Sr Machine Learning Engineer
About this opportunity
Career Category
Engineering
Job Description
Position Overview
The GCF5 Sr Machine Learning Engineer is the senior technical leader for the Agentic & 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.
This role reports to the GCF7 leader and partners closely with peer GCF5 domain leads across SCIP to ensure cohesive, scalable platform evolution.
Core Responsibilities
Own the ML and agentic platform technical roadmap within SCIP.
Design and operationalize reusable ML/agentic infrastructure components enabling repeatable deployment.
Define evaluation harnesses and model release gates.
Establish monitoring, rollback, and observability practices for production ML systems.
Implement guardrails and operational controls for safe agentic workflows.
Define reproducibility standards and artifact versioning practices.
Lead architecture reviews for ML platform evolution.
Mentor engineers and elevate ML engineering rigor.
Partner with research stakeholders to translate AI use cases into scalable platform capabilities.
Core Competencies
Deep expertise in the assigned pillar (Agentic & ML Platform) (Agentic‑ML) with evidence of standard‑setting and reuse.
Systems design at scale (ML); performance, security, and observability fundamentals.
Product/engineering thinking: road mapping, prioritization, and outcome‑oriented delivery.
Stakeholder influence across science, engineering, and governance forums; crisp written/verbal communication.
Core Success Measures
Adoption rate of standardized ML platform components.
Evaluation coverage across supported ML use cases.
Reduction in model regressions and production ML incidents.
Time-to-deploy new ML use cases.
Reproducibility rate of experiments and deployments.
Reduction in safe-use escalations.
Key Relationships
Collaborates with GCF6 Group Lead and cross‑functional leaders (R&D/PD/Dev).
Mentors and develops GCF4 Data and Software Engineers, partners with platform, data, ML, and research teams.
Interfaces with governance (architecture, security, compliance) and vendor/partner teams.
Decision Authority
Approve designs within the pillar; define and waive standards/patterns with rationale.
Recommend buy‑vs‑build; commit pillar resources to meet SLAs/SLOs; escalate risks.
Prioritize pillar backlog and roadmap in alignment with strategy and OKRs.
Qualifications
Basic Qualifications:
BS+8 / MS+6 / PhD in CS/Engineering/Data disciplines.
Demonstrated production delivery experience in ML/agentic platforms at scale.
Demonstrated literacy in a relevant scientific domain (e.g., biology, chemistry, therapeutic discovery).
Preferred Qualifications:
Depth in the assigned pillar (Agentic & ML Platform).
Kubernetes and continuous integration/continuous delivery (CI/CD) at scale; observability, performance tuning, and security-by-design.
Evidence of standard‑setting and cross‑team influence; mentoring experience.
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Job details
How this role compares
Computed from every other active Information Technology role in our database, not just this employer's listings.
We currently track 378 comparable Senior Information Technology roles across 34 biopharma companies.
Salary context
45 of 378 peers report a salary range (USD, annualized)
Peers share this role's job function and a matching or adjacent seniority level -- not necessarily the same therapeutic area or country.
Where these roles are based
Top locations among the 378 comparable roles
+ 15 more countries
Seniority mix
378 of 378 peers have a known seniority level
Therapeutic area mix
0 of 378 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden
No peers with a known therapeutic area yet.
Similar opportunities
The closest matches from our peer group, ranked by how similar they are, not how well you'd qualify for them -- treat this as market context, not a guaranteed shortlist; a weak match is labeled as one below.
How we calculate "similar"
No black box, no LLM guesswork: a deterministic score built from four normalized attributes. Here's this role's own peer group at different match levels, so you can see the mechanism, not just the result.
Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.
0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.