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What is the difference between Fulltime Reinforcement Learning Phd vs Machine Learning Engineer?

AspectFulltime Reinforcement Learning PhdMachine Learning Engineer
Required CredentialsPhD in Computer Science, AI, or related fieldBachelor's or Master's in CS, AI, or related field
Work EnvironmentResearch-focused, academic or R&D labsIndustry, product development teams
Employer & Industry UsageUniversities, research institutions, tech companiesTech companies, startups, enterprise firms
Common Search & ComparisonYesNo

Fulltime Reinforcement Learning Phds typically focus on research and theoretical development in AI, often working in academic or R&D settings. Machine Learning Engineers apply AI techniques to develop practical applications in industry. While both roles require strong AI knowledge, the Phd emphasizes research, whereas the Engineer emphasizes implementation.

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Fulltime Reinforcement Learning Phd jobs near you
Infographic showing various Fulltime Reinforcement Learning Phd job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Reinforcement Learning Engineer - Whole Body Control

San Jose, CA • On-site

Figure
See more jobs at Figureexternal

$150K/yr

Full-time

Medical

Re-posted 21 days ago

Job description

Figure is an AI Robotics company autonomous general-purpose humanoid robots. The goal of the company is to ship humanoid robots with human level intelligence. Its robots are engineered to perform a variety of tasks in the home and commercial markets. We are based in North San Jose, CA and require 5 days/week in-office collaboration. It's time to build.
We are looking for a Reinforcement Learning Engineer to develop, train, deploy, and evaluate advanced reinforcement learning algorithms for whole body control of our humanoid robot.
Key Responsibilities:
  • Develop, train, and deploy reinforcement learning algorithms for whole body control
  • Determine the observations, actions, and model types that unlock maximum performance
  • Identify and close the most important sim-to-real gaps
  • Define, test, and evaluate performance metrics for learned policies
  • Harden the control stack to ensure rock solid robustness

Requirements:
  • Strong background in dynamics and control, ideally of legged robots
  • Experience with reinforcement learning algorithms for robotics: PPO, SAC, etc
  • Experience tuning hyperparameters and cost functions for these RL algorithms
  • Familiarity with common RL techniques such as: domain randomization, curriculum learning, reward shaping, etc.
  • Capable of leading complex controls projects and mentoring junior engineers

Bonus Qualifications:
  • Experience with behavior cloning techniques (e.g. distillation)

The US base salary range for this full-time position is between $150,000 and $350,000 annually.
The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.

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