Staff Engineer - ML Operations - USA Remote

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 experimentsturning 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 viaOpenTelemetry/Prometheus/Grafana so issues are caught before they reach users.

Drive GPU and accelerated-compute efficiency — scheduling, quota andutilizationmanagement, and driver/CUDA image hygiene — partnering with the platform team to maximize value from contended, high-demandcompute.

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; setMLOpsstandards and best practices while staying hands-on with architecture and delivery.

Requirements

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-onMLOpsanda track recordof 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), usingIaCand CI/CD for ML.

Proficiencyin Python (and familiarity with Bash) for automation, tooling, and pipeline development.

Preferred / bonus qualifications:

Travel, Motor Vehicle Record & Physical/Environment Requirements:

• Ability to travel – up to10%

It would be a plus if you also possesspreviousexperience in:

• Experience supportingcomputational 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 aregulated environment(GxP, SOX, or HIPAA), including model traceability and audit evidence.
• Familiarity withLLMOps / agentic frameworksand evaluation tooling (e.g.,Langfuse,OpenTelemetryfor LLMs).

About Us

Danaher is a leading global life sciences, biotechnology, and diagnostics innovator, helping to solve many of the world’s most important health challenges, ultimately improving quality of life for billions of people today, while setting the foundation for a healthier, more sustainable tomorrow. The Danaher ecosystem is made up of more than 15 businesses united by a shared commitment to innovate for tangible impact.