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Research Scientist, Machine Learning

Arena Physica· Applied Research
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Hybrid (New York, NY; SF Bay Area)$200k - $300kFull-timePosted 3mo ago

About the role

Research Scientist — Machine Learning focused on setting the technical direction for Arena's electromagnetic foundation model, including model architecture, training/evaluation pipelines, data strategy, and publications. Located in New York, NY or SF Bay Area; full-time role within the research team.

Responsibilities

  • Designing our Model Architecture - Design and prototype model architectures grounded in EM physics and applied math — neural operators (FNO, AFNO, deformation-FNO, U-FNO), Transolver-class architectures, foundation-model backbones, and world-model-style joint-embedding predictive architectures, or geometry focused encoders (GeoPT, DeepSDF, point-cloud / mesh transformers). - Make principled choices based on physics priors: PINN losses, structural inductive biases, conservation enforcement, passivity / reciprocity constraints, and exterior-calculus-style discretization.
  • Fast Experimentation - Build and run end-to-end training and evaluation pipelines: dataloading, distributed training, ablations, scaling experiments, and large-model orchestration. - Drive experimental discipline — pre-registered hypotheses, paired comparisons, parameter-matched runs, confidence bands on results.
  • Expand the Data Strategy - Partner with the team on training-corpus composition, augmentation, curriculum design, and physics-informed multipliers to drive learning in and out of distribution. - Specify coverage targets and the simulation campaigns required to achieve them; close the loop with Arena's solver-farm, sim infrastructure, and lab measurements.
  • Translate Research into Atlas Deployments - Partner with the Atlas product and platform engineering teams to land research artifacts in production deployments across our customer base. - Define inference performance, accuracy, and uncertainty requirements that real-world use cases need; close the loop from production data back into research direction.
  • Author Research Publications - Author papers at top venues (NeurIPS, ICML, ICLR, IMS) that reinforce Arena's research footprint and externally validate the foundation-model approach. - Own at least one substantive publication per year as first author.
  • Drive External Collaborations - Develop and support research collaborations with academic labs and industry partners. - Represent Arena externally at conferences, workshops, and customer engagements.

Qualifications

  • PhD or equivalent research track in Electrical Engineering, Applied Physics, Applied Mathematics, or Computer Science.
  • Deep background in electromagnetic physics or in the applied mathematics of PDE solvers — ideally directly in EM.
  • Demonstrated focus on foundation models, neural operators, world models, Transolver-class architectures, or related operator-style ML.
  • A published track record at top ML or computational-physics venues — first-author or senior-author papers.
  • Strong intuition for the mathematics behind EM solvers (FEM / FDTD / MoM / eigenmode / Green's functions) and how that intuition translates into architectural choices.
  • Proficiency in Python and the common ML framework + tooling stack — frameworks such as PyTorch and JAX, distributed-training stacks (e.g. FSDP, DeepSpeed), experiment tracking (e.g. MLflow, Weights & Biases), and performance tooling (e.g. torch.compile, XLA, CUDA); comfort writing C / C++ when needed for performance-critical paths.
  • Track record of shipping experiments end-to-end — not just architecting in slideware, but actually running pipelines at meaningful scale.
  • [Preferred] Prior work integrating physics priors directly into model architectures (e.g., PINN losses, exterior-calculus-style discretizations, scale-invariance constraints).
  • [Preferred] Direct experience with EM simulators such as ADS, HFSS, MATLAB EM, or comparable solvers.
  • [Preferred] Experience with HPC ecosystems.

Skills

Required:Model Architecture design grounded in EM physicsTraining and Evaluation PipelinesData Strategy for MLProduction Deployment & Atlas IntegrationAcademic Publishing

Benefits

  • 100% of the monthly premiums covered with Aetna medical vision, and dental insurance for you and your dependents
  • 401(k) Retirement Plan
  • Unlimited PTO
  • Lunch every day from local restaurants via Sharebite
  • Relocation support provided
Last updated Oct 22, 2026. Interested? Apply on the company site.
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