Machine Learning Scientist – Generative Molecular Design

The AI Simulation (AI Sim) division at SandboxAQ applies physics and AI simulation to problems in drug discovery and material sciences. The role is to help build the generative engine at the centre of SandboxAQ's material discovery work. The platform finds compounds that match a desired property profile by navigating chemical space while balancing multiple competing objectives.

Responsibilities

Architect and evolve the flagship generative platform as a core contributor to the engine for discovering new materials; Implement and optimize state-of-the-art generative models, including diffusion, flow-matching, GFlowNets, LLM- and agent-based methods, to navigate high-dimensional chemical space and solve multi-objective optimization problems; Own the reward interface, integrating learned surrogates and simulation-derived oracles as the objective signal and designing reward formulations for objectives in tension; Develop and deploy computational methods and workflows to generate and evaluate hypotheses, guiding design decisions and project direction; Identify relevant data sources and take responsibility for data ingestion, QC, and validation; Collaborate daily with a core group of ML researchers to implement frontier generative architectures, while working closely with the broader R&D, discovery, engineering, and product teams

Requirements

Hands-on expertise building and shipping modern generative or applied ML for material discovery, with at least 4+ years of experience delivering these solutions in industry or an equivalent research setting; Demonstrated ability to write clean, maintainable, and robust production-grade code designed for industry-scale applications; Proven ability to build scalable ML, e.g. using PyTorch, TensorFlow, or JAX; Familiarity with cloud-based platforms (e.g. Google Cloud Platform, AWS) for compute and data processing and storage; PhD or MSc in Computational Chemistry, Physics, Computer Science, or equivalent; Excellent communication skills and a record of fostering successful internal collaboration in a fast-paced environment; Experience developing ML code in a collaborative environment using modern software engineering best practices

Nice to have

Active learning and Bayesian optimization, including design-make-test loops with expensive oracles such as physics simulations (DFT, molecular dynamics) or experimental lab-in-the-loop assays; Guided generative modeling for material discovery, including constrained generation within makeable chemical space, inference-time steering (guided diffusion, sequential Monte Carlo), or fine-tuning (LoRA, reinforcement learning); Agentic and LLM approaches to material discovery

Benefits

Competitive base salary commensurate with experience; Equity and performance-based incentives; Comprehensive health, dental, and vision insurance; 401(k) with company match; Generous parental leave; Flexible hybrid work arrangements; Generous PTO; Direct exposure to CHIPS Act-funded programs; Senior scientific and executive leadership access; Mentorship; Dedicated learning budgets