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Reinforcement Learning Engineer information

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$38.3K

$116.9K

$193.2K

How much do reinforcement learning engineer jobs pay per year?

As of Aug 2, 2026, the average yearly pay for reinforcement learning engineer in Philadelphia, PA is $116,917.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,800.00 and $152,900.00 per year, depending on experience, location, and employer.

What are Reinforcement Learning Engineers?

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 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 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 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 July 2026, with employment types broken down into 72% Full Time, and 28% Contract. Highlights an 67% In-person, and 33% Remote job distribution, with an average salary of $116,917 per year, or $56.2 per hour.

Sr Applied ML Engineer - Physics-Driven Systems & Optimization

Keysight Technologies, Inc.

Harrisonville, NJ • On-site

$103K - $142K/yr

Other

Re-posted 4 days ago

Keysight Technologies rating

8.1

Company rating: 8.1 out of 10

Based on 20 frontline employees who took The Breakroom Quiz

49th of 156 rated electronics manufacturers

Job description

Overview

Keysightis on the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~15,000 employees create world-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries. Learn moreabout what we do. 

Our award-winningculture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions.We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers.

About Keysight AI Labs

Keysight’s AI Labs is a global R&D group pioneering the integration of machine learning, generative AI into Keysight’s test, measurement, and design solutions. Our mission is to transform how engineers design, simulate, and validate advanced systems- from 6G and semiconductors to quantum and automotive - by embedding AI throughout our workflows.

About the AI Team 

Join Keysight's central AI Hub in the heart of Barcelona. We are expanding our newly formed AI Team. As part of this growing team, you will join a vibrant, cross-functional environment that brings together experts in ML engineering, data science, physics-informed modeling, and software development. You’ll work closely with domain experts across RF, EM, circuit design, and test & measurement to accelerate scientific innovation through AI.

About the Role

As a Senior Applied Machine Learning Engineer, you will design, implement, and deploy state-of-the-art ML architectures that merge physics insights, numerical optimization, and modern AI techniques.


You’ll contribute to building scalable and explainable ML systems, from geometry-aware GNNs and Transformers to reinforcement learning and generative models, that drive design automation, anomaly detection, and optimization in Keysight’s next-generation platforms.


Responsibilities
  • Partner with Keysight experts in RF, EM, circuit, and measurement domains to translate physical constraints and design workflows into ML-ready formulations.
  • Design and implement advanced ML architectures:
    • Graph Neural Networks (GNNs) for geometry/topology-aware modeling
    • Transformers for sequential and multimodal data
    • Vision Models (CNNs, ViTs) for field- or spectrogram-based detection
    • Generative Models (GANs, Diffusion) for data augmentation and design candidate generation
  • Apply advanced optimization and control methods:
    • Bayesian, gradient-based, and gradient-free optimization
    • Reinforcement Learning (PPO, DDPG, SAC) for continuous tuning and control tasks
  • Develop scalable training and inference pipelines (multi-GPU, HPC, AWS) ensuring efficiency and reliability.
  • Write production-ready code in Python, C++, and CUDA, integrating with CI/CD pipelines and performance profiling tools.
  • Benchmark ML and RL models against physics simulators and measurement datasets for robustness and reproducibility.
  • Collaborate with product teams to embed AI/ML-based optimization and generative modules into Keysight software.
  • Stay current with the latest ML, RL, and generative AI research; evaluate and prototype promising new techniques.

Qualifications

Required Qualifications

  • Master’s or PhD in Applied Mathematics, Scientific Computing, Computer Science, Electrical Engineering, or related field

  • 5+ years of experience applying scientific computing and optimization to real-world problems (e.g., RF, EM, or measurement systems)

  • Strong hands-on experience with modern ML architectures (GNNs, Transformers, Vision Models, Neural Operators)

  • Practical experience with generative models (GANs, VAEs, Diffusion)

  • Background in Bayesian and numerical optimization and hyperparameter tuning

  • Applied experience with reinforcement learning (PPO, DDPG, SAC)

  • Proficiency in Python, C++, CUDA, and GPU performance optimization

  • Experience with multi-GPU/distributed training in HPC or cloud (Slurm, MPI, AWS)

  • Solid software-engineering discipline (testing, CI/CD, modular design)

  • Excellent communication and collaboration skills across cross-functional teams

Desired Qualifications

  • Experience applying ML/RL/generative models to parameter tuning, data augmentation, or design exploration

  • Familiarity with Keysight simulation tools (ADS, RFPro, EMPro, Signal Studio, RaySim)

  • Publications or patents in scientific ML, generative modeling, RL, or optimization

  • Experience deploying ML/RL systems in production or embedded workflows

Careers Privacy Statement***Keysight is an Equal Opportunity Employer.*** 

Qualifications:

Required Qualifications

  • Master’s or PhD in Applied Mathematics, Scientific Computing, Computer Science, Electrical Engineering, or related field

  • 5+ years of experience applying scientific computing and optimization to real-world problems (e.g., RF, EM, or measurement systems)

  • Strong hands-on experience with modern ML architectures (GNNs, Transformers, Vision Models, Neural Operators)

  • Practical experience with generative models (GANs, VAEs, Diffusion)

  • Background in Bayesian and numerical optimization and hyperparameter tuning

  • Applied experience with reinforcement learning (PPO, DDPG, SAC)

  • Proficiency in Python, C++, CUDA, and GPU performance optimization

  • Experience with multi-GPU/distributed training in HPC or cloud (Slurm, MPI, AWS)

  • Solid software-engineering discipline (testing, CI/CD, modular design)

  • Excellent communication and collaboration skills across cross-functional teams

Desired Qualifications

  • Experience applying ML/RL/generative models to parameter tuning, data augmentation, or design exploration

  • Familiarity with Keysight simulation tools (ADS, RFPro, EMPro, Signal Studio, RaySim)

  • Publications or patents in scientific ML, generative modeling, RL, or optimization

  • Experience deploying ML/RL systems in production or embedded workflows

Careers Privacy Statement***Keysight is an Equal Opportunity Employer.*** 

Education:UNAVAILABLEEmployment Type: UNAVAILABLE

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