Voio

Frontier healthcare AI platform for radiology

Member of Technical Staff - ML Infrastructure Engineer

Platform EngineeringConfirmed live in the last 24 hours

Full-timeBerkeley, CA, USANo salary listed

About the Job

Requirements

Strong production experience operating GPU infrastructure at scale, including multi-node distributed training, high-speed networking such as InfiniBand or equivalent, storage tiering, and debugging failed long-running jobs.
Comfort working across Linux, Python, Kubernetes or equivalent orchestration, at least one major cloud provider (AWS, GCP, or Azure), and CI/CD.
A developer-experience mindset, treating researchers as primary users and measuring success by making infrastructure unobtrusive.
Solid software engineering fundamentals, including building dependable tooling with tests, documentation, and a sane API.

Responsibilities

Own the infrastructure and tooling used daily by the research team.
Run and scale GPU clusters across cloud providers, including job orchestration with SkyPilot, multi-node training reliability, capacity planning, and cost optimization.
Manage petabyte-scale medical imaging and clinical data, including DICOM, NIfTI, parquet, and raw EHR; build and maintain data pipeline tiers from object storage through NFS to NVMe scratch.
Own the Weights & Biases integration and build abstractions, command-line interfaces, and templates that simplify distributed training job submission and experiment launch, tracking, comparison, and resumption.
Build Grafana dashboards and structured alert tiers routed through Slack and PagerDuty to detect issues early.
Conduct postmortems, maintain runbooks, and establish sensible defaults to prevent researchers from being blocked on infrastructure.

Desired Qualifications

Production experience with SkyPilot, lakeFS, or Weights & Biases.
Background in healthcare or biomedical data, including DICOM, NIfTI, FHIR, or HIPAA-aware infrastructure.
Experience designing data versioning systems for mixed-modality datasets at scale.
Open-source contributions to machine learning infrastructure projects.

We're hiring an ML Infrastructure Engineer to own the infrastructure and tooling that our research team runs on every day. This is a hybrid DevOps / Developer Experience role: you'll stand up and operate large GPU clusters, build data pipelines that move massive medical datasets through training jobs efficiently, and create the tooling that lets our researchers move fast without thinking about plumbingML Infrastructure Engineer

About Voio
Voio is a healthcare AI company building frontier foundation models for medicine. We're based in San Francisco and our research team trains on extremely large multimodal medical datasets. The infrastructure this role owns goes directly into models aimed at improving clinical decision-making at scale.

The Role

If you've ever sat next to an ML researcher, watched them lose half a day to a flaky GPU node or a misversioned dataset, and thought "I could fix this for everyone" — this role is for you.

What you'll work on

GPU cluster operations. Run and scale our GPU cluster across cloud providers. Job orchestration with SkyPilot, multi-node training reliability, capacity planning, and cost optimization. You'll be the person who makes a 64-GPU run "just work."
Data infrastructure at scale. Manage petabyte-scale medical imaging and clinical data, such as DICOM, NIfTI, parquet, raw EHR. Build and maintain the data pipeline tiers (object store → NFS → NVMe scratch) so GPUs are never starved.
Experiment tracking and developer experience. Own our W&B integration. Build the abstractions, CLIs, and templates that turn a complex distributed training job into a one-line submission. Make it easy for researchers to launch, track, compare, and resume experiments.
Observability and on-call. Grafana dashboards, structured alert tiers routed through Slack and PagerDuty. Catch problems before researchers do.
Reliability culture. Postmortems, runbooks, sane defaults. The bar is that no researcher is ever blocked on infrastructure for more than a few minutes.

What we're looking for

Strong production experience operating GPU infrastructure at scale — multi-node distributed training, high-speed networking (InfiniBand or equivalent), storage tiering, and the kind of debugging that happens at 11pm when a long-running job dies on epoch 3.
Comfort across the stack: Linux, Python, Kubernetes or equivalent orchestration, at least one major cloud (AWS / GCP / Azure), CI/CD.
A genuine developer-experience instinct. You treat researchers as your primary users, and you measure success by how invisible the infrastructure becomes.
Solid software engineering fundamentals — you can build tooling that other engineers rely on, with tests, docs, and a sane API.

Bonus points

Production experience with SkyPilot, lakeFS, or W&B.
Background in healthcare or biomedical data (DICOM, NIfTI, FHIR, HIPAA-aware infrastructure).
Experience designing data versioning systems for mixed-modality datasets at scale.
Open-source contributions to ML infrastructure projects.

Stack
SkyPilot · lakeFS · W&B · S3-compatible object stores · Grafana · Slack · PagerDuty · Python · Linux

Logistics

Location: Berkeley-based

About the Company

Voio

Frontier healthcare AI platform for radiology

Voio builds a unified AI platform to help radiologists read medical images faster and more accurately. Its Pillar models analyze CT, MRI, and X-ray scans to detect hundreds of pathologies and draft high-quality reports, integrating images, patient history, and prior exams in one interface. The company also offers Pillar-0, an open-source 3D medical-image model that shows improved accuracy over other tech companies’ models and encourages researchers to validate and extend the technology. Voio differentiates itself through a strong academic founder base from UC Berkeley and UCSF, a focus on addressing radiology workforce shortages with an end-to-end reading platform, and by providing open-source components to promote transparency and evidence-based AI in radiology. The goal is to reduce diagnostic delays and radiologist burnout by streamlining image interpretation and report generation across all imaging modalities.

Company size: 11-50
Company stage: Seed
Total funding: $8.6M
Headquarters: Berkeley, CA
Founded: 2025

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