Machine Learning Engineer, AI

Biohub

. Work with researchers to train, fine-tune, and evaluate biological foundation models and diagnose training problems .

Posted 11/11/2026full-timeUnited StatesMid-LevelSenior💰 $214,000 - $301,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Expertise in building and optimizing machine learning systems and distributed infrastructure at scale, with a strong focus on Python, PyTorch, and CUDA. Proficient in managing large-scale data pipelines, model deployment, and monitoring throughout the ML lifecycle.

Highest-signal resume keywords

Machine Learning Systems DevelopmentPython ProgrammingPyTorch FrameworkCUDA OptimizationDistributed Infrastructure Management

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Machine LearningModel EvaluationData Pipeline DevelopmentPerformance DiagnosisCheckpointingFailure RecoveryMulti-GPU TrainingUnit TestingIntegration TestingContinuous Integration

Tools & Technologies

DockerKubernetesSlurmZarrHDF5

Industry Keywords

Biological Foundation ModelsGPU UtilizationModel MonitoringArtifact TrackingDistributed Systems

Tech Stack

Tools & technologies

Distributed SystemsDockerKubernetesPythonPyTorch

About the role

Key responsibilities & impact

Work with researchers to train, fine-tune, and evaluate biological foundation models and diagnose training problems
Evaluate and curate training and validation datasets at Petabytes scale
Design and create libraries and abstractions for researchers and engineers
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 and track 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

Requirements

What you’ll need

5+ years building ML systems or distributed infrastructure at scale
Strong Python, PyTorch, and CUDA skills, including custom training loops
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

Benefits

Comp & perks

Generous employer match on employee 401(k) contributions
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