Staff Engineer - ML Operations - Remote

Position: Staff Engineer - ML Operations - USA Remote
Bring more to life.  

At Danaher, our work saves lives. And each of us plays a part. Fueled by our culture of continuous improvement, we turn ideas into impact - innovating at the speed of life.

Our 60,000+ associates work across the globe at more than 15 unique businesses within life sciences, diagnostics, and biotechnology .

Are you ready to accelerate your potential and make a real difference? At Danaher, you can build an incredible career at a leading science and technology company, where   we're   committed to hiring and developing from within.
You'll   thrive in a culture of belonging where you and your unique viewpoint matter.

Learn about the    Danaher Business Syste  m    which makes everything possible.

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 wi ll 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 b iology teams   running   cutting-edge   protein design and structure prediction workloads, ensuring those workloads run efficiently at scale o n share d accelerated   compute .

This   position reports to th e   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 product ionize 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   Open Telemetry /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.

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   1 0%   

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., Alpha Fold, Boltz/ Boltz Gen , Chai,   RF diffusion ,   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 ,   Open…