Note: The job is a remote job and is open to candidates in USA. Danaher is a leading science and technology company committed to innovation in life sciences, diagnostics, and biotechnology. The Staff Engineer - ML Operations will own significant parts of the machine learning lifecycle, driving the deployment of AI-powered research and collaborating with bioinformatics teams to ensure efficient operations of large-scale ML experiments.
Responsibilities
- Own the end-to-end ML lifecycle and deployment — experiment tracking, model registry, versioning, lineage, and reproducibility (e.g., MLflow, Weights & Biases, Kubeflow); design and operate model serving for batch and low-latency online inference with autoscaling, GPU efficiency, and performance optimization (batching, quantization, caching) — so every model in production is traceable, auditable, and performant
- Partner with bioinformatics and computational biology teams to productionize large-scale protein design and structure-prediction experiments turning research workflows into scalable, repeatable, high-throughput pipelines (Airflow, Dagster, Prefect, Nextflow) with containerized, reproducible execution that serve many concurrent researchers without contention
- Implement CI/CD, continuous training, and observability for ML — automate the path from model code to validated production through testing, evaluation gates, and safe deployment patterns (blue/green, canary); monitor model performance, data/prediction drift, latency, and cost; implement automated retraining and alerting instrumented via OpenTelemetry/Prometheus/Grafana so issues are caught before they reach users
- Drive GPU and accelerated-compute efficiency — scheduling, quota and utilization management, and driver/CUDA image hygiene — partnering with the platform team to maximize value from contended, high-demand compute
- Build self-service ML tooling and provide technical leadership — develop golden paths that let data scientists and researchers train, track, serve, and monitor models without deep infrastructure expertise, treating ML enablement as a product; set MLOps standards and best practices while staying hands-on with architecture and delivery
Skills
- Degree in Computer Science, Engineering, Computational Biology, or a related technical field, or equivalent practical experience
- 5+ years of software, ML, or infrastructure engineering experience, including hands-on MLOps and a track record of taking ML models into production at scale
- Strong experience with ML lifecycle tooling — experiment tracking, observability/monitoring, model registry, versioning, lineage, and reproducibility (e.g., MLflow, Kubeflow, Weights & Biases)
- Strong experience with containerization and orchestration (Docker, Kubernetes) — including scaling GPU workloads — and with a major cloud platform (Azure preferred) and its ML services (e.g., Azure ML), using IaC and CI/CD for ML
- Proficiency in Python (and familiarity with Bash) for automation, tooling, and pipeline development
- Experience supporting computational biology or bioinformatics pipelines, including protein structure prediction or design tools (e.g., AlphaFold, Boltz/BoltzGen, Chai, RFdiffusion, ProteinMPNN) or molecular simulation
- Experience operating ML in a regulated environment (GxP, SOX, or HIPAA), including model traceability and audit evidence
- Familiarity with LLMOps / agentic frameworks and evaluation tooling (e.g., Langfuse, OpenTelemetry for LLMs)
Benefits
- Bonus/incentive pay
- Paid time off
- Medical/dental/vision insurance
- 401(k) to eligible employees
- Remote work arrangement in which you can work remotely from your home
Company Overview
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