S

SandboxAQ

AI and quantum security solutions

Machine Learning Scientist - Generative Molecular Design, Material Discovery

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeRemote in USARemote

About the Job

Requirements

  • At least 4 years of experience building and shipping modern generative or applied machine learning solutions for material discovery in industry or an equivalent research setting.
  • Ability to write clean, maintainable, robust production-grade code for industry-scale applications.
  • Ability to build scalable machine-learning systems using PyTorch, TensorFlow, or JAX.
  • Familiarity with cloud-based platforms such as Google Cloud Platform or AWS for compute, data processing, and storage.
  • PhD or MSc in Computational Chemistry, Physics, Computer Science, or equivalent.
  • Experience developing machine-learning code collaboratively using modern software-engineering best practices.

Responsibilities

  • Architect and evolve the flagship generative platform, contributing to the engine for discovering new materials.
  • Implement and optimize state-of-the-art generative models, including diffusion, flow-matching, GFlowNets, or LLM- and agent-based methods, to navigate high-dimensional chemical space and solve multi-objective material-discovery optimization problems.
  • Integrate learned surrogates and simulation-derived oracles as generation objective signals, and design reward formulations that balance competing objectives such as performance, environmental impact, and safety constraints.
  • Develop and deploy computational methods and workflows to generate and evaluate hypotheses, guide design decisions, and influence project direction.
  • Identify relevant data sources and take responsibility for data ingestion, quality control, and validation.
  • Collaborate daily with machine-learning researchers to implement generative architectures and work closely with research and development, discovery, engineering, and product teams.

Desired Qualifications

  • Experience with active learning and Bayesian optimization, including driving a design-make-test loop with an expensive oracle such as a physics simulation (density functional theory or molecular dynamics) or an experimental lab-in-the-loop assay.
  • Experience with guided generative modeling for material discovery, including generation constrained to makeable chemical space; inference-time steering such as guided diffusion or sequential Monte Carlo; or fine-tuning such as low-rank adaptation or reinforcement learning to adapt general models to a specific domain.
  • Experience with agentic and large language model approaches to material discovery.

About the Company

SandboxAQ

AI and quantum security solutions

SandboxAQ helps organizations prepare for the impact of quantum computing by combining artificial intelligence and quantum technologies. Its offerings include crypto-agile security, quantum sensing, and quantum simulation and optimization, delivered as services and solutions to global clients. The company works with leading professional services firms to help clients implement AQ solutions, and it also engages in research fellowships and hiring for PhD and post-doc roles. Unlike others that only develop hardware or software, SandboxAQ emphasizes enabling enterprises and even nations to gain a competitive edge before scalable, fault-tolerant quantum computers are widely available, and to bridge the global digital divide through practical, implementable solutions. Its business model centers on providing services and solutions, monetized through client engagements and partnerships with professional services firms.

Company size
201-500
Company stage
Grant
Total funding
$1.6B
Headquarters
Palo Alto, CA
Founded
2021

Recently Posted Jobs

Explore more opportunities at SandboxAQ

See more jobs