Computational Biology MLOps Engineer: AI Infra & HPC

Marlabs Inc.; San Diego (CA); Hybrid; USD 140,000 - 180,000; Full time

Job summary

Marlabs Inc. is seeking a Computational Biology MLOps Engineer to build and scale ML infrastructure for in silico protein design. You will bridge cutting-edge AI research with production systems, collaborating with computational scientists and platform engineers to accelerate research velocity.

Responsibilities include designing CI/CD pipelines, orchestrating HPC and Kubernetes, and developing data pipelines for ML-ready datasets from PDB/mmCIF and sequence databases.

Qualifications

• 5+ years of software/DevOps/ML engineering experience, with 3+ years in MLOps.
• Strong CI/CD and GitHub Actions; infrastructure as code practices.
• Kubernetes experience with distributed training and HPC environments.
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow, JAX).
• Experience building ETL/data pipelines and cloud platforms (AWS, GCP, Azure).
• Familiarity with protein data formats (PDB/mmCIF) and related ML models.

Responsibilities

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

Skills

CI/CD

Python

PyTorch/TensorFlow/JAX

Data pipelines

Distributed training

Cloud platforms (AWS/GCP/Azure)

Tools

GitHub Actions

Kubernetes

SLURM

HPC environments

Container registries

Job description

Marlabs Inc. is seeking a Computational Biology MLOps Engineer to build and scale ML infrastructure for in silico protein design. You will bridge cutting-edge AI research with production systems, collaborating with computational scientists and platform engineers to accelerate research velocity.

Responsibilities include designing CI/CD pipelines, orchestrating HPC and Kubernetes, and developing data pipelines for ML-ready datasets from PDB/mmCIF and sequence databases.