Axiom is building AI systems for drug safety and toxicity assessment to replace lab and animal experiments, aiming to accelerate drug discovery and bring new medicines to patients faster.
Requirements
- Strong proficiency in Python and core data libraries such as Pandas, NumPy, and the broader Python data ecosystem
- Hands-on experience building distributed systems from scratch using tools like Kubernetes, Slurm, Modal, Anyscale, Ray, Daft, Dask, or Spark
- Solid DevOps background—experience with CI/CD systems, cloud platforms (AWS, GCP, Azure), Terraform, and compute provisioning
Responsibilities
- Build and maintain the core data systems for Axiom’s research platform, including ingestion, processing, storage, and serving
- Work with scientists to understand their data needs and create simple APIs for accessing chemical and biological datasets
- Architect LLM systems to curate, clean, and analyze human clinical trial data, and evaluations and observability for these systems
- Develop distributed systems to run large-scale LLM jobs that clean and curate biological and clinical data
- Set up quality checks, testing tools, and monitoring systems to ensure data and model outputs stay accurate and reliable
- Own the pipelines, systems, and tooling that turn raw chemical, biological, and clinical data into ML-ready training data and into customer-ready insights
- Build LLM-driven literature research and data platforms, scale inference of image and graph neural networks, automate ETL from diverse sources, and ensure the integrity of datasets that drive critical decisions internally and externally
Other
- Be a founding member of a team building the first accurate AI systems for drug toxicity prediction
- High energy, high agency, and have great taste for what matters
- Relentless “observe, orient, decide, act” loop, and be constantly identifying what needs to happen and getting it done
- Great curiosity which will keep them at the frontier of tech and help them interface between AI, engineering, product, biology, chemistry, and business
- Deep, obsessive curiosity about both the science and the business driving the work
