Computational Biology MLOps Engineer

Onebridge; United States; Hybrid; USD 140,000 - 210,000; Full time

Job summary

Marlabs is seeking a Computational Biology MLOps Engineer to build and scale ML infrastructure for in silico protein design, bridging cutting edge AI research and production systems.

You will collaborate with computational scientists and platform engineers to accelerate research velocity, implement CI/CD pipelines, orchestrate compute with Kubernetes and SLURM, and manage data and ML workloads across distributed environments.

Qualifications

• 5+ years in software/ML engineering or related field.
• 3+ years in production-grade MLOps and ML infrastructure.
• CI/CD pipelines and GitHub Actions experience.
• Kubernetes experience for ML workloads.
• SLURM or HPC environment familiarity; distributed training.
• Strong Python; experience with PyTorch, TensorFlow, or JAX.
• AWS/GCP/Azure cloud experience; data pipelines with ML readiness.

Responsibilities

• Build and maintain ML infrastructure, including CI/CD pipelines for model training, evaluation, and deployment.
• Orchestrate compute across Kubernetes clusters and HPC environments to optimize large-scale training.
• Develop scalable data pipelines delivering ML-ready datasets from biological sources.
• Create tools to accelerate protein language and diffusion models.
• Architect systems for distributed environments handling multimodal data.
• Implement monitoring, logging, and cost-conscious production ML systems.

Skills

5+ years software/ML engineering

MLOps

CI/CD

GitHub Actions

Kubernetes

Python

SLURM

Distributed training

AWS

GCP

Azure

Protein models

Job description

Marlabs, a global AI and Digital Solutions Consulting firm, delivers intelligent solutions across AI, data, analytics, and product engineering. Since 2000, we have partnered with some of the largest healthcare, life sciences, financial services, and government organizations worldwide. As we continue to expand our global footprint, we have an exciting opportunity for a highly skilled Computational Biology MLOps Engineer to join our innovative and dynamic team.

This role requires onsite work three (3) days per week in Indianapolis, IN or San Diego, CA.

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.