Member of Technical Staff, Data & Training Infrastructure

Arena Physica is on a mission to accelerate hardware innovation that powers human progress. Our name is inspired by Theodore Roosevelt's 'Citizenship in a Republic' speech. To us, entering the Arena means committing fully and accepting the risk of failure in pursuit of an audacious, worthy cause. We believe the future belongs to those brave enough to build it.

Our team of 50 combines AI engineering and applied physics expertise with deep experience in enterprise deployments. We're headquartered in NYC with presences in San Francisco and Los Angeles, backed by ~$90M from Initialized, Founders Fund, Goldcrest Capital, Fifth Down Capital, and Shield Capital.

If you're ready to do the most important work of your career, join us in the Arena.

At Arena Physica, we're building electromagnetic superintelligence. Our AI platform Atlas operationalizes physics-grounded intelligence to verify, debug, and optimize hardware across its lifecycle. Atlas is already trusted globally by the world's most advanced hardware companies, including AMD, Anduril, and Bausch & Lomb, for applications across R&D, integration testing, production assembly, and field repair.

As a Machine Learning Engineer focused on Data & Training Infrastructure, you will join Arena's Platform team to build the data generation and training substrate behind the first electromagnetic foundation model. You will design the systems that turn simulation, measurement, expert workflows, and customer-grounded hardware problems into high-quality training data at scale.

This role sits at the intersection of large-scale ML infrastructure, scientific computing, and applied physics. You will work closely with applied researchers, electrical engineers, and product engineers to orchestrate solver farms, standardize multimodal data corpora, improve training throughput, and make foundation model development faster, more reproducible, and more reliable.

• Scale electromagnetic training data generation - Build infrastructure for generating, validating, versioning, and replaying synthetic and physically grounded EM datasets across solver farms, hardware-in-the-loop campaigns, and customer-inspired design spaces.
• Build the data foundation for Heaviside - Design dataset APIs, schemas, lineage systems, and quality gates for simulations, measurements, design files, S-parameters, meshes, fields, telemetry, documents, and expert annotations.
• Own high-throughput training pipelines - Develop distributed data loading, preprocessing, sharding, caching, and observability systems that keep large model training jobs performant across GPU and HPC environments.
• Partner with applied research - Work with researchers building electromagnetic foundation models to translate coverage targets, evaluation failures, and model needs into new data generation programs and infrastructure capabilities.
• Make ML experimentation reproducible - Build tooling for dataset snapshots, experiment traceability, regression detection, and training run analysis so Arena can move quickly without losing scientific rigor.
• Operationalize physics-grounded intelligence - Connect EM solvers, lab measurements, and platform services into production-grade pipelines that let Atlas learn from the real workflows hardware engineers use every day.
• Travel domestically and internationally (10-20% of your time).
• Work in person at Arena's NYC HQ when not traveling.

• 5 years of software engineering or ML infrastructure experience at a venture-backed startup, top technology company, frontier AI lab, or high-performing research organization
• Strong experience building distributed systems, data platforms, or training infrastructure for large-scale machine learning workloads
• Proficiency with Python and at least one modern ML framework such as PyTorch or JAX. Experience with large-scale data processing, storage, orchestration, and observability systems across cloud or HPC environments
• Deep practical judgment around reliability, throughput, reproducibility, and developer ergonomics for research and production systems
• Comfort working with ambiguous research requirements and turning them into robust, scalable platform primitives
• Strong communication skills and the ability to collaborate across ML researchers, electrical engineers, software engineers, and customer-facing teams
• Self-directed ownership mindset and excitement for building foundational infrastructure in a fast-moving environment
• [Preferred] Experience with scientific ML, neural operators, physics simulation, EDA tooling, EM solvers, or hardware design workflows
• [Preferred] Experience with Slurm, Ray, Kubernetes, AWS Batch, Elastic Fabric Adapter, distributed filesystems, or high-performance data loading for GPU clusters.
• [Preferred] Familiarity with synthetic data generation, active learning, data quality evaluation, or model-driven dataset curation
• [Preferred] An interest in electromagnetic systems, RF, signal integrity, power integrity, or the application of AI to real-world engineering work

• 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