Role Description 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: - 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: - Turn research workflows into scalable, repeatable, high-throughput pipelines (Airflow, Dagster, Prefect, Nextflow) - Ensure containerized, reproducible execution that serves 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/Prometheus/Grafana - Drive GPU and accelerated-compute efficiency: - Scheduling, quota and utilization management, and driver/CUDA image hygiene - Partner 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 - Set MLOps standards and best practices while staying hands-on with architecture and delivery Qualifications - 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 Requirements - Ability to travel – up to 10% - 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) Benefits - Comprehensive package of benefits including paid time off, medical/dental/vision insurance, and 401(k) to eligible employees - Remote work arrangement available for eligible roles - Annual salary range for this role is $180,000 - $220,000 - This job is also eligible for bonus/incentive pay

• Own the end-to-end ML lifecycle and deployment • Partner with bioinformatics and computational biology teams • Implement CI/CD, continuous training, and observability for ML • Drive GPU and accelerated-compute efficiency • Build self-service ML tooling and provide technical leadership

• Design, build, and set the ML platform surface used by our data science team • Establish and evangelize ML platform standards, patterns, and reusable components • Mentor data scientists and engineers on production ML practices • Own model serving infrastructure on AWS SageMaker • Build and maintain the model registry, version control, and promotion workflows • Stand up and operate retraining pipelines using MLflow, Weights & Biases, and orchestration tools • Build monitoring and alerting for production models • Write clean, modular Python and infrastructure-as-code (Terraform) • Partner closely with data scientists to make their workflow faster and more reliable • Collaborate with Data / Platform Engineering and AI Engineering counterparts

• The Machine Learning Engineer will perform their job duties to a high standard, working both independently and collaboratively. • The core responsibility is to assist in the development, implementation, testing, and evaluation of an AI-based Real-Time Mesoscale Analysis (AI-RTMA) system in support of NOAA’s National Blend of Models (NBM). • The AI-RTMA system will generate high spatial and temporal resolution analyses of meteorological variables to reduce biases in the NBM fields. • Conduct a comprehensive review of state-of-the-art AI-based data assimilation and end-to-end weather forecasting methodologies, systems, and frameworks. • Communicate findings with EMC scientists and external partners to inform the development of a scientifically robust and efficient AI-RTMA approach. • Collaborate with NOAA’s NBM team and key stakeholders to define product requirements for AI-RTMA, including domain configuration, grid structure, output variables, spatial and temporal resolution, and data formats suitable for operational evaluation and transition. • Design, implement, and maintain robust data pipelines to support AI-RTMA training, validation, testing, and evaluation. This includes collecting, formatting, quality-controlling, and integrating diverse observational datasets (e.g., conventional observations, satellite, radar, and other sources), as well as preparing model inputs, targets, metadata, and training/validation splits. • Develop, train, rigorously test, and deploy a fully functional AI-RTMA system based on selected AI frameworks or architectures. • Implement cross-validation and other evaluation methodologies to quantify model performance and reliability during inference.