Who we are:
Our name is inspired by Theodore Roosevelt’s ‘Citizenship in a Republic’ speech, which pays homage to the ‘(hu)man in the Arena’. To us, entering the Arena means committing oneself fully and accepting the risk of failure in the pursuit of an audacious, worthy cause.
We’re a close-knit team of scientists and builders exploring the boundaries of how artificial intelligence can benefit humanity. If you share our passion for delving deep into real-world problems and solving them with fully autonomous AI, join us in the Arena.
What we do:
Our collective future is being built in the physical world, but the builders of tomorrow’s technology can no longer rely on yesterday’s tools. At Arena, we’re building the world’s first AI industrial engineer designed to solve the most complex hardware and manufacturing challenges. Our product, Atlas, is built with an understanding of the behavior of physical systems, powered by a superior knowledge of core domains of physics. Paired with its ability to reason about multimodal industrial data, Atlas can test, debug, optimize, and repair physical systems and products in the real world. Arena is already trusted by some of the most advanced industrial companies in the world (AMD, Bausch & Lomb), and we're rapidly already scaling into the defense, automotive, and pharmaceuticals industries, and we’re just getting started.
How you will contribute:
As a Machine Learning Scientist, RF, you will define the future of RF circuit design, combining the latest breakthroughs in machine learning with human expertise from the world’s leading experts in RF.
Working at the intersection of physics-informed AI and RF engineering, you’ll implement a variety of ML models for the design and optimization of next-generation RF circuits. Some examples of what you’ll be working on: (i) deep neural networks that function as surrogates for rapid simulation of electromagnetic behavior (Maxwell's equations), (ii) reinforcement learning and other optimization methods to tackle critical aspects like antenna layout, BFIC routing, power, signal distribution, topology selection and component sizing and (iii) Generative models for inverse design.
This role offers the unique opportunity to shape the future of AI-native RF circuits. RF circuits are fundamental to modern communications, radars, novel therapeutics and much more. In this role you’ll get the chance to work closely with our customers – who are the most advanced hardware teams on the planet, as well as with Arena’s own RF experts who are legends in the industry.
Travel domestically and internationally (up to 20-25% of your time) and work in person at Arena's NYC headquarters when not traveling.
You Have:
- 4+ years of professional experience, including experience building surrogates predicting the behavior of physical systems
- Model training, deployment and maintenance experience in a production environment
- Strong skills in deep learning and related frameworks (i.e., Pytorch, Tensorflow, DeepSpeed, Ray, Pytorch Lightning)
- Experience in dealing with high performance, large scale ML systems in a production environment
- Ability to translate production requirements into improvements in model architecture for better performance and generalizability
- Experience with distributed training architectures
- Strong high-level programming skills and passionate about good engineering, clean code, scalability
- Interested in the full ML stack from initial R&D through deploying models into production
- Strong written and verbal communication skills to operate in a cross functional team environment
Benefits & Perks Include:
- 99% of the monthly premium for Aetna medical insurance, plus vision and dental coverage
- 401(k) Retirement Plan
- Unlimited PTO
- Lunch every day from local restaurants via Sharebite
- Relocation support provided
The base salary range for this position is $180,000 - $225,000 yr. However, base pay offered may vary depending on job-related knowledge, skills, and experience. In addition to base salary, we also offer competitive equity and benefits packages.
