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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 design and operate the pipelines, serving infrastructure, and observability that let our scientists run large-scale ML experiments with speed, reproducibility, and rigor. You will work hand in hand with bioinformatics and computational biology teams running cutting-edge protein design and structure prediction workloads, ensuring those workloads run efficiently at scale on shared accelerated compute.
This position reports to the Senior Director, Data and AI Platform and is part of the Chief Scientific Officer (CSO) Office and will be fully remote.
In this role, you will have the opportunity to: - 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/PrometheGrafana 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.
The essential requirements of the job include:
- 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.
Preferred / bonus qualifications:
Travel, Motor Vehicle Record & Physical/Environment Requirements: - Ability to travel - up to 10%
It would be a plus if you also possess previous experience in: - 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).
Danaher offers a broad array of comprehensive, competitive benefit programs that add value to our lives. Whether it's a health care program or paid time off, our programs contribute to life beyond the job. Check out our benefits at Danaher Benefits Info.
At Danaher, we believe in designing a better, more sustainable workforce. We recognize the benefits of flexible, remote working arrangements for eligible roles and are committed to providing enriching careers, no matter the work arrangement. This position is eligible for a remote work arrangement in which you can work remotely from your home. Additional information about this remote work arrangement will be provided by your interview team. Explore the flexibility and challenge that working for Danaher can provide.
The annual salary range for this role is $180,000 - $220,000. This is the range that we in good faith believe is the range of possible compensation for this role at the time of this posting. This range may be modified in the future.
This job is also eligible for bonincentive pay. #LI-Remote
We offer comprehensive package of benefits including paid time off, medical/dental/vision insurance and 401(k) to eligible employees.
Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole discretion unless and until paid and may be modified at the Company's sole discretion, consistent with the law.
Join our winning team today. Together, we'll accelerate the real-life impact of tomorrow's science and technology. We partner with customers across the globe to help them solve their most complex challenges, architecting solutions that bring the power of science to life.
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