Join the Squad | Now Hiring a MLOps Engineer

Marlabs • San Diego, CA

21h • Hybrid

About The Position

Marlabs is hiring an MLOps Engineer to build and scale machine learning infrastructure for next-generation in silico protein design and engineering. The role bridges AI research and production systems at the intersection of machine learning, computational biology, and high-performance computing, working closely with computational scientists and platform engineers to accelerate research.

The position requires onsite work three days per week in Indianapolis, Indiana, or San Diego, California. The engineer will support model training, evaluation, and deployment; build data pipelines from biological sources; enable experimentation with protein language and generative models; and ensure production ML systems are reliable, performant, and cost-efficient.

Requirements

• At least 5 years of overall industry experience in software engineering, DevOps, data engineering, or ML engineering, including at least 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 deploying and managing containerized ML workloads, with familiarity using container registries.
• Proficiency with SLURM or similar job schedulers in HPC environments.
• Experience optimizing distributed training, including mixed precision and checkpointing.
• Strong Python skills and experience with major ML frameworks such as PyTorch, TensorFlow, or JAX.
• Experience building ETL processes and scalable data and feature pipelines.
• Experience with cloud platforms such as AWS, GCP, or Azure.

Nice To Haves

• Experience with scientific data and protein structure formats such as PDB and mmCIF.
• Experience with protein AI models, including ESM.
• Experience with agentic systems such as MCP, LangGraph, and LangChain.
• Interest in protein language and generative models.

Responsibilities

• Build and maintain ML infrastructure, including GitHub Actions CI/CD pipelines for model training, evaluation, and deployment.
• Orchestrate compute across Kubernetes clusters, SLURM, and HPC environments to optimize utilization for large-scale training.
• Develop robust, scalable data pipelines that deliver ML-ready datasets from biological sources, including 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 the reliability, performance, and cost efficiency of production ML systems.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed