Computational Biology MLOps Engineer | About You As a Computational Biology MLOps Engineer, you are responsible for building and scaling the ML infrastructure that supports next generation in silico protein design and engineering. You bridge cutting edge AI research and production systems at the intersection of machine learning, computational biology, and high performance computing. You thrive in cross functional environments and partner closely with computational scientists and platform engineers to accelerate research velocity. You bring strong software, DevOps, and data engineering fundamentals with hands on experience across CI/CD, orchestration, and distributed training. Experience working with scientific or multimodal data and interest in protein language and generative models is a plus.
Computational Biology MLOps Engineer | Day-to-Day - Build and maintain ML infrastructure, including CI/CD pipelines (GitHub Actions) for model training, evaluation, and deployment.
- Orchestrate compute across Kubernetes clusters and SLURM and HPC environments to optimize utilization for large scale training.
- Develop robust and scalable data pipelines that deliver ML ready datasets from biological sources such as PDB and mmCIF files, sequence databases, and assay readouts.
- Create tools and frameworks that enable rapid iteration on protein language models, diffusion models, and other generative approaches.
- Architect systems that scale across distributed environments and support multimodal datasets for large foundational models.
- Implement monitoring, logging, and alerting to ensure reliability, performance, and cost efficiency of production ML systems.
Computational Biology MLOps Engineer | Skills & Experience - 5+ years of overall industry experience in software engineering, DevOps, data engineering, or ML engineering roles, including 3+ years of focused MLOps experience building and maintaining production grade ML infrastructure.
- Proven CI/CD expertise with GitHub Actions and strong DevOps practices including infrastructure as code, version control, and collaborative workflows.
- Hands on Kubernetes experience in deploying and managing containerized ML workloads with familiarity using container registries.
- Proficiency with SLURM or similar job schedulers in HPC environments and experience with distributed training optimization including mixed precision and checkpointing.
- Strong Python skills and experience with major ML frameworks including PyTorch, TensorFlow, or JAX.
- Experience building ETL processes and scalable data and feature pipelines and experience with cloud platforms such as AWS, GCP, or Azure.
- Preferred experience with scientific data, protein structure formats such as PDB and mmCIF, protein AI models including ESM, and agentic systems such as MCP, LangGraph, and LangChain.
