Staff ML Engineer (ML/AI)

About the role

This position centers on shaping how artificial intelligence creates measurable impact within a healthcare context. You will translate ambiguous clinical and business needs into concrete technical strategies for machine learning and generative AI. The role requires deep ownership of the end-to-end lifecycle, from initial exploration to robust deployment in production. You will define the long-term vision for ML capabilities and ensure they align with product objectives and clinical safety. Close collaboration with clinical leadership and product teams is essential to validate assumptions and drive alignment. You will establish evaluation and guardrail frameworks that balance innovation with reliability, security, and precision. Success will be measured by the scalability and real-world effectiveness of the systems you build.

Key facts

What you'll do

Architect and execute a long-term machine learning and generative AI roadmap in partnership with product and clinical leadership.

Implement enterprise evaluation and guardrail frameworks to ensure high reliability, security, and clinical precision.

Write production-grade code to deliver scalable and maintainable AI infrastructure solutions.

Deploy complex ML/AI solutions into mission-critical production environments requiring 8+ years of proven technical leadership.

Master system and software engineering, including Python, RESTful API design, Protobuf, and microservices architecture.

Operate production AI/ML infrastructure at scale, including Docker, Kubernetes, container orchestration, and real-time inference services.

Design and deploy modern generative AI architectures, such as RAG pipelines, LLM fine-tuning, vector database management, and evaluation/guardrail frameworks.

Manage data layer systems, including relational databases, low-latency key-value stores, distributed queueing systems such as Kafka or Celery, and data pipeline orchestration.

Architect cloud-native solutions primarily on AWS or equivalent cloud providers to meet performance and reliability goals.

Distill highly ambiguous technical problems into clear strategic priorities and influence cross-functional leadership.

Demonstrate polyglot engineering by writing high-performance production code in Java or Kotlin when required.

Architect AI/ML systems in highly regulated environments, adhering to HIPAA compliance, SOC2, and handling PHI/PII with care.

Build internal developer platforms or ML tooling used by dozens of data scientists and engineers to improve team efficiency.

Requirements

Eligibility for employment in the United States is mandatory for this role.

Bring 8+ years of hands-on experience deploying complex ML/AI solutions into production environments.

Demonstrate mastery of system and software engineering, with fluency in Python, RESTful API design, Protobuf, and microservices architecture.

Operate production AI/ML infrastructure at scale, including containerization with Docker, orchestration with Kubernetes, and real-time inference services.

Design and deploy modern generative AI architectures, including RAG pipelines, LLM fine-tuning, vector database management, and evaluation/guardrail frameworks.

Manage data layer systems such as relational databases, low-latency key-value stores, distributed queueing systems like Kafka or Celery, and data pipeline orchestration tools.

Architect cloud-native solutions predominantly on AWS or comparable cloud platforms, ensuring high availability and security.

Distill ambiguous problems into clear strategic priorities and exert influence across cross-functional leadership teams.

Nice to have

Write high-performance production code in Java or Kotlin to support polyglot engineering standards.

Architect AI/ML systems in highly regulated environments, adhering to HIPAA compliance, SOC2 requirements, and strict handling of PHI/PII.

Develop internal developer platforms or ML tooling that is used by dozens of data scientists and engineers to streamline workflows.

Practical notes

This role requires United States eligibility for employment.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Machine learning roles of this level combine software engineering rigor with data science depth.

Generative AI platforms rely on MLOps practices, cloud infrastructure, and evaluation frameworks to deliver reliable outcomes.

Healthcare AI systems often operate under strict compliance regimes and require close collaboration with clinical stakeholders.

Platform thinking and cross-functional communication are critical for scaling AI capabilities across large organizations.

Proficiency with container orchestration and modern inference serving is essential for production AI workloads.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.