Applied Physicist/Engineer (AI)

Location: Boston, United States; hybrid. Remote candidates are considered on a case-by-case basis.

Company

hirify.global is an early-stage company building AI systems to discover and commercialize new physics at scale.

What you will do

• Model real physical systems with AI, including thermal, fluid, electrical, structural, and coupled multiphysics systems.
• Build simulations, surrogate models, and learned models that improve on traditional solvers in accuracy, speed, or both.
• Train and validate models against classical solvers, measured data, and facility results, including inputs outside the training set.
• Use models and agents to search design spaces and connect analysis with engineering design.
• Determine where high-fidelity solvers are required and where learned models can be used effectively.
• Deliver reproducible, tested systems that customers can run and reuse across engagements.

Requirements

• PhD or equivalent research record in mechanical, electrical, aerospace, or chemical engineering, applied physics, computational science, or machine learning for science.
• Demonstrated experience modeling a meaningful physical system and validating the model against a solver, facility, or measured data.
• Deep expertise in at least one domain: thermal and fluid systems, power systems, structures, or electrochemistry.
• Hands-on experience with computational engineering, including CFD, FEA, power-system, or multiphysics codes, discretization, convergence, and boundary conditions.
• Applied machine learning experience with PyTorch or JAX, including training, debugging, and evaluation beyond synthetic benchmarks.
• Experience deploying models behind APIs with versioning, reproducibility, tests, and HPC or cloud GPU environments.

Nice to have

• Experience with neural operators, physics-informed networks, differentiable simulation, or uncertainty quantification.
• Experience with open-source simulation tools such as OpenFOAM, OpenDSS, pandapower, CalculiX, or comparable systems.
• Experience with design-space optimization, including mixed-integer, derivative-free, or Bayesian search.
• Peer-reviewed publications in computational physics, engineering, or machine learning for science.

Culture & Benefits

• Full ownership from specification through shipping and on-call support.
• AI-native development using agentic coding tools and specifications designed for both humans and agents.
• Preference for simple, reliable designs that ship and testing against real systems instead of mocks.
• Competitive compensation with salary, benefits, and meaningful early-stage equity.
• Cross-disciplinary work focused on physics depth, modeling judgment, ML fluency, and shipping velocity.