Applied AI Engineer for Physics & Digital Twins

Physical Superintelligence; Boston (MA); On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

Equity

Remote work considered

Job summary

Physical Superintelligence in Boston is building platform infrastructure at the intersection of computational science, AI systems, and software engineering to advance physics discovery at scale. The role focuses on shipping ML models, simulations, and digital twins for real-world physics problems, with an engineering-led culture and emphasis on end-to-end delivery.

You will work directly with customers and PhD researchers, translating data into clean inputs for ML pipelines, and delivering

Qualifications

• Five or more years building ML systems in production with grounding in applied physics, computational science, or engineering.
• You can read a domain paper, converse with a senior domain engineer, and reason about accuracy vs speed vs extrapolation risk.
• You have shipped ML models, simulations, or digital twins for physics or engineering problems.
• You communicate technically with researchers and practitioners and ship working artifacts, not slides.

Responsibilities

• Work with customers to apply AI/ML to hard physics problems and ship results that can run in production.
• Build demos and engagement tooling with live dashboards and runnable outputs.
• Translate real-world data into clean inputs for ML and simulation pipelines; define schemas and ingestion pipelines.
• Drive engagements end-to-end from scoping to implementation and customer communication.

Skills

Production ML

Physics literacy

Customer-facing

Engineering instincts

Education

PhD or Master’s in physics/engineering

Tools

OpenFOAM

Ansys

COMSOL

Job description

Physical Superintelligence in Boston is building platform infrastructure at the intersection of computational science, AI systems, and software engineering to advance physics discovery at scale. The role focuses on shipping ML models, simulations, and digital twins for real-world physics problems, with an engineering-led culture and emphasis on end-to-end delivery.

You will work directly with customers and PhD researchers, translating data into clean inputs for ML pipelines, and delivering