About the company :

We are an AI company that transforms biomedical data into actionable knowledge for clinicians, researchers, and life-sciences teams. Founded in 2013, we are headquartered in Cambridge, Massachusetts, with a large engineering centre in Bangalore. Our software makes sense of decades of unstructured electronic health records, alongside molecular and imaging data.

Our flagship platform, nSights, gives researchers access to de-identified real-world data across clinical, molecular and imaging modalities, covering more than 20 years of data and over 11 million patient records. We work with leading academic health systems, including a long-term partnership with Mayo Clinic that began in 2017.

About the team :

Platform Engineering builds the foundation every other team stands on. We run a hybrid estate : thousands of bare-metal servers, an in-house AI compute cluster, and workloads on GCP and AWS.

On top of that we build Workspace, the self-service environment where data scientists spin up Python and R environments, train models and explore biomedical data in minutes instead of days. When we do our job well, researchers ship faster and model training runs on infrastructure we own rather than rent.

The role :

You will lead Platform Engineering : the people, the roadmap and the reliability of everything from physical racks and AI accelerators up to the developer experience. You'll grow the team, set its technical direction with senior engineers, and make sure the platform keeps pace with the product, data and ML teams that depend on it. As the group grows, you may manage team leads as well as engineers.

This is a hands-on leadership role. You won't be writing production code every day, but you'll be expected to review designs, debug alongside the team during incidents, and make sound technical calls.

What you'll do :

- Hire, onboard and retain platform, infrastructure and SRE engineers. Own job descriptions, interview loops and hiring decisions.

- Run regular 1 : 1s, set clear expectations, and give direct, timely feedback.

- Run performance reviews and calibrations, and build career paths that grow engineers into senior, staff and lead roles.

- Shape team structure as scope grows, splitting into focused teams (for example bare metal and AI compute, Kubernetes and cloud, developer platform) when it makes sense.

- Build a culture of ownership, blameless learning and written decision-making.

Set technical direction :

- Own the platform roadmap and balance new capability, reliability work and tech debt.

- Make build-versus-buy decisions, and decide what runs on-prem and what runs in the cloud.

- Plan capacity and spend for AI compute, storage and cloud, and track infrastructure cost against usage.

- Make sure security and compliance controls for de-identified patient data are built into the platform rather than added later.

Deliver and operate :

- Turn requests from product, data science and ML teams into a prioritised plan with clear scope and timelines, and communicate trade-offs early.

- Own platform reliability : SLOs, observability, a sustainable on-call rotation, incident response and blameless postmortems.

- Measure the team's delivery and the platform's health (deployment frequency, lead time, incident rate, time to restore, environment provisioning time) and act on the trends.

- Introduce engineering practices that make delivery predictable across teams, including design reviews, release processes and runbooks.

What you'll bring :

- 10+ years in software engineering, including a strong background in platform, infrastructure, SRE or DevOps work.

- 3+ years managing engineers directly, including hiring, performance management and growing people into senior roles.

- Hands-on depth in Kubernetes and at least one major cloud (GCP or AWS), and comfort with on-prem or bare-metal infrastructure.

- A track record of running production systems reliably : SLOs, incident management, on-call design and postmortems.

- Experience owning a technical roadmap and making build-versus-buy and cost trade-offs.

- Strong judgment in setting priorities when many teams want your team's time.

- Clear written and verbal communication with engineers, scientists and executives.

Nice to have :

- Experience running GPU or AI accelerator infrastructure and high-performance storage.

- Experience building internal developer platforms or self-service data science environments.

- Experience scaling a team from a handful of engineers to multiple teams.

- Experience in regulated environments (HIPAA, SOC 2, ISO 27001).

- Startup or early-stage experience, where you built processes from scratch.

- Experience using AI coding tools and agents to raise the team's productivity.