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Senior Reinforcement Learning Jobs (NOW HIRING)

JazzX AI

Senior Staff AI Engineer

Los Altos, CA · On-site

$123K - $169K/yr

The Senior Staff AI Engineer will lead the design and development of reinforcement learning systems, ensuring they are scalable, efficient, and integrated into the company's broader AGI platform.

JazzX AI

Senior Staff AI Engineer

Los Altos, CA · On-site

$123K - $169K/yr

The Senior Staff AI Engineer will lead the design and development of reinforcement learning systems, ensuring they are scalable, efficient, and integrated into the company's broader AGI platform.

Unitytech

Senior Machine Learning Engineer

Mountain View, CA · On-site +1

$123K - $169K/yr

We're looking for a Senior Machine Learning Engineer to lead the development of these foundational ... Lead high-impact initiatives, including hierarchical reinforcement learning for scalable behaviors ...

JazzX AI

Senior Staff AI Engineer

San Francisco, CA

$65 - $84/hr

About the Role We are seeking an experienced AI Engineer with deep expertise in Reinforcement Learning (RL) to join our team as a Senior Staff Architect. In this role, you will be responsible for ...

Roku

Senior Machine Learning Engineer, Search

San Jose, CA · On-site

$361K - $510K/yr

Our systems leverage modern ML and GenAI techniques, including LLMs, reinforcement learning, multi ... About the Role We are looking for a Senior Machine Learning Tech Lead to drive ranking and ...

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Senior Reinforcement Learning information

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How much do senior reinforcement learning jobs pay per year?

As of Aug 12, 2026, the average yearly pay for senior reinforcement learning in the United States is $80,287.00, according to ZipRecruiter salary data. Most workers in this role earn between $41,500.00 and $103,000.00 per year, depending on experience, location, and employer.

What are some common challenges faced by senior reinforcement learning professionals when deploying models in real-world environments?

Senior Reinforcement Learning professionals often encounter challenges such as ensuring model robustness when transferring algorithms from simulated to real-world environments, handling limited or noisy data, and managing the computational demands of training complex models. Additionally, safety and interpretability are critical, as real-world deployments can have significant impacts if models behave unpredictably. Close collaboration with domain experts and engineering teams is essential to address these challenges and ensure successful, scalable deployments.

What are the key skills and qualifications needed to thrive as a senior reinforcement learning engineer, and why are they important?

To thrive as a Senior Reinforcement Learning Engineer, you need deep expertise in machine learning, reinforcement learning algorithms, and programming languages such as Python, often supported by an advanced degree in computer science or a related field. Familiarity with frameworks like TensorFlow, PyTorch, and RL-specific libraries, as well as experience with high-performance computing and cloud platforms, is typically required. Strong problem-solving abilities, collaboration, and communication skills help distinguish top performers in this role. These skills ensure the development of efficient, robust RL models and effective teamwork on complex AI projects.

What is the difference between Senior Reinforcement Learning vs Data Scientist?

AspectSenior Reinforcement LearningData Scientist
Required CredentialsAdvanced degrees in CS, ML, or related fields; experience with RL frameworksDegree in CS, Statistics, or related; strong analytical skills
Work EnvironmentResearch labs, AI teams, tech companies focusing on ML projectsBusiness analytics, data analysis, and modeling in various industries
Employer & Industry UsageTech firms, AI startups, research institutionsFinance, healthcare, marketing, tech, and more

While both roles require strong analytical skills and technical knowledge, Senior Reinforcement Learning specialists focus on developing RL algorithms and models, often in AI research settings. Data Scientists analyze data to inform business decisions across industries. The roles overlap in data handling and programming but differ in their core focus and application areas.

What does a senior reinforcement learning engineer do?

A Senior Reinforcement Learning Engineer designs, develops, and implements advanced machine learning algorithms that enable systems to learn optimal behaviors through trial and error. They work on complex problems such as robotics, game AI, recommendation systems, and automated decision-making. In addition to coding and model development, they often lead research initiatives, collaborate with cross-functional teams, and mentor junior engineers. Their role requires deep knowledge of reinforcement learning theory, practical experience with machine learning frameworks, and strong programming skills.
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Senior Reinforcement Learning jobs near you
Infographic showing various Senior Reinforcement Learning job openings in the United States 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 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $80,287 per year, or $38.6 per hour.

Senior Applied Scientist, Reinforcement Learning

Hippocratic AI

Menlo Park, CA • On-site

Full-time

Posted 14 days ago

Job description

About Us
Hippocratic AI is the leading generative AI company in healthcare. We have the only system that can have safe, autonomous, clinical conversations with patients. We have trained our own LLMs as part of our Polaris constellation, resulting in a system with over 99.9% accuracy.
Why Join Our Team
Reinvent healthcare with AI that puts safety first. We're building the world's first healthcare‑only, safety‑focused LLM - a breakthrough platform designed to transform patient outcomes at a global scale. This is category creation.
Work with the people shaping the future. Hippocratic AI was co‑founded by CEO Munjal Shah and a team of physicians, hospital leaders, AI pioneers, and researchers from institutions like El Camino Health, Johns Hopkins, Washington University in St. Louis, Stanford, Google, Meta, Microsoft, and NVIDIA.
Backed by the world's leading healthcare and AI investors. We recently raised a $126M Series C at a $3.5B valuation, led by Avenir Growth, bringing total funding to $404M with participation from CapitalG, General Catalyst, a16z, Kleiner Perkins, Premji Invest, UHS, Cincinnati Children's, WellSpan Health, John Doerr, Rick Klausner, and others.
Build alongside the best in healthcare and AI. Join experts who've spent their careers improving care, advancing science, and building world‑changing technologies - ensuring our platform is powerful, trusted, and truly transformative.
Location Requirement
We believe the best ideas happen together. To support fast collaboration and a strong team culture, this role is expected to be in our Palo Alto office five days a week, unless otherwise specified.
About the Role
LLM post-training is where raw capability becomes reliable, safe behavior - and in healthcare, the stakes are as high as they get. You'll own the Reinforcement Learning (RL) and On-Policy Distillation (OPD) post-training pipeline end to end, to improve our models' clinical reasoning, safety, and alignment. Your models will be deployed to interact with millions of patients across diverse clinical use cases.
What You'll Do
  • Design RL and OPD post-training methods (RLHF, RLVR, OPD, etc.)
  • Build and evaluate reward models, verifiers, and LLM-as-judge pipelines
  • Develop conversational AI environments and simulations for healthcare RL training with synthetic data
  • Automate post-training loops with agents (auto-research)
  • Run rigorous experiments to understand what drives post-training gains
  • Collaborate with research, engineering, and clinical teams
What You Bring
  • MS or PhD in CS or relevant field
  • 3+ years or experience in NLP, LLM training, or RL
  • 1+ years experience in RL for LLM post-training
  • Experience with large-scale (50B+ parameter and multi-node) LLM training
  • Strong Python and PyTorch coding skills
  • Experience with RLHF, RLVR, LLM-as-judge or similar methods for LLM post-training

Please be aware of recruitment scams impersonating Hippocratic AI. All recruiting communication will come from @hippocraticai.com email addresses. We will never request payment or sensitive personal information during the hiring process.
Please be aware of recruitment scams impersonating Hippocratic AI. All recruiting communication will come from @hippocraticai.com email addresses. We will never request payment or sensitive personal information during the hiring process.

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