ML Researcher

Axiom is building the translational intelligence layer for drug discovery: AI and agentic systems that help scientists predict human toxicity earlier, more accurately, and more mechanistically than animal studies or legacy in vitro assays.

Unexpected toxicity is one of the largest reasons drug programs fail. Today, drug discovery teams still rely on animal studies, fragmented assays, and expert judgment to decide which molecules are safe enough to advance. We believe this can be dramatically improved.

At Axiom, we generate and curate massive multimodal datasets spanning chemical structures, primary human cell imaging, multicellular tissue systems, transcriptomics, proteomics, mass spectrometry, ADME, dose-response curves, clinical outcomes, and human exposure. To date, we've built the largest experimental-to-clinical dataset in the world and we are just getting started. We use these datasets to train models and agents that connect chemistry, biology, mechanism, and clinical risk.

We are looking for a machine learning researcher to help define and build the core AI systems behind Axiom: models that learn from human-relevant experimental biology, predict toxicity at clinically meaningful exposures, explain mechanisms, and eventually help scientists design safer molecules.

This is an end-to-end ML research role. You will work across data generation, data processing, model architecture, training, agentic workflows, evaluation, deployment, and product. You will build systems that drug hunters use to improve their drug discovery outcomes.

Be a founding member of the team building the first accurate AI systems for replacing animal and legacy toxicity experiments with human-relevant predictive models.

You will help answer one of the hardest questions in drug discovery:

• Given a molecule’s structure, potency, exposure, and biological response, will it be toxic in humans — and why?

You will help define Axiom’s core ML research agenda and build the models that power our product.

You will:

• Define end-to-end ML and agent systems spanning wet-lab data generation, data cleaning, feature extraction, representation learning, model training, evaluation, inference, deployment, and customer-facing outputs.

• Build novel models that learn the relationship between chemistry, biological response, dose, exposure, and human toxicity.

• Train large multimodal models on paired chemical structures, high-content cellular images, transcriptomics, proteomics, mass spectrometry, ADME, and clinical outcome data.

• Develop foundation models and representation-learning systems for biological images, molecules, and multimodal experimental readouts.

• Architect models that predict human toxicity as a function of dose, Cmax, in vitro potency, chemical structure, and biological state.

• Develop new ways to aggregate, pool, align, and interpret embeddings across assays, doses, timepoints, modalities, compounds, and biological systems.

• Work on contrastive learning, self-supervised learning, semi-supervised learning, multimodal learning, graph neural networks, biological image models, generative models, and mechanistic reasoning systems.

• Build models that can generalize across chemical space, mechanisms, targets, assays, and customer programs.

• Conduct rigorous error analysis to understand when models fail, why they fail, and what data would make them better.

• Collaborate with computational biologists, chemists, mass spec scientists, data engineers, and wet-lab teams to design experiments that maximally improve model performance.

• Help build Axiom’s mechanistic agents: systems that reason over experimental data, compare compounds to mechanistic neighbors, explain toxicity mechanisms, and guide scientific decisions.

• Own the research-to-product loop: prototype, train, evaluate, ship, observe real usage, improve, and repeat.

• Ship insanely great models and products to customers.

We are especially interested in people excited by:

• Multimodal ML across chemistry, cellular imaging, transcriptomics, proteomics, mass spectrometry, ADME, and clinical outcomes.

• Reasoning over massive amounts of multimodal experimental data, model outputs, literature, and mechanistic evidence.

• Self-supervised and semi-supervised learning on high-content imaging and biological readouts.

• Uncertainty estimation, calibration, and confidence for scientific decision-making.

• Mechanistic interpretability for biological and chemical models.

• Evaluation systems for models that must perform on real drug discovery problems, not toy benchmarks.

We are looking for someone with exceptional ML talent, strong engineering ability, and the ambition to become a leader in AI for biology and drug discovery.

You might be a great fit if:

• You have done at least one piece of work, in industry, academia, open source, or independently, that shows exceptional machine learning ability.

• You are deeply technical and comfortable writing PyTorch, debugging training runs, working with messy data, scaling inference, and building real systems.

• You are excited by non-standard, thorny modeling problems where the data is noisy, multimodal, sparse, biased, biological, and deeply important.

• You want to work on ML problems where better models can directly change scientific and clinical decisions.

• You are not afraid of the data dirty work required to make models better.

• You can move between research ideas and production systems.

• You care about evaluation, calibration, failure modes, and real-world usefulness.

• You are curious enough to learn biology, chemistry, toxicology, pharmacology, and drug discovery.

• You want to grow as both a researcher and an entrepreneur.

• You want your work to become a product that customers love and rely on.

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

• PyTorch, JAX, TensorFlow, or other deep learning frameworks.

• Python, NumPy, Pandas, Polars, PyArrow, scikit-learn, and scientific computing.

• Training and evaluating deep neural networks at scale.

• Representation learning, embeddings, contrastive learning, metric learning, and self-supervised learning.

• Computer vision models, especially for biological imaging, microscopy, cell painting, or high-content screening.

• Multimodal ML across images, molecules, text, omics, mass spec, or tabular data.

• Large-scale model training, distributed training, GPU infrastructure, inference pipelines, and cloud compute.

• Model evaluation, ablations, benchmarking, uncertainty estimation, calibration, and interpretability.

• LLMs, agents, retrieval, tool use, and reasoning systems.

• Biology, chemistry, toxicology, pharmacology, or drug discovery datasets.

Axiom is not a normal company, and this is not a normal ML research role.

We are looking for people who are intense, curious, practical, ambitious, and deeply motivated by the mission. You should want ownership, ambiguity, and responsibility. You should be excited to work on problems where the correct model architecture is not obvious, the dataset has to be invented, and the evaluation must be grounded in real scientific decisions.

The people who thrive here:

• Have extremely high agency.

• Move with urgency.

• Have exceptional taste for what matters.

• Care deeply about the truth.

• Are technically excellent and relentlessly curious.

• Can do research and engineering.

• Are practical, unpretentious, and collaborative.

• Want to ship products, not just papers.

• Are excited by biology and chemistry, even if they did not start there.

• Raise the bar for everyone around them.

• Want to build a generational company.

• We are looking for people who could work in big tech, but would not be satisfied there because they want to solve a harder, messier, and more consequential problem.