ZipRecruiter

Log In

1

Reinforcement Learning Engineer Jobs in Philadelphia, PA

Athenago

AI Engineering Intern

Wilmington, DE

Familiarity with concepts like reinforcement learning, prompt engineering for LLMs, or building multi-agent systems. * Experience with data preprocessing pipelines, and working with datasets relevant ...

previous pagenext page

Showing results 21-40

All JobsReinforcement Learning Engineer JobsReinforcement Learning Engineer Jobs in Philadelphia, PA

Reinforcement Learning Engineer information

See Philadelphia, PA salary details

$36.3K

$110.7K

$183K

How much do reinforcement learning engineer jobs pay per year?

As of Aug 22, 2026, the average yearly pay for reinforcement learning engineer in Philadelphia, PA is $110,735.00, according to ZipRecruiter salary data. Most workers in this role earn between $79,300.00 and $144,800.00 per year, depending on experience, location, and employer.

What is a reinforcement learning engineer?

Reinforcement Learning Engineers are specialized professionals who design, develop, and implement algorithms based on reinforcement learning, a type of machine learning where agents learn to make decisions by receiving rewards or penalties. They work on building models that enable machines to learn optimal actions through trial and error in complex environments. Their responsibilities often include developing RL architectures, tuning hyperparameters, running simulations, and applying RL methods to real-world problems like robotics, gaming, or recommendation systems. RL Engineers typically have strong backgrounds in computer science, mathematics, and deep learning, along with experience in programming languages like Python and frameworks such as TensorFlow or PyTorch.

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

One of the main challenges Reinforcement Learning (RL) Engineers face is bridging the gap between simulation and real-world deployment. Models that perform well in controlled environments may struggle with unpredictable data, safety constraints, or limited feedback in production. Additionally, RL algorithms often require significant computational resources and careful tuning to avoid instability. Collaboration with domain experts and software engineers is essential to address these issues and ensure successful integration of RL solutions into existing systems.

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

To thrive as a Reinforcement Learning Engineer, you need a strong background in machine learning, mathematics (especially probability and statistics), and programming languages like Python, often supported by a relevant degree in computer science or engineering. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), RL libraries (like OpenAI Gym), and cloud computing platforms is typically required. Problem-solving skills, creativity, and effective collaboration help set outstanding engineers apart in this field. These competencies enable the design and deployment of advanced RL solutions that address real-world challenges and drive innovation.

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

AspectReinforcement Learning EngineerMachine Learning Engineer
CredentialsBachelor's/Master's in CS, AI, or related; experience with RL frameworksBachelor's/Master's in CS, Data Science, or related; experience with ML algorithms
Work EnvironmentResearch labs, AI startups, tech companies focusing on RL applicationsTech companies, data-driven firms, AI departments across industries
Industry UsageSpecialized in RL projects like robotics, game AI, autonomous systemsBroader applications including predictive modeling, NLP, computer vision

Reinforcement Learning Engineers focus on developing algorithms that learn through interactions with environments, often in robotics or gaming. Machine Learning Engineers work on a wider range of models and applications. While both roles require strong programming and math skills, RL Engineers specialize in sequential decision-making, whereas ML Engineers handle diverse data-driven tasks across industries.

What are popular job titles related to Reinforcement Learning Engineer jobs in Philadelphia, PA?

For Reinforcement Learning Engineer jobs in Philadelphia, PA, the most frequently searched job titles are:

What job categories do people searching Reinforcement Learning Engineer jobs in Philadelphia, PA look for?

The top searched job categories for Reinforcement Learning Engineer jobs in Philadelphia, PA are:

What cities near Philadelphia, PA are hiring for Reinforcement Learning Engineer jobs?

Cities near Philadelphia, PA with the most Reinforcement Learning Engineer job openings:

Reinforcement Learning Engineer jobs near you
Infographic showing various Reinforcement Learning Engineer job openings in Philadelphia, PA as of August 2026, with employment types broken down into 92% Full Time, and 8% Contract. Highlights an 90% In-person, and 10% Remote job distribution, with an average salary of $110,735 per year, or $53.2 per hour.

Robotics Software Engineer - Controls

The O'Connor Group TOG Inc.

Philadelphia, PA

$90K - $140K/yr

Full-time

Re-posted 28 days ago

Job description

Description

 We are partnering with Ghost Robotics and have announced the search for a Robotics Software Engineer (Controls)

Description  
Ghost Robotics is a robotics company building autonomous systems that operate in complex, dynamic environments. Our perception stack enables our robots to understand, localize, and navigate the world in real time, and we place a strong emphasis on robustness, performance, and maintainable engineering.
We are seeking a Senior Controls Engineer to design, implement, and deploy cutting-edge control algorithms for dynamic legged robotic systems operating in complex real-world environments. You will work across dynamics, optimization, state estimation, and real-time software owning both algorithmic innovation and system-level performance on hardware.  
This role is ideal for engineers who thrive on high-velocity problem solving, deep technical ownership, and hands-on testing and validation. 

Responsibilities 

Design, implement, and validate advanced control architectures (e.g., model-based, optimization-based, learning-augmented controllers) for agility and robustness. 

Develop, maintain, and validate state estimation and sensor fusion pipelines (IMU, joint encoders, contact/force sensing). 

Lead gait generation, footstep planning, contact scheduling, and disturbance recovery tuning. 

Perform rigorous offline and real-time testing in simulation and hardware environments. 

Debug and analyze system performance using logs, visualization tools, hardware experiments, and fleet data. 

Build automated diagnostics, analysis scripts, and tools to improve robot reliability and field performance. 

Collaborate closely with mechanical, perception, embedded, and systems teams to ensure end-to-end performance and robustness. 

Write clean, maintainable, real-time-safe code in C++ and Python. 

Mentor junior engineers and contribute to long-term architectural decisions. 


 

 

Requirements

Requirements 

Required Qualifications 

Strong foundations in control theory (linear, nonlinear, optimal control) with experience in legged locomotion or other dynamic systems. 

Experience with multi-body dynamics, modeling, and simulation (e.g., MuJoCo, Gazebo, Isaac Sim, PyBullet). 

Hands-on experience deploying algorithms on physical robotic systems and debugging complex hardware/software interactions. 

Proficiency in modern C++ (C++17/20) and Python for development and tooling. 

Experience with Unix/Linux environments and software engineering best practices (version control, CI/CD). 

Masters/PhD in Robotics, Mechanical, Electrical, Aerospace Engineering or equivalent work experience. 

Preferred Qualifications 

Experience with legged or humanoid robots and real-world locomotion challenges. 

Background in whole-body control frameworks (operational space control, MPC, etc.). 

Familiarity with state estimation methodologies (EKF, factor graphs, UKF). 

Experience architecting data analysis pipelines and automated diagnostic systems. 

Publications or significant open-source contributions in robotics, controls, or estimation. 

Experience with ROS 2 and real-time middleware. 

A combination of classical control and reinforcement learning applied to robotic systems. 

Demonstrated ability to lead technical efforts and mentor junior engineers. 

Apply