Finance

RL Environment Engineer

Work directly with our research team on long-horizon RL environment and task creation for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts Build and shape RL environments in your domain, including the setup, state handling, and tooling an agent interacts with Generate and refine ideas for tasks and agentic trajectories, then validate that the work is correct, hard, and covers the right edge cases Design reward functions, milestones, and rubrics that give meaningful signal across long trajectories, including partial credit where pass/fail is too blunt Identify failure modes, reward hacking, and degenerate solutions before they reach training

  • Montpelier, VT

  • no content

RL Environment Engineer

Work directly with our research team on long-horizon RL environment and task creation for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts Build and shape RL environments in your domain, including the setup, state handling, and tooling an agent interacts with Generate and refine ideas for tasks and agentic trajectories, then validate that the work is correct, hard, and covers the right edge cases Design reward functions, milestones, and rubrics that give meaningful signal across long trajectories, including partial credit where pass/fail is too blunt Identify failure modes, reward hacking, and degenerate solutions before they reach training

  • Evansville, IN

  • no content

RL Environment Engineer

Work directly with our research team on long-horizon RL environment and task creation for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts Build and shape RL environments in your domain, including the setup, state handling, and tooling an agent interacts with Generate and refine ideas for tasks and agentic trajectories, then validate that the work is correct, hard, and covers the right edge cases Design reward functions, milestones, and rubrics that give meaningful signal across long trajectories, including partial credit where pass/fail is too blunt Identify failure modes, reward hacking, and degenerate solutions before they reach training

  • Barre, VT

  • no content

RL Environment Engineer

Work directly with our research team on long-horizon RL environment and task creation for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts Build and shape RL environments in your domain, including the setup, state handling, and tooling an agent interacts with Generate and refine ideas for tasks and agentic trajectories, then validate that the work is correct, hard, and covers the right edge cases Design reward functions, milestones, and rubrics that give meaningful signal across long trajectories, including partial credit where pass/fail is too blunt Identify failure modes, reward hacking, and degenerate solutions before they reach training

  • Sterling Heights, MI

  • no content

RL Environment Engineer

Work directly with our research team on long-horizon RL environment and task creation for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts Build and shape RL environments in your domain, including the setup, state handling, and tooling an agent interacts with Generate and refine ideas for tasks and agentic trajectories, then validate that the work is correct, hard, and covers the right edge cases Design reward functions, milestones, and rubrics that give meaningful signal across long trajectories, including partial credit where pass/fail is too blunt Identify failure modes, reward hacking, and degenerate solutions before they reach training

  • Brookings, SD

  • no content

RL Environment Engineer

Work directly with our research team on long-horizon RL environment and task creation for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts Build and shape RL environments in your domain, including the setup, state handling, and tooling an agent interacts with Generate and refine ideas for tasks and agentic trajectories, then validate that the work is correct, hard, and covers the right edge cases Design reward functions, milestones, and rubrics that give meaningful signal across long trajectories, including partial credit where pass/fail is too blunt Identify failure modes, reward hacking, and degenerate solutions before they reach training

  • Minot, ND

  • no content

RL Environment Engineer

Work directly with our research team on long-horizon RL environment and task creation for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts Build and shape RL environments in your domain, including the setup, state handling, and tooling an agent interacts with Generate and refine ideas for tasks and agentic trajectories, then validate that the work is correct, hard, and covers the right edge cases Design reward functions, milestones, and rubrics that give meaningful signal across long trajectories, including partial credit where pass/fail is too blunt Identify failure modes, reward hacking, and degenerate solutions before they reach training

  • Billings, MT

  • no content

RL Environment Engineer

Work directly with our research team on long-horizon RL environment and task creation for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts Build and shape RL environments in your domain, including the setup, state handling, and tooling an agent interacts with Generate and refine ideas for tasks and agentic trajectories, then validate that the work is correct, hard, and covers the right edge cases Design reward functions, milestones, and rubrics that give meaningful signal across long trajectories, including partial credit where pass/fail is too blunt Identify failure modes, reward hacking, and degenerate solutions before they reach training

  • Wichita, KS

  • no content

RL Environment Engineer

Work directly with our research team on long-horizon RL environment and task creation for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts Build and shape RL environments in your domain, including the setup, state handling, and tooling an agent interacts with Generate and refine ideas for tasks and agentic trajectories, then validate that the work is correct, hard, and covers the right edge cases Design reward functions, milestones, and rubrics that give meaningful signal across long trajectories, including partial credit where pass/fail is too blunt Identify failure modes, reward hacking, and degenerate solutions before they reach training

  • Scottsdale, AZ

  • no content

RL Environment Engineer

Work directly with our research team on long-horizon RL environment and task creation for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts Build and shape RL environments in your domain, including the setup, state handling, and tooling an agent interacts with Generate and refine ideas for tasks and agentic trajectories, then validate that the work is correct, hard, and covers the right edge cases Design reward functions, milestones, and rubrics that give meaningful signal across long trajectories, including partial credit where pass/fail is too blunt Identify failure modes, reward hacking, and degenerate solutions before they reach training

  • Sacramento, CA

  • no content

RL Environment Engineer

Work directly with our research team on long-horizon RL environment and task creation for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts Build and shape RL environments in your domain, including the setup, state handling, and tooling an agent interacts with Generate and refine ideas for tasks and agentic trajectories, then validate that the work is correct, hard, and covers the right edge cases Design reward functions, milestones, and rubrics that give meaningful signal across long trajectories, including partial credit where pass/fail is too blunt Identify failure modes, reward hacking, and degenerate solutions before they reach training

  • Rochester Hills, MI

  • no content

RL Environment Engineer

Work directly with our research team on long-horizon RL environment and task creation for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts Build and shape RL environments in your domain, including the setup, state handling, and tooling an agent interacts with Generate and refine ideas for tasks and agentic trajectories, then validate that the work is correct, hard, and covers the right edge cases Design reward functions, milestones, and rubrics that give meaningful signal across long trajectories, including partial credit where pass/fail is too blunt Identify failure modes, reward hacking, and degenerate solutions before they reach training

  • Raleigh, NC

  • no content