Member of Technical Staff, Data & Training Infrastructure
New York, NY$175k - $250kFull-timePosted 3mo ago
About the role
Machine Learning Engineer focused on Data & Training Infrastructure at Arena Physica, building the data generation and training substrate behind the first electromagnetic foundation model. Interface between large-scale ML infrastructure, scientific computing, and applied physics to enable scalable, reproducible, and reliable model development and production pipelines.
Responsibilities
- 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.
Qualifications
- 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
Skills
Required:PythonMachine Learning InfrastructureDistributed SystemsResearch collaboration
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.
About Arena Physica
Electromagnetic Superintelligence
HomeThesisPlatformServicesResearchCompanyCareersNews
More open roles at Arena Physica
- Member of Technical Staff, Evaluations1mo agoNew York, NY · Engineering
- Member of Technical Staff, Product2mo agoNew York · Engineering
- Research Scientist, Machine Learning3mo agoHybrid (New York, NY; SF Bay Area) · Applied Research