---
title: 'Machine Learning Scientist, Generative Molecular Design (Material Discovery) at SandboxAQ'
canonical: 'https://feeny.ai/job/machine-learning-scientist-generative-molecular-design-material-discovery-hnn5gpxgjkzc'
type: 'job'
last_seen: '2026-11-04'
---

# Machine Learning Scientist, Generative Molecular Design (Material Discovery) at SandboxAQ

- **Company:** SandboxAQ
- **Location:** United States
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-10-26
- **Last confirmed live:** 2026-11-04
- **Apply:** https://jobs.ashbyhq.com/sandboxaq/8579117b-a05a-4b64-9595-58fb5e83c685

## Job description

## About SandboxAQ

SandboxAQ is a high-growth company delivering AI solutions that address some of the world's greatest challenges. The company’s Large Quantitative Models (LQMs) power advances in life sciences, financial services, navigation, cybersecurity, and other sectors. We are a global team that is tech-focused and includes experts in AI, chemistry, cybersecurity, physics, mathematics, medicine, engineering, and other specialties. The company emerged from Alphabet Inc. as an independent, growth capital-backed company in 2022, funded by leading investors and supported by a braintrust of industry leaders. At SandboxAQ, we’ve cultivated an environment that encourages creativity, collaboration, and impact. By investing deeply in our people, we’re building a thriving, global workforce poised to tackle the world's epic challenges. Join us to advance your career in pursuit of an inspiring mission, in a community of like-minded people who value entrepreneurialism, ownership, and transformative impact.

The Opportunity Introduction to the team: The AI Simulation (AI Sim) division at SandboxAQ applies physics and AI simulation to problems in drug discovery and the material sciences. You will work within a highly skilled, multidisciplinary team of computational chemists, physicists, AI experts, and software engineers, on a generative platform that is used across both application areas.

Introduction to the role: We are looking for a Machine Learning Scientist 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. You will: (1) implement, extend, and optimize frontier generative architectures for material discovery; (2) own the interface between the generative engine and the property predictors that supply its reward signal; (3) identify, ingest, and quality-control the data these models are trained and evaluated on; and (4) turn the models you build into production capability that discovery scientists across the company can run. [See how SandboxAQ is helping build America's semiconductor supply chain from the materials up](https://www.sandboxaq.com/post/sandboxaq-helping-build-americas-semiconductor-supply-chain)

## Key Responsibilities

- Architect and evolve our flagship generative platform, serving as a core contributor to the engine that will redefine how the industry discovers new materials.
- Implement and optimize state-of-the-art generative models - including diffusion, flow-matching, and GFlowNets, or LLM- and agent-based methods — to navigate high-dimensional chemical space and solve the multi-objective optimization problems that material discovery presents.
- Own the reward interface. Integrate learned surrogates, and simulation-derived oracles as the objective signal driving generation, and design reward formulations that hold up when several objectives are in genuine tension e.g. performance against environmental and safety constraints.
- Develop and deploy computational methods and workflows to generate and evaluate hypotheses, guiding design decisions and influencing 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.

Essential Skills & Experience

- 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.

Highly Desired Skills & Experience We are growing this team across three areas of generative expertise, listed in the order we most need them. You do not need all three — deep, hands-on strength in any one of them is what we are looking for.

- Active learning and bayesian optimization. Driving a design-make-test loop with an expensive oracle such as a physics simulation (DFT, molecular dynamics) or an experimental lab-in-the-loop assay.
- Guided generative modeling for material discovery; generation constrained to makeable chemical space; inference-time steering (guided diffusion, sequential monte-carlo and related approaches) or fine-tuning (LoRA, reinforcement learning) for adapting general models to a particular domain of interest;.
- Agentic and LLM approaches to material discovery.

Why Join Us? We offer competitive compensation, a comprehensive benefits package, and opportunities for professional growth.

- Compensation: Competitive base salary commensurate with experience, plus equity and performance-based incentives.
- Benefits: Comprehensive health, dental, and vision insurance; 401(k) with company match; generous parental leave.
- Work-Life Balance: Flexible hybrid work arrangements, generous PTO, and a culture that respects focus time and recovery.
- Career Development: Direct exposure to CHIPS Act-funded programs, senior scientific and executive leadership, mentorship, and dedicated learning budgets to support continued growth.

SandboxAQ Welcomes All We are committed to fostering a culture of belonging and respect, where diverse perspectives are actively sought and valued. Our multidisciplinary environment provides ample opportunity for continuous growth - working alongside humble, empowered, and ambitious colleagues ready to tackle epic challenges. Equal Employment Opportunity: All qualified applicants will receive consideration regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Accommodations: We provide reasonable accommodations for individuals with disabilities in job application procedures for open roles. If you need such an accommodation, please let a member of our Recruiting team know. Read: [Guidance for candidates on using AI Tools in interviews](https://www.sandboxaq.com/ai-in-interviews)

## About SandboxAQ

## Company Overview
- **One-liner**: SandboxAQ develops Large Quantitative Models (LQMs) that combine AI and quantum techniques to solve real-world challenges in drug discovery, materials, navigation, cybersecurity, and other high-impact sectors.
- **Entity Type**: Private (Late Stage; raised $1.6B total, including Series E, venture rounds, and grants)
- **Headquarters**: Palo Alto, California, United States
- **Founded**: 2021 (spun out from Alphabet in 2022)
- **Founders**: Jack Hidary (CEO); Eric Schmidt serves as Chairman of the Board of Directors

## Core Business
- **Primary Industries**: AI/Quantum software, life sciences (drug discovery), cybersecurity, financial services, navigation/defense, materials science
- **Target Customers**: B2B, serving large enterprises and government organizations in pharma, energy, defense, finance, and logistics
- **Mission**: Revolutionize industries with groundbreaking solutions at the intersection of AI and quantum technology

## Products & Services
- **AQBioSim**: AI simulation platform for accelerating molecular design and reducing failure rates in drug discovery (SaaS)
- **LQMs for Cybersecurity**: Cryptography management and quantum-safe security solutions (e.g., post-quantum cryptography, key management)
- **AI Navigation & Sensing**: Precise positioning and navigation systems resistant to jamming/spoofing, leveraging AI and quantum sensors (SaaS + hardware integration)
- **Materials & Chemistry Modeling**: LQMs that predict atomic-level properties to unlock new compounds for chemicals and materials (SaaS)
- **Financial Services LQMs**: Models for risk analysis, portfolio optimization, and fraud detection (SaaS)

## Market Standing
- **Valuation**: Not publicly disclosed; total funding reached $1.6 billion
- **Key Metric**: Total Funding: $1.6B (13 rounds); Annual Revenue (estimated): $9M (via disclosed financial data)
- **Notable Investors/Partners**: T. Rowe Price, Eric Schmidt, Breyer Capital, Guggenheim Partners, Marc Benioff, Thomas Tull, Paladin Capital Group, Nvidia (backed via grant/award)
- **Growth Signals**:
  - Headcount: ~296 employees (+10.6% YoY)
  - Operating in 16 countries (US, Canada, UK, France, Spain, Switzerland, etc.)
  - Acquired Good Chemistry (Jan 2024) and Cryptosense (Sep 2022) to expand capabilities
  - Recent funding: $95M venture round (July 2025), additional private equity round (Feb 2026)
  - US government awarded $500M (Nvidia-backed) for finding new outcomes (likely defense/pharma contract)
  - Recognized with multiple industry awards (Fierce Innovator in Life Sciences 2025, Wash100 Award, etc.)

## Competitive Advantages
- **Quantitative AI (LQMs)**: Models that understand the physical world (molecules, materials, navigation) – distinct from purely data-driven LLMs
- **Alphabet Spinout Heritage**: Foundational technology and talent from Google’s parent company
- **Multidisciplinary Team**: Physicists, chemists, cryptographers, AI researchers – rare combination
- **Strong Government & Enterprise Ties**: Direct contracts with US defense and pharma, plus deep investor network
- **Integrated Portfolio**: One platform covering simulation, cybersecurity, and sensing – creating cross-selling opportunities

## Strategic Focus
- **Scale LQM Applications**: Expand from drug discovery into climate, energy, and defense
- **Deepen Government Partnerships**: Capitalize on $500M US award and develop mission-critical navigation/security systems
- **Build Ecosystem**: Open APIs and partnerships with cloud providers (AWS, Azure, Databricks) to embed LQMs into existing workflows
- **Talent Acquisition**: Aggressively hiring ML engineers, computational chemists, and research scientists to maintain technical lead

## Why Work Here
- **Culture**: Emphasizes inclusion, service leadership, and fearlessness. Described as “the most humble player in the AQ industry” with a growth mindset.
- **Remote/Hybrid Policy**: Flexible work environment; home office stipend and connectivity stipend provided; regular offsites for collaboration.
- **Benefits**: Best-in-class medical, dental, vision; 100% paid maternity/paternity leave; 401k with company match; wellness stipend; learning & development programs; flexible time off including winter break and volunteering days.
- **Engineering Culture**: Hands-on work at the frontier of AI and quantum; cross-disciplinary teams (physics, chemistry, neuroscience, cryptography); opportunity to publish research and attend conferences.
- **Growth Opportunities**: Residency program for early-career scientists; stock options; clear career progression in a fast-growing late-stage startup.

## Sources
1. [sandboxaq.com](https://www.sandboxaq.com/)
2. [sandboxaq.com/company/about-us](https://www.sandboxaq.com/company/about-us)
3. [sandboxaq.com/careers](https://www.sandboxaq.com/careers)
4. [linkedin.com/company/sandboxaq](https://www.linkedin.com/company/sandboxaq)
5. [jobs.ashbyhq.com/sandboxaq](https://jobs.ashbyhq.com/sandboxaq)

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