Staff Engineer – ML Operations

🔥 1 hour ago

🗽 New York – Remote

🗽 New York – Remote

💵 $180k - $220k / year

⏰ Full Time

🔴 Lead

🤖 Machine Learning Engineer

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Danaher

WebsiteLinkedInAll Job Openings

10,000+ employees

🧬 Biotechnology

🏥 Healthcare

🔬 Science

Biotechnology • Healthcare • Science

Danaher is a leading global life sciences and diagnostics innovator that applies science and technology to improve human health. Its portfolio includes businesses in biotechnology, diagnostics, and life sciences that provide instruments, tools, software and services to accelerate research, develop and deliver therapies, and enable precise and rapid diagnostics. Danaher emphasizes continuous improvement through its Danaher Business System, pursues acquisitions and partnerships to expand capabilities, and focuses on scaling scientific innovation and delivering impact at global scale.

📋 Description

• 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; • Implement CI/CD, continuous training, and observability for ML; • Drive GPU and accelerated-compute efficiency; • Build self-service ML tooling and provide technical leadership.

🎯 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-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.

🏖️ Benefits

• Health insurance • 401(k) • Paid time off • Flexible working arrangements • Bonus/incentive pay