Biohub is the first large-scale initiative bringing frontier AI models, massive compute, and frontier experimental capabilities under one roof. We're building a general-purpose system to accelerate scientific discovery, integrating frontier AI models, biological foundation models, and lab capabilities, with the ultimate goal of curing disease. Our technology powers scientists around the world, translating AI capabilities into tools that accelerate research everywhere.
The Team
Biohub is the first large-scale initiative bringing frontier AI models, massive compute, and frontier experimental capabilities under one roof. We're building a general-purpose system to accelerate scientific discovery, integrating frontier AI models, biological foundation models, and lab capabilities, with the ultimate goal of curing disease. Our technology powers scientists around the world, translating AI capabilities into tools that accelerate research everywhere.
Biohub operates one of the largest AI compute clusters dedicated to biology, spanning three frontier research institutes with some of the world's leading biologists. We're not a startup trying to find product-market fit, and we're not a pharma company optimizing a pipeline. We're building frontier AI for fundamental science, as open science, at a scale no one else is doing. This is a unique moment for scientific acceleration. The problems are among the hardest and most impactful problems you can choose to work on, and we move at a pace that meets this moment.
Our research spans:
- Frontier molecular modeling, from protein language models (e.g., ESM) to structure prediction (e.g., ESMFold) and beyond.
- Scaled biological foundation models trained on some of the largest GPU clusters dedicated to science
- Imaging foundation models trained across the world's largest microscopy datasets
- Reasoning and agentic systems that connect frontier LLMs with biological foundation models
- Mechanistic interpretability of biological foundation models: extracting new biological knowledge directly from model weights
- Scientific data at unprecedented scale: AI systems to collect, curate, and learn from some of the richest biological datasets ever assembled
The Opportunity
As an ML Engineer, you'll join some of the strongest infrastructure engineers in AI, building the systems that connect everything together. The infrastructure problems you solve directly determine what science becomes possible.
What You'll Do
Our team has a wide range of responsibilities:
- Work with researchers to train, fine-tune, and evaluate biological foundation models and diagnose training problems.
- Help evaluate and curate training and validation datasets at Petabytes scale.
- Design and create libraries and abstractions that researchers and engineers build on.
- Optimize GPU-native data loading for large tensor workloads and resolve hardware utilization bottlenecks across backends.
- Build and maintain reliable distributed pre and post training systems.
- Support production deployment, with tracking of model and dataset artifacts throughout the ML lifecycle.
- Improve model and cluster monitoring, unit and integration testing, and continuous integration.
- Work with partner researchers and engineers to deploy models and supporting infrastructure in external environments.
What You'll Bring
Significant experience in at least one of the following areas:
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- 5+ years building ML systems or distributed infrastructure at scale.
- Strong Python, PyTorch, and CUDA skills, including custom training loops, with a solid understanding of model architectures, optimization, and evaluation.
- Experience with large scale distributed systems, multi-GPU training, checkpointing, and failure recovery.
- Ability to diagnose performance problems across GPUs, memory, networking, storage, and data loading.
- Experience building large-scale data pipelines and shared research tools; familiarity with scientific formats such as Zarr or HDF5.
- Experience deploying and monitoring models, tracking model and dataset versions, and maintaining automated tests and continuous integration.
- Familiarity with containers and orchestration tools such as Slurm, Docker, and Kubernetes.
Compensation
The future anticipated Redwood City, CA, and New York City, NY base pay range for a role in this field is for Senior Machine Learning Engineer $214,000 -$268,000 and for Staff Machine Learning Engineer $241,000 - $301,000 annually. Final compensation is based on the level at which you are hired. Actual placement in range is based on job-related skills and experience, as evaluated throughout the interview process.
Benefits for the Whole You
We’re thankful to have an incredible team behind our work. To honor their commitment, we offer a wide range of benefits to support the people who make all we do possible.
- 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
Please note that applying to this opportunity does not guarantee that we will be in touch with you regarding our opportunities. Our recruiting team will contact you if your experience aligns with the skills we seek for future open positions. We will keep your interest on file, contact you as opportunities arise, and send you information about the exciting work we are doing at Biohub. You can opt out at any time!
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