DeepRec.ai
US Recruitment Consultant: Guiding GenAI professionals towards their dream careers
Join a frontier AI team building systems that can act in the physical world, experimenting, optimizing, and controlling real processes through advanced ML, simulation, and automation. This group is pushing the boundaries of physical intelligence, backed by significant long-term funding and a mandate to invent from first principles.
If you want to:
Work on problems few teams in the world can touch
Build RL systems that power real tools, workflows, and scientific processes
Operate in a fast, high-ownership, deeply technical culture
…this is the kind of role that defines a career.
The Role You’ll design and deploy reinforcement learning systems that control complex tools, optimize multi-step processes, and operate across high-fidelity simulations and digital twins. Expect hands‑on research, real‑world experimentation, and tight collaboration with teams across ML, simulation, and systems engineering.
What You’ll Do
Build RL environments for tool control, workflow optimization, and long‑horizon decision‑making
Develop safe and constrained RL methods, verifier‑driven rewards, and offline to online training pipelines
Create state/action representations and evaluation frameworks for reliable policy behavior
Work with cross‑functional researchers and engineers to deploy RL agents into real workflows
What You Bring
Strong background in RL, optimal control, or sequential decision‑making
Experience applying RL to complex simulated or physical systems
Familiarity with safe/constrained RL, verifiers, or advanced evaluation pipelines
Ability to design environments, rewards, and diagnostics at scale
Comfort working across ML, simulation, and systems interfaces
Seniority level Not Applicable
Employment type Full‑time
Job function Information Technology
Base pay range $200,000.00/yr – $250,000.00/yr
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If you want to:
Work on problems few teams in the world can touch
Build RL systems that power real tools, workflows, and scientific processes
Operate in a fast, high-ownership, deeply technical culture
…this is the kind of role that defines a career.
The Role You’ll design and deploy reinforcement learning systems that control complex tools, optimize multi-step processes, and operate across high-fidelity simulations and digital twins. Expect hands‑on research, real‑world experimentation, and tight collaboration with teams across ML, simulation, and systems engineering.
What You’ll Do
Build RL environments for tool control, workflow optimization, and long‑horizon decision‑making
Develop safe and constrained RL methods, verifier‑driven rewards, and offline to online training pipelines
Create state/action representations and evaluation frameworks for reliable policy behavior
Work with cross‑functional researchers and engineers to deploy RL agents into real workflows
What You Bring
Strong background in RL, optimal control, or sequential decision‑making
Experience applying RL to complex simulated or physical systems
Familiarity with safe/constrained RL, verifiers, or advanced evaluation pipelines
Ability to design environments, rewards, and diagnostics at scale
Comfort working across ML, simulation, and systems interfaces
Seniority level Not Applicable
Employment type Full‑time
Job function Information Technology
Base pay range $200,000.00/yr – $250,000.00/yr
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