Sr Machine Learning Engineer

<h2><b>Career Category</b></h2>Engineering<h2></h2><h2><b>Job Description</b></h2><h1>Position Overview</h1><p>The GCF5 Sr Machine Learning Engineer is the senior technical leader for the Agentic &amp; ML Platform pillar. They define and socialize platform standards and patterns, lead multi-team delivery, mentor GCF4 engineers, and translate scientific needs into scalable ML/agentic platform designs. They own pillar-level adoption, reliability, and SLA/SLO outcomes, and influence cross-team engineering quality.</p><p></p><p>This role reports to the GCF7 leader and partners closely with peer GCF5 domain leads across SCIP to ensure cohesive, scalable platform evolution.</p><p></p><h1>Core Responsibilities</h1><ul><li>Own the ML and agentic platform technical roadmap within SCIP.</li><li>Design and operationalize reusable ML/agentic infrastructure components enabling repeatable deployment.</li><li>Define evaluation harnesses and model release gates.</li><li>Establish monitoring, rollback, and observability practices for production ML systems.</li><li>Implement guardrails and operational controls for safe agentic workflows.</li><li>Define reproducibility standards and artifact versioning practices.</li><li>Lead architecture reviews for ML platform evolution.</li><li>Mentor engineers and elevate ML engineering rigor.</li><li>Partner with research stakeholders to translate AI use cases into scalable platform capabilities.</li></ul><h1>Core Competencies</h1><ul><li>Deep expertise in the assigned pillar (Agentic &amp; ML Platform) (Agentic‑ML) with evidence of standard‑setting and reuse.</li><li>Systems design at scale (ML); performance, security, and observability fundamentals.</li><li>Product/engineering thinking: road mapping, prioritization, and outcome‑oriented delivery.</li><li>Stakeholder influence across science, engineering, and governance forums; crisp written/verbal communication.</li></ul><h1>Core Success Measures</h1><ul><li>Adoption rate of standardized ML platform components.</li><li>Evaluation coverage across supported ML use cases.</li><li>Reduction in model regressions and production ML incidents.</li><li>Time-to-deploy new ML use cases.</li><li>Reproducibility rate of experiments and deployments.</li><li>Reduction in safe-use escalations.</li></ul><h1>Key Relationships</h1><ul><li>Collaborates with GCF6 Group Lead and cross‑functional leaders (R&amp;D/PD/Dev).</li><li>Mentors and develops GCF4 Data and Software Engineers, partners with platform, data, ML, and research teams.</li><li>Interfaces with governance (architecture, security, compliance) and vendor/partner teams.</li></ul><h1>Decision Authority</h1><ul><li>Approve designs within the pillar; define and waive standards/patterns with rationale.</li><li>Recommend buy‑vs‑build; commit pillar resources to meet SLAs/SLOs; escalate risks.</li><li>Prioritize pillar backlog and roadmap in alignment with strategy and OKRs.</li></ul><h1>Qualifications</h1><p>Basic Qualifications:</p><ul><li>BS&#43;8 / MS&#43;6 / PhD in CS/Engineering/Data disciplines.</li><li>Demonstrated production delivery experience in ML/agentic platforms at scale.</li><li>Demonstrated literacy in a relevant scientific domain (e.g., biology, chemistry, therapeutic discovery).</li></ul><p>Preferred Qualifications:</p><ul><li>Depth in the assigned pillar (Agentic &amp; ML Platform).</li><li>Kubernetes and continuous integration/continuous delivery (CI/CD) at scale; observability, performance tuning, and security-by-design.</li><li>Evidence of standard‑setting and cross‑team influence; mentoring experience.</li></ul><p style="text-align:inherit"></p><p style="text-align:inherit"></p><p style="text-align:inherit"></p>.