Data Engineer

Charter:

Join Axiom as a founding team member and help build a technology ecosystem that will replace animal testing and ultimately reshape clinical trials through agentic systems that can accurately predict human outcomes.

About Axiom:

Axiom is building a compounding ecosystem to replace animal testing and, over time, reshape how clinical trials are run. It starts with deeply understanding the needs of drug hunters inside large pharma. Those needs shape the world-class datasets we build from scratch. We then use that data to advance our own ML research, while also collaborating with leading AI labs to improve frontier models’ ability to reason over Axiom’s data inside Axiom’s agent harness. This creates a compounding loop: deeper customer understanding shapes the data we generate; better data improves frontier models, Axiom’s fine-tuned models, and our agentic infrastructure; stronger models and tooling expand the capabilities we can offer; and those capabilities are forward deployed into pharma's drug discovery workflows, where scientists use them to solve the highest value drug discovery problems. In turn, this helps us identify the next problems to tackle. Today, we are focused on solving drug-induced liver injury through an integrated data and agentic system already being used by 7 of the top 20 pharma companies and several of the world’s most innovative biotechs. Over time, Axiom will build the world’s largest human datasets across all the major organ systems, paired with an agentic harness that uses this data to predict human drug outcomes dramatically better than animals.

What you will do

You will own core data infrastructure across Axiom’s research, ML, lab, and product systems.

• Build and maintain Axiom’s core data platform for ingesting, processing, validating, storing, and serving chemical, biological, clinical, and customer datasets.

• Build Axiom’s LabOS: the software layer that connects lab protocols, compound logistics, assay execution, plate/well metadata, instrument outputs, QC checks, data processing, model inference, and customer-facing results.

• Turn raw experimental outputs into clean, versioned, ML-ready datasets across high-content imaging, transcriptomics, proteomics, mass spectrometry, ADME, dose-response, and clinical outcome data.

• Design simple, reliable APIs and data interfaces that let scientists, ML researchers, and product engineers access the data they need without fighting infrastructure.

• Build LLM powered systems for literature research, clinical data extraction, evidence curation, and dataset generation.

• Develop distributed systems for running large-scale LLM jobs that clean, normalize, deduplicate, and structure biological and clinical data.

• Scale inference pipelines for image models, graph neural networks, chemical models, and mechanistic agents.

• Automate ETL from diverse sources, including lab instruments, CRO outputs, public databases, customer files, internal research tables, cloud storage, and literature-derived datasets.

• Create rigorous data validation, testing, monitoring, lineage, and observability systems so Axiom can trust the datasets that drive model training, evaluation, customer delivery, and scientific decisions.

• Work closely with scientists to understand messy real-world data needs and translate them into robust infrastructure.

• Support customer-facing data delivery systems, including raw data transfer, processed feature exports, model predictions, compound metadata, and versioned result packages.

• Build infrastructure that accelerates every team at Axiom.

What we are looking for

We are looking for someone who combines engineering taste, scientific curiosity, and extreme ownership.

You might be a great fit if:

• You have built large-scale data platforms used by many internal teams or external users.

• You are excited by messy, heterogeneous scientific data and want to make it clean, reliable, searchable, and useful.

• You can move fluidly between backend engineering, distributed systems, ML infrastructure, data modeling, DevOps, and user-facing tooling.

• You are comfortable talking to scientists, understanding their workflows, and building systems that make their work dramatically faster.

• You care deeply about correctness, reproducibility, versioning, and data quality.

• You have experience building AI- or LLM-powered data systems, especially for research workflows, retrieval, curation, or structured extraction.

• You enjoy turning ambiguous research needs into simple, reliable infrastructure.

• You want to own critical systems at an early-stage company.

• You are deeply curious about biology, chemistry, drug discovery, AI, product, and business.

• You could work in big tech, but you would rather build the data foundation for a company trying to change how medicines are discovered.

Technical skills we value

We do not expect every candidate to have all of these, but we are especially excited by experience with:

• Python, Pandas, NumPy, Polars, PyArrow, DuckDB, SQL, and the broader Python data ecosystem.

• Distributed systems and large-scale compute using Kubernetes, Slurm, Modal, Ray, Anyscale, Daft, Dask, Spark, or similar tools.

• Cloud infrastructure on AWS, GCP, or Azure.

• Infrastructure as code with Terraform, Pulumi, or similar tools.

• CI/CD, automated testing, deployment systems, and production observability.

• Data warehouses, lakehouses, object storage, and columnar formats such as Parquet.

• Workflow orchestration tools such as Airflow, Dagster, Prefect, Flyte, or Argo.

• LLM-powered data extraction, retrieval systems, evaluation harnesses, embeddings, and human-in-the-loop review systems.

• ML inference infrastructure for image models, graph neural networks, chemical models, or large language models.

• APIs, backend services, and internal tools that make complex data easy to use.

• Scientific, biological, chemical, clinical, or healthcare data systems.

• Petabyte-scale data processing.