Staff Engineer - ML Operations

Role Description

The Staff Engineer - ML Operations is responsible for owning significant parts of the machine learning lifecycle that powers Danaher's next-generation AI-driven research. You will be taking models from experimentation to reliable, scalable production. In this highly impactful role, you will:

• 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).
• Partner with bioinformatics and computational biology teams to productionize large-scale protein design and structure-prediction experiments:
  • Turn research workflows into scalable, repeatable, high-throughput pipelines (Airflow, Dagster, Prefect, Nextflow).
  • Ensure containerized, reproducible execution that serves 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.
• Drive GPU and accelerated-compute efficiency:
  • Scheduling, quota and utilization management, and driver/CUDA image hygiene.
  • Partner 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.
  • Set MLOps standards and best practices while staying hands-on with architecture and delivery.

Qualifications

• 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.

Requirements

• Ability to travel – up to 10%.
• 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

• Comprehensive package of benefits including paid time off, medical/dental/vision insurance, and 401(k) to eligible employees.
• Annual salary range for this role is $180,000 - $220,000.
• This job is also eligible for bonus/incentive pay.