthropic Claude.
• Serverless/event‐driven: AWS Lambda, Google Cloud Functions, Cloud Run,Requisitos del perfil
  +5 años de experiencia en ML/AI engineering, con- 2 años dedicados a MLOps en producción.
• Experiencia desplegando sistemas RAG a escala.
• Capacidad para liderar iniciativas técnicas y coordinar equipos multidisciplinarios.
• Residencia en Buenos Aires, Argentina o disponibilidad para esquema híbrido.
• Valorable experiencia en sector financiero o consultoría tecnológica B2B.

Responsabilidades clave

• Diseñar y mantener pipelines de ML y LLMs (entrenamiento, fine‐tuning, inferencia,
  
  retraining).
• Desplegar modelos ML en producción.
• Implementar y gobernar vector stores y pipelines RAG para exponer capacidades
  
  de IA generativa sobre datos corporativos.
• Colaborar en la operacion del equipo (estimaciones técnicas, demos y RFPs).
• Liderar revisiones de arquitectura con foco en escalabilidad, resiliencia y
  
  compliance.
• Investigar continuamente la evolución de la IA generativa para proponer
  
  innovaciones.

Inallmedia is a forward-thinking organization committed to leveraging advanced technologies to enhance our software products and applications. Our team is dedicated to implementing cutting-edge AI/ML solutions that drive innovation and efficiency. We are seeking a highly skilled Machine Learning Engineer to join our inception team, a pivotal group that will augment our existing team and spearhead AI/ML initiatives.

The Machine Learning Engineer will be integral to our AI/ML initiative's success. This role involves developing and deploying machine learning models, collaborating with cross-functional teams, and ensuring seamless integration of AI/ML solutions into various software products and applications. The successful candidate will possess a strong background in Python, R, SQL, and Azure, and will be instrumental in allowing the team to grow and achieve its strategic objectives.

• Develop and Implement AI/ML Solutions: Design, develop, and deploy machine learning models and algorithms to solve complex business problems and enhance software products.
• Collaborate with Cross-Functional Teams: Work closely with data scientists, software engineers, product managers, and other stakeholders to integrate AI/ML solutions seamlessly.
• Data Analysis and Management: Conduct thorough data analysis, preprocessing, and management using Python, R, and SQL to ensure high-quality datasets for model training.
• Model Evaluation and Optimization: Evaluate model performance, perform hyperparameter tuning, and optimize models to achieve desired accuracy and efficiency.
• Azure Integration: Utilize Azure cloud services for model deployment, monitoring, and scaling to ensure robust and scalable AI/ML solutions.
• Continuous Improvement: Stay updated with the latest advancements in AI/ML technologies and methodologies, and apply them to enhance existing solutions.

• Proficiency in Python: Extensive experience in developing machine learning models and data analysis using Python.
• Expertise in R: Strong knowledge of R for statistical computing and data analysis.
• SQL Skills: Proficient in SQL for data manipulation, querying, and management.
• Azure Experience: Hands-on experience with Azure cloud services for deploying and managing AI/ML solutions.
• Work Experience: A minimum of 8+ years of work experience in machine learning, data science, or a related field.
• Educational Background: Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field.
• Problem-Solving Skills: Excellent analytical and problem-solving abilities, with a keen eye for detail.
• Collaboration and Communication: Strong collaboration and communication skills to work effectively with cross-functional teams.

Join Our Data Products and Machine Learning Development Remote Startup!

Mutt Data is a dynamic startup committed to crafting innovative systems using cutting-edge Big Data and Machine Learning technologies.

We’re looking for a Machine Learning Engineer to help take our expertise to the next level. If you consider yourself a data nerd like us, we’d love to connect!

What We Do:

1. Leveraging our expertise, we build modern Machine Learning systems for demand planning and budget forecasting.
2. Developing scalable data infrastructures, we enhance high-level decision-making, tailored to each client.
3. Offering comprehensive Data Engineering and custom AI solutions, we optimize cloud-based systems.
4. Using Generative AI, we help e-commerce platforms and retailers create higher-quality ads, faster.

Our Partnerships:

• Amazon Web Services
• Google Cloud
• Astronomer
• Kaszek
• Product Minds
• H2O.ai
• Soda

Our values:

• We are Data Nerds
• We are Open Team Players
• We Take Ownership
• We Have a Positive Mindset

Curious about what we’re up to? Check out our case studies and dive into our blog post to learn more about our culture and the exciting projects we’re working on!

Responsibilities:

1. Lead Model Productization: Implement MLOps practices for model deployment (orchestration, monitoring, testing).
2. ML POC Development: Collaborate on ML Proof of Concepts for internal and client projects.
3. Model Lifecycle Management: Optimize ML models for performance, memory, and throughput.
4. Translate Business Needs: Convert business requirements into technical solutions, balancing trade-offs.
5. Stay Ahead: Research new ML tools and techniques to enhance our systems.
6. Bridge DS & DE: Connect Data Science and Engineering teams for smooth application development.
7. Plan Projects: Help define project timelines and estimates.
8. Share Knowledge: Document and share best practices across teams.
9. Support Hiring: Participate in technical interviews and evaluations.
10. Safeguard information security, ensuring responsible data usage and compliance with best practices for protection and confidentiality.

We don’t expect you to check every box, but here’s what we’re looking for:

Required Skills:

1. Proven Experience: Experience as ML Engineer
2. AI/ML Expertise: Solid understanding of ML models, deep learning, and statistical analysis.
3. Tech Stack Know-How: Experience with MLOps tools (Airflow, MLflow, DBT) and cloud services (AWS, GCP).
4. Programming Skills: Strong in Python and at least one other language.
5. MLOps: Experience managing model lifecycles and setting up CI/CD pipelines.
6. Collaboration: Team player with strong communication skills.
7. Languages: Intermediate English, Advanced Spanish.

Nice to Have:

1. Modern Data Stack: Familiar with tools like Kafka, DuckDB, Apache Pinot.
2. Cloud AI Services: Hands-on with AWS Sagemaker, GCP Vertex AI.
3. Software Development: Experience with software engineering practices.
4. Problem-Solver: Positive, proactive attitude towards challenges.
5. Consulting Experience: Worked in client-facing roles.
6. Cloud Certifications: AWS, GCP, or related certifications.
7. Python Libraries: Experience with libraries like SQLAlchemy, Great Expectations.

Why Join Us?

At Mutt Data, we offer a remote-first culture with flexibility, autonomy, and awesome benefits:

• Salary in USD (up to 20% bonus).
• Remote-First Culture
• ️️ Gympass or sport club stipend.
• Paid AWS & Databricks Certifications.
• Food credits via Pedidos Ya.
• Birthday off + extra vacation week ( Mutt Week! ).
• Referral bonuses to grow our team.
• ️ Annual Mutters' Trip – Join us for an unforgettable team trip!

Even if you don’t meet every requirement, we’d love to hear from you! Let us know why you think Mutt Data is the next step in your journey.

Apply now and join a team that’s shaping the future of Machine Learning and Data Engineering.

Buscamos un/a profesional apasionado/a por la inteligencia artificial y con experiencia en proyectos innovadores de Generative AI. Si tenés conocimientos en desarrollo y automatización de soluciones basadas en Machine Learning y disfrutás trabajando con tecnologías de última generación, ¡esta oportunidad es para vos!

1. Participar en el desarrollo e implementación de soluciones de Machine Learning y Generative AI.
2. Desarrollar aplicaciones utilizando Python como lenguaje principal.
3. Automatizar procesos para optimizar tareas y mejorar la eficiencia.
4. Colaborar en proyectos que integren servicios avanzados como APIs y plataformas en la nube.

1. Experiencia demostrable en proyectos de Generative AI.
2. Buen nivel de desarrollo en Python.
3. Experiencia en implementación de soluciones de ML y/o GenAI.
4. Experiencia en automatización de procesos.
5. Experiencia con Azure OpenAI Services.
6. Experiencia con Llama 3.2.
7. Experiencia en desarrollo de APIs.
8. Nociones de DevOps.

We're SweedPos, a product-driven startup building an all-in-one cannabis retail platform. We’re on the lookout for a Machine Learning Tech Lead to join our team remotely and help us scale and optimize our platform.

At Sweed, we’re reimagining how cannabis retailers operate. Our enterprise-grade platform combines POS, eCommerce, Marketing, Analytics and Inventory Management into a single, seamless solution—eliminating the need for multiple third-party tools.

We believe in simplicity, efficiency, and innovation. That’s why we build for scalability and performance, making life easier for cannabis retailers while driving real business growth.

At Sweed, we believe in the medicinal potential of cannabis. It has been shown to help with chronic pain, anxiety, depression, and many other conditions. Despite the lingering stigma, we see cannabis as a powerful tool for improving lives.

The industry is evolving rapidly, and we’re here to drive that transformation—making cannabis retail more efficient, accessible, and customer-friendly.

We’ve been on the market for 7 years, continuously growing and refining our product.

Our focus is on earning customer trust, which means constantly improving our delivery processes and rolling out new features. At the same time, we navigate the complex legal landscape of the cannabis industry, ensuring our platform remains compliant and future-proof.

Our total team size is over 200 people: The development team is distributed globally and organized into cross-functional product teams. These teams typically consist of 8–12 members, including front-end and back-end developers, QA specialists, and analysts. Each team is led by a Team Lead and a Product Owner, ensuring effective collaboration and clear direction. Meanwhile, our CEO, account managers, and customer success team are based in the USA, working closely with us to align product development with business and user needs.

As ML Tech Lead, you’ll own the technical foundation of all ML initiatives at Sweed. Your focus will be on designing and scaling the ML infrastructure, ensuring high-quality model development, deployment, and monitoring. You’ll drive MLOps best practices, lead engineers/data scientists, and make sure our ML systems are reliable, scalable, and production-ready.

• Lead the technical architecture and infrastructure for ML systems, ensuring scalability and reliability.
• Own the full ML engineering lifecycle: data pipelines, model training, deployment, monitoring, retraining.
• Define and enforce MLOps standards: versioning, testing, CI/CD, observability.
• Mentor engineers and data scientists, fostering technical excellence.
• Collaborate with the Data Platform team to ensure robust and high-quality data foundations.
• Evaluate and introduce new tools, frameworks, and practices to keep our ML stack modern and efficient.
• Partner with the ML Product Lead to translate business goals into feasible technical solutions.

• 5+ years in Machine Learning engineering, with proven experience in production ML systems.
• Strong hands-on expertise in Python, SQL, and modern ML frameworks.
• Experience with cloud ML services (AWS SageMaker preferred) and containerization (Docker).
• Deep understanding of MLOps, CI/CD, data pipelines.
• Ability to lead technically while staying hands-on.
• Excellent problem-solving and system design skills.

• Proactivity – We love team members who take initiative and provide feedback
• Critical thinking – We value problem-solvers who think beyond just writing code
• Adaptability – Our industry is evolving fast, and we need people who thrive in change

• Salary in USD (B2B contract with the US company)
• 100% remote – We’re a remote-first company, no offices needed!
• Flexible working hours – Core team time: 09:00-15:00 GMT (flexible per team)
• 20 paid vacation days per year
• 12 holidays per year
• 3 sick leave days
• Medical insurance after probation
• Equipment reimbursement (laptops, monitors, etc.)

• Recruiter Call (up to 45 minutes) – Quick intro & optional tech screen
• Hiring Manager call (up to 45 minutes) - Deep dive into your ML background
• Technical Interview (up to 1.5 hours) – Deep dive into ML skills

We're looking for a Machine Learning Engineer who loves building real products and shipping code. If you enjoy owning production systems, solving tough engineering problems, and bringing cool ML research into real-world applications, you'll love it here

Identity security today sucks. People hate passwords, 2FA codes, and security questions; it's an endless cycle of frustration. At TWOSENSE.AI, we're fixing this using AI-powered behavioral biometrics. The system we created automatically recognizes people by their unique behaviors—how you type, move the mouse, or even walk—creating the world's first invisible, private biometric. No passwords, no puzzles—just seamless security that's always on. Our mission is to fundamentally change secure human-computer interactions, making forgotten passwords and frustrating authentication a thing of the past.

We're an engineer-founded and led team of PhDs and exceptional software engineers based in Brooklyn, NYC. Transparency, autonomy, continuous improvement, and strong engineering culture matter deeply to us. Right now, we're fully remote and plan to stay flexible for the foreseeable future. As an early team member, you'll directly shape our strategy, trajectory, and your own career as you grow with us.

• Build and maintain our production ML pipeline—including ETL processes, data cleaning, preprocessing, feature extraction, training, evaluation, deployment, and monitoring.
• Develop streamlined ML workflows to effectively support our production systems.
• Write clean, maintainable Python code using test-driven or test-first development practices.
• Collaborate closely with founders and researchers to bring ML ideas to life—with opportunities to participate directly in research projects.
• Optimize our infrastructure to handle growth and scale effectively.

• Strong software engineering skills—grounded in SOLID principles and best practices.
• Hands-on experience deploying ML models to production.
• Experience with common ML libraries like scikit-learn, TensorFlow, or PyTorch.
• Basic understanding of ML fundamentals (algorithms, math/stats), along with strong intuition for how and when to apply different modeling approaches.

• Familiarity with developing and deploying ML systems using AWS tools and infrastructure.
• Experience with varied data types (structured, time-series).
• Previous experience with behavioral biometrics or security-focused products.
• Previous experience with ONNX.

Salary ranges:

• Argentina - $70,000 - $90,000

We genuinely care about our people. Here's how:

• Flexible working—remote or in-office, whatever suits you best.
• Project Day once a month—dedicate a day to something you're passionate about.
• Equity—share directly in our success.
• Open vacation policy—take time off whenever you need.
• Subscription to online learning resources like O'Reilly and Pluralsight.

Extra perks for our Argentina-based folks:

• Business English courses.
• Travel for team events and company meet-ups.
• Technical books, VPN, and high-end work equipment provided.

We're growing fast, and you'll have plenty of opportunities to shape the company, influence our direction, and rapidly grow your career.

We are actively seeking a highly skilled ML/AI/NLP Developer to join our team and lead the development of consumer-oriented features. If you're passionate about artificial intelligence, innovation, and using data-driven solutions to solve real product challenges, this is your opportunity to make an impact.

We're looking for someone with strong technical and analytical abilities, capable of independently conducting research and delivering smart, efficient solutions. In this role, you'll collaborate with stakeholders to understand product needs and goals, and then leverage your expertise in ML/AI/NLP to build and implement improvements that truly make a difference.

Once opportunities for enhancement are identified, you will autonomously develop and implement solutions, working closely with developers and product managers to deploy these features to end-users. You will also be responsible for continuously monitoring and optimizing the performance of these features based on user feedback and data insights.

We're looking for a proactive team player ready to take the lead on our ML/AI/NLP initiatives and committed to staying current with the latest advancements in the field.

Responsibilities:

• As a ML/AI/NLP Developer, you will play a pivotal role in integrating intelligent technologies into our products to improve the user experience and increase customer retention. Your key responsibilities include:
• Taking the lead in advancing our ML/AI/NLP initiatives by identifying and analyzing areas of opportunity within the current product.
• Conducting continuous research on existing ML/AI/NLP technologies and trends, identifying innovative tools and practices we can adopt.
• Designing and proposing creative ML/AI/NLP-based solutions that elevate the customer journey and product experience.
• Collaborating with cross-functional teams—developers, designers, content managers, and product managers—to implement, monitor, optimize, and deploy AI-driven solutions.
• Evaluating and integrating third-party ML/AI/NLP tools and services to expand our internal capabilities.

Requirements:

• 3+ years of hands-on experience developing and deploying ML/AI/NLP models in real-world applications.
• Proven ability to independently identify product improvement opportunities through ML/AI/NLP technologies, conduct relevant research, and implement tangible solutions

About Trafilea

Trafilea is a dynamic and innovative Tech E-commerce Group that operates multiple direct-to-consumer brands in the intimate apparel and beauty sectors, with a focus on using data-driven strategies to scale their businesses. In addition to our products, we have our own online community dedicated to promoting body positivity. As a rapidly growing global player, Trafilea is committed to creating high-quality products and services that enhance the customer experience and drive long-term growth.

At Trafilea, we foster a culture of collaboration, innovation, and continuous learning. We believe in investing in our people and providing them with the support and development opportunities they need to grow both personally and professionally. With our remote-first approach, you'll have the freedom to work from anywhere in the world, surrounded by a diverse and talented team that spans the globe.

Mission

As a
Senior Machine Learning Engineer
, you'll lead the development and deployment of advanced models, driving innovation and efficiency across the organization. This role involves crafting production-ready code, designing systems within the overarching architecture, and applying scientific methodologies to solve complex problems. Bridging the gap between business strategy and technical execution, you will ensure that machine learning projects align with company goals, delivering tangible value.

With your expertise, we'll harness the power of machine learning to unlock opportunities, streamline processes, and maintain our position at the forefront of technological advancement.

General Accountabilities

• Develop and maintain production-ready machine learning code that is testable, well-documented, and accounts for edge cases and errors.
• Design code that aligns with the service architecture, utilizing effective abstractions and code isolation.
• Write comprehensive tests covering edge cases, errors, and happy paths following the testing pyramid.
• Gain a high-level understanding of the team's domain and develop expertise in a specific portion of it, contributing significantly to the team's projects.
• Apply the scientific method to bring insights from research directly into all areas of the team's project, showcasing innovative ideas, technologies, or techniques.
• Master the application of theory to practice, iterating through the KDD process, understanding the Deep Learning ecosystem, focusing on robust and scalable ML processes, and developing efficient data pipelines.
• Integrate business and technical strategies, contributing to business discussions and aligning technical projects with business goals.
• Ensure tasks are critically reviewed, appropriately sized, and prioritized, and dependencies are managed for continuous integration and incremental delivery.
• Deliver and encourage the delivery of constructive feedback within the team and to business stakeholders, fostering a culture of continuous improvement.
• Communicate effectively in both technical and non-technical terms, ensuring clarity and audience engagement, while actively listening and paying attention to nonverbal cues.
• Share knowledge frequently, contribute to team documentation, and encourage a culture of knowledge sharing and mentorship within the team.

Role-related Competencies & Requirements

• Graduated in Computer Science, Mathematics, Statistics, or a related field with a strong focus on machine learning, or advanced student (min 3rd degree completed)
• Minimum of 5 years of experience in machine learning engineering, with a proven track record of developing and deploying robust, scalable machine learning models.
• Expertise in writing production-ready code, with a strong understanding of the testing pyramid and experience in writing comprehensive tests.
• Deep knowledge of the machine learning development lifecycle, including the KDD process, Deep Learning ecosystems, MLOps practices, and data engineering.
• Ability to integrate business strategy with technical execution, contributing to business discussions and ensuring alignment of machine learning projects with business objectives.
• Strong communication skills, capable of conveying complex technical concepts in simple terms to a diverse audience, and fostering a culture of open, effective communication within the team.
• Leadership in fostering a culture of continuous improvement, knowledge sharing, and mentorship within the team.
• Proficiency in advanced ML tools and programming languages used in data science (e.g., Python, R, SQL).
• Proficiency in ML cloud services and Saas tools like MLFlow and AWS ecosystem.
• Proven experience in marketing, e-commerce marketplace, or growth teams.

Expected Outcomes for initial 3 months

First 30 Days:

Understand the current machine learning landscape, including tools, frameworks, and ongoing projects within the company. Establish relationships with key stakeholders and identify immediate areas for impact.

First 60 Days:

Develop and deploy at least one major machine learning model or improvement, demonstrating expertise in code quality, testing, and alignment with business goals. Begin mentoring team members and contributing to knowledge sharing.

First 90 Days:

Lead a significant machine learning project from concept to deployment, showcasing the ability to integrate technical and business strategies. Implement and advocate for improvements in team practices and processes, enhancing overall efficiency and collaboration.

What we offer

• Collaborate with world-class talents in a data-driven, dynamic, energetic work environment.
• Opportunity to grow and develop both professionally and personally.
• Safe space to be who you truly are, with a commitment to diversity, equity, and inclusion.
• Openness to new ideas and initiatives.
• Great benefits package including remote work, 15 working days of paid holidays, Learning subsidy, and more

Who We Are
SQUIRE is the leading business management system designed for the needs of barbers, shop owners, and their communities.

We believe the pursuit of artistry and autonomy should not be restricted by the complexities of running a business.

With SQUIRE, we provide custom-branded tools, resources, and guidance to help barbers of all stages and experience levels attract and retain more customers, efficiently manage their shop operations, and increase their revenue.

Founded in 2015, SQUIRE is trusted by barbers in 4,000+ shops in more than a thousand cities around the globe.

From streamlined booking and opening new shops to real-time earning dashboards and building lasting customer relationships, SQUIRE supports shop owners in seamlessly bridging the gap between their personal craft and business goals.

SQUIRE enables barbers everywhere to unlock their full potential both as artists and as entrepreneurs.

For more information, please visit

or download the SQUIRE app from the App or Play Store.

We're hiring a Machine Learning Engineer to embed into our product teams and own the entire ML lifecycle—from prototype to production.

This is not a role for model research or experimentation in a vacuum.

We're looking for an engineer who thrives on taking state-of-the-art ML (especially LLMs and AWS-based solutions) and transforming it into deployed, maintainable, and scalable real-world systems that power SQUIRE's products.

If you're passionate about building practical ML applications and turning ideas into shipping features, this role is for you.

Job Duties & Responsibilities
Own the full ML lifecycle for product features—from ideation to deployment, observability, and iteration

Use off-the-shelf models (e.g. in AWS SageMaker, Bedrock, Comprehend, HuggingFace) to solve core user and business problems

Build and maintain robust, scalable ML pipelines and infrastructure (batch and real-time)

Work cross-functionally with product managers, designers, and backend engineers to ship ML-powered features

Drive productionization: model inference, latency tuning, monitoring, logging, and failure recovery

Continuously evaluate and integrate emerging models (especially LLMs) to improve accuracy and capabilities

Bring an engineering mindset to ML work—automate processes, reduce tech debt, and champion good software practices in the ML stack

The duties and responsibilities outlined above are not a comprehensive list, and additional tasks may be assigned based on business needs.

Ideal Requirements & Qualifications
3+ years of experience in machine learning engineering, ideally within a product-oriented tech team

Strong software engineering background with experience writing clean, scalable, and maintainable code in Python

Hands-on experience using cloud-based ML services (especially AWS) and pre-trained models in production

Experience with modern deployment stacks and MLOps: CI/CD, model versioning, monitoring, etc

A pragmatic mindset—you value impact, simplicity, and reliability over academic perfection

NICE TO HAVE

Experience integrating LLMs into products via APIs or fine-tuning

Familiarity with React Native applications or full-stack development environments

Prior work in consumer or SMB-facing products.

WHY JOIN SQUIRE?

Work on impactful, customer-facing features powered by machine learning

Help shape our AI-first engineering culture

Enjoy autonomy and ownership over your domain with the support of an exceptional engineering team

Competitive salary, equity, and benefits in a high-growth, mission-driven company

Interview Accommodations

SQUIRE is committed to working with and providing reasonable assistance to individuals with physical and mental disabilities.

If you are an individual with a disability requiring an accommodation to apply for an open position, please email your request to *** and someone on our team will respond to your request.

Equal Employment Opportunity

SQUIRE provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

This applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.

Pay Transparency Nondiscrimination Provision

SQUIRE will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant.

However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is

• in response to a formal complaint or charge,
• in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or
• consistent with the contractor's legal duty to furnish information.