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# Machine Learning Engineer, AI

- **Company:** BIOHUB LLC
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $214,000 - $268,000
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Automation of Tests, Unit Testing, Nvidia CUDA, Continuous Integration, Extract Transform Load (ETL), Distributed Systems, High-Level Architecture, Python (Programming Language), Machine Learning, Software Tools, Software Deployment, Workflow Management Systems, Graphics Processing Unit (GPU), Pytorch, Kubernetes, Slurm, Machine Learning Operations, Data Pipelines, Docker
- **Published:** November 13, 2026
- **Apply:** https://www.dice.com/job-detail/72b84098-5ec1-47c6-a6c0-40b1e8641875

## About the Role

+ 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.

## Description

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.