Machine Learning Scientist, Generative Molecular Design – Material Discovery

SandboxAQ

. Architect and evolve SandboxAQ's flagship generative platform for material discovery .

Posted 10/26/2026full-timeRemote • United StatesMid-LevelSenior💰 $134,400 - $252,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and deploying generative machine learning solutions for material discovery, with a strong focus on scalable implementations and collaboration across multidisciplinary teams. Proficient in navigating complex optimization problems and integrating advanced computational methods to enhance research outcomes.

Highest-signal resume keywords

Generative Machine Learning SolutionsPyTorch, TensorFlow, or JAXCloud-Based Platforms (Google Cloud Platform, AWS)Computational Chemistry or Physics PhD/MScActive Learning and Bayesian Optimization

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Generative Machine LearningProduction-Grade Code DevelopmentMulti-Objective OptimizationData Ingestion and Quality ControlPhysics Simulations (DFT, Molecular Dynamics)Guided Generative ModelingReinforcement LearningSequential Monte CarloAgent-Based MethodsFlow-Matching

Soft Skills

Excellent Communication SkillsCollaboration

Tools & Technologies

Google Cloud PlatformAWSML Frameworks (PyTorch, TensorFlow, JAX)

Industry Keywords

Material DiscoveryComputational MethodsHigh-Dimensional Chemical SpaceSimulation-Derived OraclesReward Formulations

Tech Stack

Tools & technologies

AWSCloudGoogle Cloud PlatformPyTorchTensorflow

About the role

Key responsibilities & impact

Architect and evolve SandboxAQ's flagship generative platform for material discovery
Implement, extend, and optimize frontier generative architectures
Navigate high-dimensional chemical space and solve multi-objective optimization problems
Integrate learned surrogates and simulation-derived oracles as reward signals
Design reward formulations balancing competing objectives, including performance, environmental, 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 own data ingestion, quality control, and validation
Collaborate daily with ML researchers to implement frontier generative architectures
Work with R&D, discovery, engineering, and product teams
Turn models into production capabilities that discovery scientists can run

Requirements

What you’ll need

At least 4+ years of experience delivering modern generative or applied ML solutions for material discovery in industry or an equivalent research setting
Hands-on expertise building and shipping modern generative or applied ML for material discovery
Ability to write clean, maintainable, robust production-grade code for industry-scale applications
Ability to build scalable ML 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
Excellent communication skills and a record of fostering successful internal collaboration
Experience developing ML code collaboratively using modern software engineering best practices
Active learning and Bayesian optimization experience is highly desired
Experience with physics simulations such as DFT or molecular dynamics, or experimental lab-in-the-loop assays, is highly desired
Guided generative modeling for material discovery is highly desired
Experience with diffusion, flow-matching, GFlowNets, LLMs, agent-based methods, guided diffusion, sequential Monte Carlo, LoRA, or reinforcement learning is highly desired
Agentic and LLM approaches to material discovery are highly desired

Benefits

Comp & perks

Equity
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
Mentorship
Dedicated learning budgets
Opportunities for professional growth