Build the toolbox for AI scientists

Own Rafflesia Homology, our serverless search engine that runs directly on object storage. You'll make tree-of-life-scale search fast, reproducible, and an order of magnitude cheaper: designing immutable index formats, squeezing every query down to the bytes it truly needs, and benchmarking hard against systems like MMseqs2 and Foldseek. Systems, storage, and search background welcome; the biology is learnable.

Make records from UniProt, the PDB, variant databases, assay datasets, and papers work together. You'll design schemas, reconcile conflicting identifiers, track where data came from and how it changed, and expose the results through typed, versioned APIs. This role is for someone who likes untangling real data more than designing abstractions in the abstract.

Take biological models that only run in a researcher's environment and turn them into dependable services. You'll build the APIs, job system, and artifact storage behind structure prediction, embeddings, protein design, and variant-effect models. The work includes packaging checkpoints, making every result reproducible, handling retries and failures, and keeping GPU workloads fast and cost-effective across multiple providers.

Turn real structural-biology research into reproducible environments that show what agents can do, where they break, and what we should build next. You'll reproduce papers from raw data, design graders and partial-credit signals for long-horizon work, and turn agent failures into usable training signal. A fit if you like reading papers, rebuilding workflows, and finding exactly where a polished result stops being trustworthy.

Build the surfaces where scientists and agents inspect data, run tools, read eval traces, and turn raw infrastructure into daily research workflows. You'll own features end to end, from data model and API contract to a fast, precise, accessible interface, making complex scientific state legible without hiding provenance or uncertainty. Strong full-stack engineers with real product judgment and a taste for interaction detail.

Embed with pharma teams to turn their hardest research workflows into deployed tools, benchmarks, and evals. You'll connect fragmented scientific data, models, and internal systems, define clear interfaces and provenance, and turn successful engagements into durable platform capabilities rather than one-off consulting code. For engineers who thrive in ambiguity, work well with scientists, and can carry a project from an open-ended research problem to a reliable deployment.

Work alongside biotech teams to accelerate the path from experimental data to scientific decisions. You'll translate fast-changing workflows across discovery, protein engineering, and computational biology into reproducible tools that scientists and agents can use every day. You'll ship end to end, from data ingestion and model integration to interfaces and evaluation, and fold what you learn into reusable infrastructure for the next team. A fit for product-minded engineers who enjoy moving quickly and working close to the science.

Embed with AI labs training protein and biological foundation models to remove infrastructure bottlenecks from their research loops. You'll help teams use Rafflesia's object-native homology search to search massive sequence collections quickly, build and refresh datasets, detect related sequences, evaluate representations, and iterate faster as conventional search systems become the constraint. You'll benchmark real workloads, integrate the infrastructure into training and evaluation pipelines, and turn each deployment into scalable capabilities for other labs. For engineers excited by machine learning systems, biological data, retrieval infrastructure, and performance at scale.