AI Researcher (Reinforcement Learning)

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

• Build and train AI agents and training systems that learn to perform physics.
• Research physical reasoning, scientific tool-use action spaces, long-horizon reward structures, and scalable training infrastructure.
• Design evaluation workflows and benchmarks that distinguish genuine reasoning from pattern matching and benchmark gaming.
• Develop instrumentation that makes agent behavior interpretable.
• Publish research on AI for science and collaborate with physicists and engineers to ship complete systems.

Requirements

• PhD in machine learning, computer science, physics, mathematics, or a related quantitative field.
• Recent publications at top venues such as NeurIPS, ICML, ICLR, or comparable physics-ML venues.
• Hands-on experience building agents and training models with reinforcement learning, including systems that outperform meaningful baselines.
• Proficiency with modern machine learning frameworks and distributed training, with the ability to scale from a single GPU to a cluster.
• Deep fluency with PyTorch or JAX and distributed training tools such as Ray, XLA, or Accelerate.
• Physics or mathematics background and experience with RL algorithms, simulators, scientific tools, games, or rigorous benchmark suites.

Nice to have

• Experience with PPO, SAC, MuZero, multi-agent self-play, search-augmented methods, or comparable algorithms.
• Open-source contributions, conference presentations, or research artifacts adopted by the community.

Culture & Benefits

• Ownership from specification through shipping and on-call.
• Contracts are written before logic, and systems are tested against real environments rather than mocks.
• AI-native development using agentic coding tools and specifications designed for humans and agents.
• Competitive compensation including salary, benefits, and meaningful early-stage equity.
• Evaluation emphasizes technical breadth, systems thinking, scientific curiosity, and shipping velocity.