Staff Engineer – ML Operations

Danaher

Staff Engineer - ML Operations at Danaher responsible for the machine learning lifecycle. Designing scalable infrastructures for AI-driven research and collaborating with bioinformatics teams.

Posted 7/28/2026full-timeRemote • New York • 🇺🇸 United StatesLead💰 $180,000 - $220,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in managing the end-to-end ML lifecycle, including model deployment, observability, and tooling. Proficient in leveraging cloud platforms and containerization for efficient ML operations and performance optimization.

Highest-signal resume keywords

ML Lifecycle ToolingMLOps ExperienceContainerization and OrchestrationPython ProficiencyCloud Platform Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

MLflowKubeflowWeights & BiasesDockerKubernetesAzure MLCI/CDExperiment TrackingModel RegistryVersioning

Soft Skills

Technical Leadership

Tools & Technologies

GPU EfficiencyBatchingQuantizationCachingAutomationPipeline Development

Industry Keywords

Computational BiologyBioinformaticsProtein DesignStructure-PredictionInfrastructure Engineering

Tech Stack

Tools & technologies

AzureCloudDockerKubernetesPython

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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