Machine Learning Scientist, Generative Molecular Design – Material Discovery

Job not on LinkedIn

🔥 0 minutes ago

🇺🇸 United States – Remote

💵 $134.4k - $252k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 0%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

SandboxAQ

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2021

🤖 Artificial Intelligence

🔒 Cybersecurity

💊 Pharmaceuticals

Artificial Intelligence • Cybersecurity • Pharmaceuticals

SandboxAQ is a company developing quantitative AI and quantum-inspired technologies to solve real-world problems across drug discovery, materials science, cybersecurity, and navigation. They build large quantitative models (LQMs) grounded in physics and chemistry to predict molecular properties, accelerate therapeutic design, and simulate complex chemical and catalytic processes, while also applying their expertise in AI and post-quantum cryptography to enhance digital security and navigation in GPS-denied environments.

📋 Description

• 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

• 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

• 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