Machine Learning Scientist (AI)

Location: Remote within the United States.

Salary: US Tier 1 $168,000–$252,000; US Tier 2 $151,200–$226,800; US Tier 3 $134,400–$201,600 per year, plus equity and performance-based incentives.

Company

SandboxAQ develops AI solutions and Large Quantitative Models for life sciences, financial services, navigation, cybersecurity, and other sectors.

What you will do

• Architect and evolve a generative platform for material discovery.
• Implement and optimize generative models, including diffusion, flow-matching, GFlowNets, LLM-based, and agent-based methods.
• Integrate learned surrogates, simulation-derived oracles, and other reward signals while balancing competing performance, environmental, and safety objectives.
• Develop and deploy computational methods and workflows for generating and evaluating material-discovery hypotheses.
• Identify data sources and own data ingestion, quality control, and validation.
• Collaborate with ML researchers and R&D, discovery, engineering, and product teams to productionize models.

Requirements

• At least 4 years of hands-on experience building and shipping modern generative or applied ML solutions for material discovery in industry or an equivalent research setting.
• Ability to write clean, maintainable, robust, production-grade code for industry-scale applications.
• Experience building scalable ML with PyTorch, TensorFlow, or JAX.
• Familiarity with cloud platforms such as Google Cloud Platform or AWS for compute, data processing, and storage.
• PhD or MSc in Computational Chemistry, Physics, Computer Science, or an equivalent field.
• Excellent communication skills and a record of successful internal collaboration in a fast-paced environment.
• Experience developing ML code collaboratively using modern software engineering best practices.

Nice to have

• Deep hands-on expertise in active learning and Bayesian optimization, including design-make-test loops using physics simulations or experimental lab-in-the-loop assays.
• Experience with guided generative modeling for material discovery, makeable chemical-space constraints, inference-time steering, sequential Monte Carlo, LoRA, or reinforcement learning.
• Experience with agentic and LLM approaches to material discovery.

Culture & Benefits

• Comprehensive health, dental, and vision insurance.
• 401(k) with company match and generous parental leave.
• Flexible hybrid work arrangements, generous PTO, and protected focus and recovery time.
• Exposure to CHIPS Act-funded programs and senior scientific and executive leadership.
• Mentorship and dedicated learning budgets.
• Multidisciplinary, collaborative environment focused on belonging, respect, ownership, and impact.