Senior ML Engineer (ML/AI)

Remote (United States)

Job Details

Location: United States

Workplace: Remote

Employment Type: Full-time

Experience: 6+ years deploying ML/AI solutions in production environments

Core Areas: Machine Learning, Deep Learning, Generative AI (LLMs), Natural Language Processing (NLP), Python, MLOps, Retrieval-Augmented Generation (RAG), ML Pipelines

Compensation: $143,000-$197,000 annually

About the Role

The Senior ML Engineer (ML/AI) will design, develop, and deploy scalable machine learning and artificial intelligence systems that power production applications. This position focuses on building and optimizing deep learning, classical machine learning, and Generative AI models, including large language models (LLMs), to support predictive analytics, natural language processing, and intelligent product capabilities. Key responsibilities include model training, fine-tuning, evaluation, production deployment, and the development of reliable ML infrastructure.

The role combines applied machine learning with production-grade software engineering, using Python, MLOps frameworks, retrieval-augmented generation (RAG), vector databases, and scalable data pipelines. The engineer will collaborate with data scientists, data analysts, backend and frontend engineers, product managers, physicians, and therapists to translate AI research into dependable applications. Responsibilities also include technical leadership, architecture decisions, code reviews, mentoring engineers, and contributing to the AI product roadmap.

What You'll Do

• Design, train, fine-tune, and evaluate deep learning, classical machine learning, and foundation models, including large language models (LLMs) and diffusion models, to support production AI features.
• Develop, scale, and maintain ML pipelines for continuous model training, automated evaluation, and real-time and batch inference using modern MLOps frameworks.
• Build production-ready machine learning systems that integrate predictive analytics, natural language processing (NLP), and Generative AI capabilities into software applications.
• Partner with backend and frontend engineering teams to integrate AI models into microservices while maintaining low latency, high availability, and efficient resource utilization.
• Architect scalable data pipelines for preprocessing, vectorization, and ingestion of large structured and unstructured datasets.
• Establish rigorous model evaluation frameworks covering alignment, bias mitigation, guardrails, and production reliability.
• Optimize model performance, inference latency, and operational costs using techniques such as quantization and distillation.
• Bridge data science research and production software engineering by developing scalable architectures that support reliable model deployment.
• Provide technical leadership, conduct detailed code reviews, and mentor junior and mid-level engineers in machine learning engineering and software development best practices.
• Collaborate with cross-functional technical, product, and clinical stakeholders to translate complex requirements into practical AI solutions and influence the AI product roadmap.

Qualifications

Required Experience

• 6+ years of professional experience deploying machine learning or artificial intelligence solutions in production environments.

Required Skills

• Ability to develop high-quality, maintainable production code in Python.
• Practical experience building retrieval-augmented generation (RAG) solutions for AI-powered applications.
• Experience setting up, configuring, and maintaining vector databases.
• Strong interest in developing machine learning and AI-based products and applying emerging technologies to practical problems.
• Ability and willingness to learn new technologies quickly and apply them effectively.
• Sound judgment in balancing software quality, technical requirements, and delivery deadlines in fast-paced environments.
• Excellent communication skills, including the ability to establish consensus and alignment across technical and cross-functional teams.
• Strong organizational skills and the ability to simplify complex problems, prioritize competing requirements, and identify actionable next steps.

Preferred Qualifications

• Experience defining and working with Protocol Buffers (Protobuf) messages.
• Experience using Docker and deploying applications with Kubernetes.
• Familiarity with relational databases and low-latency database systems.
• Experience using Celery for application task processing.
• Additional hands-on experience implementing RAG architectures and managing vector database infrastructure.
• Experience writing production software in Java or Kotlin.
• Experience building and deploying applications on cloud infrastructure, particularly Amazon Web Services (AWS).
• Experience developing technical solutions involving highly sensitive healthcare data.

Benefits

• Comprehensive healthcare coverage, including medical, dental, and vision insurance.
• Flexible Spending Account (FSA) and Health Savings Account (HSA) options.
• Life insurance and disability insurance.
• Employee access to coaching and therapy services.
• Potential equity compensation through discretionary restricted stock units.
• Paid vacation, sick leave, and company holidays.
• Paid parental leave.
• 401(k) retirement plan with up to 3% employer matching.
• Monthly technology allowance.
• Employee well-being perks, activities, and recognition programs.