Senior Staff Data Engineer

Biohub•Redwood City, NY

3d•Hybrid

About The Position

Biohub is a 501(c)(3) biomedical research organization building a large-scale scientific initiative that combines frontier AI, compute, and experimental biology to accelerate scientific discovery and help address disease. The Data Engineering team owns the strategy, sourcing, and implementation of data that supports AI research and development, connecting public data resources and Biohub experimental platforms to AI systems. As a Senior Staff Data Engineer, you will design systems to ingest data from public repositories, transform heterogeneous biological formats into AI-ready datasets, combine them with proprietary data, and deliver training datasets to researchers developing biological AI models. The infrastructure you build will influence what these models can learn. You will work on a small, well-resourced team with significant autonomy, applying AI tools in engineering workflows while prioritizing code quality, operational reliability, scalability, and scientific correctness. This is a hybrid position in Redwood City, CA or New York City, NY. The role requires working onsite at least 60% of the working month, approximately three days per week. The anticipated annual base pay range is $270,000–$300,000. The position may be eligible for Biohub's discretionary annual performance bonus program.

Requirements

12 or more years of experience building reliable, operable data systems at petabyte scale.
Strong software engineering fundamentals.
Experience deploying distributed computing frameworks such as Databricks, Spark, or Ray for large-scale data processing.
Experience building and deploying large-scale data platform solutions using infrastructure as code, such as Terraform or CDK.
Experience with cloud infrastructure, preferably AWS, and on-premises infrastructure.
Comfort with ambiguity and the ability to make progress as requirements evolve.
Interest in AI-native development practices and tooling.

Nice To Haves

Background in computational biology, bioinformatics, or life sciences.
Experience with genomics datasets and formats such as FASTQ, BAM, or VCF, or imaging formats such as OME-Zarr or HDF5.

Responsibilities

Design and build data pipelines that process genomic and imaging data at petabyte scale.
Solve performance and bandwidth challenges through creative engineering.
Build agent-based systems for automated dataset curation, quality control, and workflow generation.
Create data cataloging and registration tools that make datasets discoverable and accessible.
Collaborate with AI Research teams to translate model requirements into data specifications.
Work with scientists to integrate public and internal data into large-scale AI-ready datasets.
Improve pipeline reliability and observability, working toward success rates above 99% without manual intervention.