Machine Learning Engineer, AI

Biohub

Machine Learning Engineer developing systems connecting AI and biological research at Biohub. Collaborating with engineers and researchers to solve infrastructure challenges for scientific discovery.

Posted 5/1/2026full-timeRedwood City • California • 🇺🇸 United StatesMid-LevelSenior💰 $150,000 - $350,000 per yearWebsite

Tech Stack

Tools & technologies

ApacheDockerKubernetesPyTorchRaySpark

About the role

Key responsibilities & impact

Work with high-dimensional scientific data formats and contribute to backend compatibility, format evaluation, and I/O performance benchmarking at petabyte scale.
Define and shape the engineering patterns your team and collaborating researchers will build on for years; the abstractions you write today become the foundation others depend on at scale.
Work at the intersection of AI systems and biological discovery, where the infrastructure problems you solve directly determine what science becomes possible.
Deploy models to production and manage artifact tracking across models and datasets.
Design and optimize GPU-native data loading pipelines for large-scale multi-dimensional tensor workloads, including profiling and resolving hardware utilization bottlenecks across multi-backend systems.
Work on simplification and improvement of codebase abstractions to accelerate research momentum.
Build and maintain primitives for pre-training infrastructure that ensure the reliability and continuity of large-scale training runs.
Help cultivate best practices in MLOps, and think about the full ML lifecycle, including data, fine-tuning, deployment, reliability and monitoring.
Possesses the ability to execute complex modifications to the research pipeline, such as fast data loading and distributed training.
Handle DevOps responsibilities, focused on making all engineers and researchers more productive. This includes tasks like cluster monitoring, unit testing and integration testing of research codebase, and continuous integration.
Collaborate with partner researchers and engineers to deploy our technology within external infrastructure.

Requirements

What you’ll need

Hands-on experience with PyTorch, including custom training loops, distributed training, or low-level performance work.
Familiarity with GPU-native data I/O tools and large-scale tensor formats (e.g. Zarr, HDF5, TensorStore, or similar).
Experience with distributed computing frameworks such as Apache Spark, Dask, or Ray.
Familiarity with containerization and orchestration tools such as Docker and Kubernetes.
Experience building or working with AI agent frameworks is a plus.
A track record of building systems that other engineers and researchers depend on. Not just running experiments, but shipping infrastructure that scales.

Benefits

Comp & perks

Provides a generous employer match on employee 401(k) contributions to support planning for the future.
Paid time off to volunteer at an organization of your choice.
Funding for select family-forming benefits.
Relocation support for employees who need assistance moving

ATS Keywords

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Hard Skills & Tools

PyTorchGPU-native data I/Olarge-scale tensor formatsZarrHDF5TensorStoredistributed computing frameworksApache SparkDaskRay

Soft Skills

collaborationproblem-solvingbest practices in MLOpsexecution of complex modificationsproductivity enhancement