# Machine Learning Scientist - Molecular Force Fields at SandboxAQ

- Company: SandboxAQ
- What the company does: SandboxAQ leverages the compound effects of AI and advanced computing to address some of the biggest challenges impacting society. SandboxAQ technologies include AI simulation, cryptography management for cybersecurity, and AI sensing for global organizations.
- Company website: https://www.sandboxaq.com
- Type: Startups (AI role)
- Level: Mid level
- Location: United States
- Work setup: Remote
- Pay: $134K to $252K base salary per year (USD)
- Posted: 2026-10-26
- Apply by: 2026-12-10
- Apply: https://jobs.ashbyhq.com/sandboxaq/2ecf82f8-6bab-48d5-b34a-e1c745b7fc60?utm_source=1752vc&utm_medium=careers
- Page: https://www.1752.vc/careers/jobs/sandboxaq-machine-learning-scientist-molecular-force-fields/

## About the role

Introduction to the team: This role is for the PFAS team which sits within SandboxAQ's Chemical Simulation (ChemSim) group. Our mission is to develop PFAS-lean or PFAS-free substitutes and formulations for semiconductor process and fab materials that meet both performance specifications and environmental and safety requirements in complex semiconductor manufacturing environments. We combine generative ML, physics based simulation (e.g. Found on 1752vc Careers, the job board for startup and VC roles.

## What they're looking for

- PhD in Computational Chemistry, Materials Science, Physics, Machine Learning, or a related field, with deep specialization in machine learned interatomic potentials or atomistic simulation
- Demonstrated fluency with descriptor based, modern equivariant, and message-passing potential architectures (e.g. MACE, NequIP, Allegro, TensorNet, DeePMD, ANI-class, or FairChem/UMA-family models) — including what each is good for and where each fails
- Strong programming skills in Python and hands on experience with modern ML frameworks (e.g. PyTorch, JAX), including training at scale on GPU clusters
- Practical experience coupling learned potentials into production molecular dynamics engines (e.g. LAMMPS, GROMACS, OpenMM, ASE) and extracting converged physical observables from the resulting trajectories
- Experience building automated computational workflows and curating large scientific datasets — provenance, versioning, and reproducibility
- Demonstrated validation discipline: a track record of characterising model error and out-of-domain failure rather than reporting best case benchmark numbers

Tags: AI Simulation

Source: 1752vc Careers, https://www.1752.vc/careers/jobs/sandboxaq-machine-learning-scientist-molecular-force-fields/