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Postdoctoral In Reinforcement Learning Jobs in Missouri

Jobgether

Our partner is looking for a Research Engineer (Reinforcement Learning) based in Netherlands. Join a small, senior engineering team building the next generation of voice- and text-driven AI agents.

VinMotion

AI Motion Intern - VinMotion US

California, MO · On-site

$90 - $120/hr

Technical Skills Proficiency in Python, C++, TensorFlow, PyTorch. Experience with Reinforcement Learning (RL), Model Predictive Control (MPC), Whole‑Body Control (WBC), and Imitation Learning.

New

Washington University of St. Louis

The postdoc will also have the opportunity to work on other topics related to air quality modeling ... Strong programming skills and experience with machine learning applications in geoscience are ...

Block

You'll work at the intersection of representation learning, foundation models, reinforcement ... Strong foundations in modern machine learning, including deep learning, optimization ...

Posted today

Block

You'll work at the intersection of representation learning, foundation models, reinforcement ... Strong foundations in modern machine learning, including deep learning, optimization ...

Posted today

Block

You'll work at the intersection of representation learning, foundation models, reinforcement ... Strong foundations in modern machine learning, including deep learning, optimization ...

Posted today

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Postdoctoral In Reinforcement Learning information

What is a postdoctoral researcher in reinforcement learning?

A Postdoctoral Researcher in Reinforcement Learning is an individual who has completed a PhD and conducts advanced research in the field of reinforcement learning, a branch of artificial intelligence focused on how agents take actions in environments to maximize rewards. These researchers often work in academic, industrial, or governmental research settings, collaborating on projects that advance the theoretical foundations or practical applications of reinforcement learning. Their responsibilities may include designing experiments, developing algorithms, publishing papers, and mentoring graduate students.

What are the key skills and qualifications needed to thrive as a postdoctoral researcher in reinforcement learning?

To thrive as a Postdoctoral Researcher in Reinforcement Learning, you need a PhD in computer science or a related field, with deep expertise in machine learning, statistics, and algorithm development. Proficiency in programming languages such as Python, experience with deep learning frameworks (e.g., TensorFlow or PyTorch), and familiarity with reinforcement learning libraries are typically required. Strong analytical thinking, problem-solving ability, collaboration, and scientific communication skills help you excel in research teams and publish impactful work. These competencies are vital to advancing state-of-the-art research, developing novel algorithms, and contributing to the academic and industrial progress in AI.

What are some common challenges faced by postdoctoral researchers in reinforcement learning, and how can they be addressed?

Postdoctoral researchers in reinforcement learning often face challenges such as balancing independent research projects with collaborative work, staying up-to-date with rapidly evolving literature, and managing the pressure to publish in top conferences. Effective time management, regular engagement with the research community through seminars and workshops, and seeking mentorship from senior colleagues can help address these challenges. Additionally, collaborating with interdisciplinary teams can offer fresh perspectives and support, making it easier to navigate complex research problems.

What is the difference between Postdoctoral In Reinforcement Learning vs Postdoctoral In Machine Learning?

AspectPostdoctoral In Reinforcement LearningPostdoctoral In Machine Learning
Required CredentialsPhD in Computer Science, AI, or related field; strong programming skills; research experience in reinforcement learningPhD in Computer Science, AI, or related field; strong programming skills; research experience in machine learning
Work EnvironmentAcademic labs, research institutions, industry R&D teams focused on reinforcement learning applicationsAcademic labs, research institutions, industry R&D teams working on various machine learning techniques
Industry UsagePrimarily in AI research, robotics, gaming, and autonomous systemsBroader applications including data analysis, predictive modeling, and AI research

Postdoctoral In Reinforcement Learning specializes in research related to decision-making algorithms and autonomous systems, whereas Postdoctoral In Machine Learning covers a wider range of AI techniques. Both roles require similar credentials but differ in focus and application areas.

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Postdoctoral In Reinforcement Learning jobs near you
Infographic showing various Postdoctoral In Reinforcement Learning job openings in Missouri as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Research Engineer (Reinforcement Learning)

Jobgether

Remote

Full-time

Medical, Dental, Vision, PTO

This job post has expired today. Applications are no longer accepted.

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Research Engineer (Reinforcement Learning) based in Netherlands.

Join a small, senior engineering team building the next generation of voice- and text-driven AI agents.
You'll focus on post-training models to make agents more capable, reliable, and effective over long-running interactions.
Your work will span environments, verifiers, synthetic data, training experiments, evaluations, and production deployment.
You'll tackle challenging problems such as persistent context, reliable tool use, and multi-turn agent behavior.
The role combines hands-on research and engineering, with a strong emphasis on measurable improvements in model performance.
You'll work closely with experienced engineers in a remote, collaborative environment where technical craft and creativity are highly valued.
Your contributions will directly shape AI systems operating at significant production scale.

Accountabilities
  • Build training environments, verifiers, and supporting infrastructure for post-training models.
  • Own the synthetic data pipeline from data generation through quality assurance and validation.
  • Run end-to-end training experiments, analyze results, and clearly identify the factors driving model improvements.
  • Design and maintain evaluations that models must pass before production releases.
  • Select and adapt suitable open-weight foundation models for specific agent and product requirements.
  • Develop trained behaviors that perform consistently across both voice and text-based agents.
  • Deploy trained models to production and continuously improve them based on real-world usage and feedback.
  • Develop robust approaches to long-horizon interactions, accumulated context, and reliable tool use during live conversations.
Requirements:
  • Strong Python engineering skills and the ability to build reliable, production-quality systems.
  • Demonstrated experience taking a machine learning model from raw data through experimentation and into production.
  • A strong data-centric mindset, with attention to coverage, diversity, quality, and data leakage.
  • The ability to anticipate reward exploitation and design robust rewards, verifiers, and evaluation mechanisms.
  • Practical experience working with GPUs and a realistic understanding of their capabilities and limitations.
  • Strong judgment around when model training is the right solution-and when a simpler approach is preferable.
  • Ability to collaborate effectively within a remote, distributed, and highly autonomous team.
  • Experience with post-training techniques such as fine-tuning, reward design, or reinforcement learning, including approaches such as GRPO, is highly desirable.
  • Familiarity with RL and fine-tuning frameworks such as TRL, verl, OpenRLHF, or custom training loops is a plus.
  • Experience with technologies such as vLLM or SGLang for fast rollouts and FSDP for multi-GPU training is advantageous.
  • Experience training tool-using or multi-turn agents, as well as building execution sandboxes, verifiers, evaluation harnesses, or developer tooling, is valuable.
  • Familiarity with open-weight model families such as Qwen or Llama and techniques such as LoRA is a plus.
Benefits:
  • Opportunity to make a significant impact on a fast-growing developer platform and help shape its future.
  • Collaboration with a small, highly experienced team that values technical excellence, creativity, and ownership.
  • Competitive salary and equity package.
  • Health, dental, and vision benefits.
  • Flexible vacation policy.
  • Remote-friendly working environment with flexibility and autonomy.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
 Why Apply Through Jobgether? 
 
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
 
 
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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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