Founding Platform Engineer, Data & ML Systems

About CellType

CellType is building foundation models and agent systems for biology.

We believe the next major advances in biotech AI will come from rich biological data, strong model systems, and reliable infrastructure working together. We work with pharma and biotech partners on problems such as preclinical-to-clinical translation, response prediction, biomarker discovery, and scientific reasoning across complex biological datasets.

We are building the core intelligence layer for biology, and that requires a world-class data and ML platform.

About the role

We are hiring a Founding Platform Engineer to build the infrastructure backbone behind our training, evaluation, and inference stack.

We are looking for someone who can build the systems that make biological data usable for model development at speed and at scale: ingestion, indexing, search, retrieval, dataset interfaces, reproducibility, validation, orchestration, observability, and distributed performance.

You will work on the full path from raw data to training-ready datasets to reliable production workflows. The right person will make it dramatically easier for the rest of the team to build, evaluate, and ship models.

What you'll do

Build and maintain data infrastructure for model training, evaluation, and inference

Design and scale high-performance inference serving systems for biological foundation models

Design standardized dataset interfaces so biological data is consistent, discoverable, and easy to use across the team

Build ingestion and processing pipelines for public, proprietary, and customer datasets

Build indexing, search, and retrieval systems that make large datasets queryable and useful in practice

Establish safeguards and validation systems so datasets are reproducible, versioned, and trustworthy once standardized

Improve throughput, latency, and reliability of distributed data loading and ML pipelines

Profile and eliminate performance bottlenecks across GPU, networking, and storage layers

Automate fault detection and recovery for serving and training systems

Build internal tools for dataset inspection, debugging, quality control, and operational visibility

Partner closely with ML engineers and researchers so the platform fits real workflows rather than abstract platform ideals

Help define how we handle permissions, privacy, compliance boundaries, and operational rigor for sensitive biological and customer data

You may be a fit if you

Have deep experience in backend, infrastructure, distributed systems, or data platform engineering

Have built scalable data pipelines or stateful distributed systems in production

Have experience building or operating large-scale inference or training systems

Have a deep understanding of GPU execution constraints, memory trade-offs, and data-loading bottlenecks around training workloads

Have experience with dataset infrastructure for large-scale ML systems, training pipelines, or inference-adjacent systems

Have worked with multimodal or very large datasets that cannot simply fit in memory

Have hands-on experience with data indexing, search, or retrieval infrastructure, and understand how to make large datasets discoverable, queryable, and usable in practice

Can reason about system-level trade-offs between latency, throughput, and cost

Have experience working with privacy-sensitive or compliance-sensitive data systems

Have built internal developer tools for ML or data teams

Have a track record of owning critical production infrastructure

Are comfortable designing APIs, modular abstractions, and internal platform interfaces with strong attention to user experience

Have strong instincts around reliability, reproducibility, and operational simplicity

Are comfortable with cloud infrastructure, containers, Kubernetes, Infrastructure-as-Code, CI/CD, and observability

Produce maintainable code and make pragmatic architecture decisions under time pressure

Thrive in a small team where ownership is broad and priorities can change