Backend Systems Engineer

Charter:

Build data systems that fuel the first scientific AI to replace lab and animal toxicity experiments.

About Axiom and the role:

We’re building AI systems for drug safety and toxicity assessment. Drug toxicity causes about half of drug program failures and by tackling it we can help drug discovery teams across the industry bring new medicines to patients far faster. We’re looking for a data engineer who’s excited to own the pipelines, systems, and tooling that turn raw chemical, biological, and clinical data into ML-ready training data and into customer-ready insights. You’ll work closely with our ML, lab, and product teams to 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. This role is ideal for someone who wants to build clean, reliable systems that directly impact the success of hundreds of drug programs.

What are we looking for:

We want to hire people who inspire us and level up the entire team. They should be high energy, high agency, and have great taste for what matters. They should have a relentless “observe, orient, decide, act” loop, and be constantly identifying what needs to happen and getting it done. They need to be technically excellent and obsessive masters of their craft, as well as having a great curiosity which will keep them at the frontier of tech and help them interface between AI, engineering, product, biology, chemistry, and business. They could work in big tech, but it won’t satisfy them. They want to go on an adventure which will be brutally challenging, and to share in the rewards and satisfaction at its end.

What you will be doing:

• Lead Axiom’s evolution into a world-class engineering company focused on enterprise ML software

• Design and build the core infrastructure that powers Axiom’s enterprise ML systems, including model evaluation/deployment, model inference/serving, and customer data management

• Architect scalable systems for inference, storage, and retrieval of chemical, biological, and clinical data

• Deploy large-scale reasoning agents from research environments into production, integrating them into on-prem customer-facing products and infrastructure

• Teach and empower scientists across ML, chemistry, and biology to become great engineers by instilling a great engineering culture

Various expertise which gets us interested:

• Built SaaS products that store and process large volumes of customer data.

• Worked directly with large enterprise customers and supported their complex software needs

• Designed and developed large-scale machine learning systems covering data access, training, evaluation, and deployment

• Handled the “messy” parts of ML deployment, such as evaluation pipelines, versioning, and monitoring

• Built LLM-powered data systems, with a focus on research workflows and information retrieval

Key criteria:

• Strong generalist software engineer with experience across cloud infrastructure,machine learning, backend systems, distributed systems

• Enjoys working with enterprise customers and simplifying complex technical solutions to meet their needs

• Built and deployed production systems used by large enterprise businesses

• Invested in team growth particularly when it comes to building strong engineering culture across the company

• Passionate about collaborating with researchers and scientists, helping them become strong engineers

• Takes full ownership of the customer experience—deeply focused on reliability and all the ways things can go wrong

• Demonstrates relentless curiosity for both the science behind the product and the business that drives it