Bio-ML Ops Engineer: Scale Protein AI Pipelines

MarLabs; Indianapolis (IN); Hybrid; USD 140,000 - 180,000; Full time

Job summary

Marlabs is seeking a Computational Biology MLOps Engineer to build and scale ML infrastructure for next-gen in silico protein design. You will work at the intersection of AI research, biology, and high performance computing to accelerate discovery.

The role requires strong software, DevOps, and data engineering fundamentals, with hands-on experience in CI/CD, orchestration, and distributed training across Kubernetes and HPC environments.

Qualifications

• 5+ years in software/ML engineering roles including 3+ years in production-grade ML infrastructure.
• Proficient CI/CD with GitHub Actions and infrastructure as code practices.
• Hands-on Kubernetes in deploying and managing containerized ML workloads.
• Experience with SLURM or similar HPC schedulers and distributed training optimization.
• Strong Python skills and experience with ML frameworks (PyTorch, TensorFlow, JAX).

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 enabling rapid iteration on protein language models and diffusion methods.
• Architect systems that scale across distributed environments for multimodal datasets.
• Implement monitoring and alerting for reliable production ML systems.

Skills

MLOps experience

CI/CD (GitHub Actions)

Kubernetes

HPC / SLURM

Python

Distributed training

ML frameworks (PyTorch, TensorFlow, JX

Tools

GitHub Actions

Container registries

SLURM

Job description

Marlabs is seeking a Computational Biology MLOps Engineer to build and scale ML infrastructure for next-gen in silico protein design. You will work at the intersection of AI research, biology, and high performance computing to accelerate discovery.

The role requires strong software, DevOps, and data engineering fundamentals, with hands-on experience in CI/CD, orchestration, and distributed training across Kubernetes and HPC environments.