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Reinforcement Learning Engineer Jobs in Philadelphia, PA

Aumni

Applied AI/ML - Vice President

Wilmington, DE · On-site

$120 - $160/hr

... learning, reinforcement learning), and generative model architectures. * Expert in Large Language models (OpenAI, Anthropic, Mistral, etc) including fine‑tuning models, prompt engineering ...

MSD

Senior Specialist, Data Science

West Point, PA · On-site

$129K - $203K/yr

This role will combine advanced machine learning, foundation model engineering, and domain ... Prior experience designing agentic systems, human-in-the-loop workflows, or using reinforcement ...

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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 23, 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.

Robotic Software Engineer - (Senior, Staff, Lead) Manipulation

Ghost Robotics

Philadelphia, PA • On-site

Full-time

Re-posted 13 days ago

Job description

We're a robotics company building autonomous systems that operate in complex, dynamic environments. We're hiring a Manipulation Engineer to design, implement, and deploy manipulation algorithms for arms mounted on dynamic legged robots operating in the real world. You'll build task and motion planning, grasping, and control pipelines in real-time software, and own their performance all the way down to hardware. This role suits engineers who thrive on high-velocity problem solving, deep technical ownership, and hands-on testing and validation.
How leveling works This is one role, open at the Senior, Staff, or Lead level. The responsibilities below reflect the core of the role at every level. You don't need to decide which level fits you before applying. The selected candidate will be placed at the level commensurate with the skills, scope, and experience they demonstrate through the interview process, and the offer will align with that level. Senior and Staff are individual-contributor positions. Lead carries the same technical scope plus people-leadership responsibilities, for candidates who want them.
What you'll do (all levels)
  • Architect end-to-end manipulation frameworks, from high-level task planning down to perception and low-level joint control
  • Develop trajectory planning, obstacle avoidance, and real-time motion generation
  • Implement grasping strategies using proprioception and vision, for rigid and deformable objects
  • Use physics-based simulators to develop and validate manipulation policies with high-fidelity transfer to hardware
  • Write clean, maintainable C++ and Python, and debug system performance across simulation, hardware experiments, and fleet data
  • Collaborate with mechanical, perception, embedded, and systems teams to ensure end-to-end performance and robustness
  • Contribute to long-term architectural decisions

Senior Manipulation Engineer
  • Typically reached with 5+ years of relevant experience, or 3+ years following a PhD
  • Owns a defined technical area of the manipulation stack and delivers independently end to end
  • Mentors less-experienced engineers
  • Track record of shipping manipulation software to real hardware

Staff Manipulation Engineer
  • Typically reached with 8+ years of relevant experience
  • Drives technical direction and architecture across the team's area
  • Sets engineering standards and multiplies the output of others
  • Demonstrated influence beyond their own deliverables, across teams or an entire manipulation stack

Lead Manipulation Engineer
  • Typically reached with 10+ years of relevant experience, including experience guiding engineers
  • Staff-level technical scope plus people-leadership responsibilities: direct reports, hiring, performance, and team planning
  • Demonstrated ability to grow engineers and run a healthy, productive team

Requirements
Core qualifications (all levels)
  • A strong background in robotic manipulation, with hands-on experience on multi-DOF arms in real systems. We weigh what you've built and shipped over where your degree is from. An advanced degree in Robotics, Mechanical, Electrical, Aerospace Engineering, CS, or a related field is one path in. Equivalent industry experience is another.
  • Strong foundations in motion planning, control theory, optimization, and dynamical systems
  • Deep understanding of coordinate transformations, forward/inverse kinematics, Jacobians, and robot dynamics
  • Experience with motion planning tools (e.g., MoveIt, OMPL, or custom optimization-based planners)
  • Experience with multi-body dynamics, modeling, and simulation (e.g., MuJoCo, Gazebo, Isaac, Bullet/PyBullet)
  • Experience with ROS 2 and real-time middleware
  • Proficiency in modern C++ (C++17/20) and Python
  • Experience with Unix/Linux environments and software engineering best practices (version control, CI/CD)
  • Master's or PhD in Robotics, Mechanical Engineering, Electrical Engineering, Aerospace Engineering, Computer Science, or a related field.
  • 3+ years of hands-on experience in robotic manipulation, specifically with multi-DOF (Degree of Freedom) arms.
  • Strong foundations in motion planning, control theory and optimization, along with experience in dynamical systems.
  • Deep understanding of coordinate transformations, forward/inverse kinematics, Jacobians, and robot dynamics.
  • Experience with motion planning software tools (e.g., MoveIt, OMPL, or custom optimization-based planners).
  • Experience with multi-body dynamics, modeling, and simulation (e.g., MuJoCo, Gazebo, Isaac, Bullet/PyBullet).
  • Experience with ROS 2 and real-time middleware.
  • 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).

Preferred qualifications
  • Experience with legged or humanoid robots
  • Experience applying Reinforcement Learning, Vision Language Models, or Vision-Language-Action models to robotic decision-making and task planning
  • Familiarity with 3D perception, point cloud processing, and vision-based feedback in manipulation loops
  • Background in whole-body control frameworks (operational space control, MPC, and similar)
  • Experience with force-feedback control or hand-eye calibration
  • Publications or significant open-source contributions in robotics or machine learning
  • Demonstrated ability to lead technical efforts and mentor engineers

What we offer
  • Work with state-of-the-art robots in challenging real-world environments
  • Hands-on hardware experience alongside strong software and simulation infrastructure
  • A collaborative, technically rigorous culture with emphasis on learning and ownership
  • Access to real robots, real data, and real impact
  • The anticipated starting base salary for this position is $140,000 to $185,000 per year, placed by level as described above

Location
Philadelphia, PA (no remote candidates considered at this time). Opportunities to work from home with prior approval.
Travel
No travel required.
Compensation
Competitive base, full benefits and highly motivating equity incentive package. Flexible time-off policy. Focus on output and ability to work with a stellar team of interdisciplinary functions.
Background Check
Clear standard background checks, pre-hire, post hire and anytime during employment as required.
Residency Requirements
Permanent Residency Required.
Physical Requirements
  • Prolonged periods of standing, sitting at a desk and working on a computer.
  • Must be able to lift 10 pounds. Assistive equipment available

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