Careers

Lead Machine Learning Engineer

Location: United States

Employment Type: Full-Time

Role Overview

The Lead Machine Learning Engineer is responsible for the production implementation and integration of core machine learning algorithms used in a regulated diagnostic platform. This role translates validated scientific and bioinformatics models into scalable, secure, and auditable machine learning systems suitable for clinical and diagnostic environments. The position works closely with bioinformatics, engineering, and infrastructure teams to ensure models are reliable, reproducible, and production-ready. This role does not originate scientific hypotheses and focuses on engineering execution and system integrity.

Key Responsibilities

Implement and productionize machine learning models defined by scientific and bioinformatics leadership.
Design and maintain model integration, versioning, scoring, and inference pipelines.
Ensure model reproducibility, performance, auditability, and traceability.
Collaborate with DevOps and platform engineering teams on deployment, monitoring, and system reliability.
Support validation, testing, and documentation required for regulated environments.
Participate in code reviews, system design discussions, and performance optimization.
Maintain clear technical documentation for machine learning systems and workflows.

Required Qualifications

Strong experience building and deploying machine learning systems in production environments.
Proficiency with Python and common machine learning frameworks.
Experience designing scalable model pipelines, APIs, and inference services.
Familiarity with version control, CI / CD workflows, and cloud-based infrastructure.
Understanding of model validation, testing, and performance monitoring.
Ability to collaborate effectively with scientific, engineering, and infrastructure teams.
Strong attention to detail and commitment to system reliability and auditability.

Preferred Qualifications

Experience working in regulated or compliance-driven environments.
Familiarity with healthcare, diagnostics, or life sciences data.
Experience with containerization, orchestration, and cloud deployment platforms.
Background working alongside bioinformatics or data science teams.