axiombio - ML Researcher
Requirements
• 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.
Responsibilities
• You will help define Axiom’s core ML research agenda and build the models that power our product. • 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.
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