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Task Agent Jobs in Michigan (NOW HIRING)

* Build long-horizon RL environments and tasks for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts * Shape environments end to end: stateful, resumable ...

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Front Desk Agent

Clarkston, MI ยท On-site

$14.25 - $18/hr

The Front Desk Agent is responsible for a variety of tasks, including: * Greeting guests as they arrive at the hotel * Checking guests in and out of the hotel * Answering phone calls and responding ...

Front Desk Agent

Clarkston, MI ยท On-site

$14.50 - $18.50/hr

The Front Desk Agent is responsible for a variety of tasks, including: * Greeting guests as they arrive at the hotel * Checking guests in and out of the hotel * Answering phone calls and responding ...

Front Desk Agent

Clarkston, MI ยท On-site

$14.50 - $18.50/hr

The Front Desk Agent is responsible for a variety of tasks, including: * Greeting guests as they arrive at the hotel * Checking guests in and out of the hotel * Answering phone calls and responding ...

Showing results 21-40

Task Agent information

What cities in Michigan are hiring for Task Agent jobs?

Cities in Michigan with the most Task Agent job openings:

Infographic showing various Task Agent job openings in Michigan as of August 2026, with employment types broken down into 86% Full Time, 12% Part Time, and 2% Contract. Highlights an 83% Physical, 1% Hybrid, and 16% Remote job distribution.

Long-Horizon Coding Task Expert

Ann Arbor, MI โ€ข On-site

Full-time

Posted 9 days ago


Job description

  • Build long-horizon RL environments and tasks for agent training, spanning many steps and hours of realistic effort rather than single-shot prompts

  • Shape environments end to end: stateful, resumable systems with snapshotting, checkpointing, and branching rollouts, at multi-node scale where needed

  • Generate and refine ideas for tasks and agentic trajectories across multi-turn, tool-using, and computer-use agent loops with persistent state across turns

  • Design reward structure for sparse-reward settings: milestone and process rewards, subgoal and task decomposition, and credit assignment across long trajectories

  • Validate that the work is correct, hard, and covers the right edge cases, including rubric design for partial credit, contamination avoidance, and defenses against reward hacking

  • Design curricula that ramp task difficulty rather than shipping fixed-difficulty tasks

  • Build automated pipelines that curate high-quality long-horizon RL environments and tasks at scale

  • Write reliable, well-tested Python infrastructure rather than one-off research scripts

  • Work directly with our research team with high ownership, building novel systems rather than maintaining legacy ones