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$27.2K

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How much do reinforcement learning jobs pay per year?

As of Aug 16, 2026, the average yearly pay for reinforcement learning in Philadelphia, PA is $55,763.00, according to ZipRecruiter salary data. Most workers in this role earn between $48,300.00 and $65,000.00 per year, depending on experience, location, and employer.

What does a reinforcement learning professional do?

A typical day for a Reinforcement Learning professional involves designing and implementing learning algorithms, running experiments, analyzing data, and iterating on models to improve performance. You might collaborate closely with data scientists, software engineers, and product managers to integrate your solutions into broader systems or products. Regular activities also include reading recent research literature and participating in team meetings to discuss progress and obstacles. This dynamic role often balances deep technical work with teamwork to drive innovative applications in areas such as robotics, recommendation systems, or autonomous systems.

What are the key skills and qualifications needed to thrive in the reinforcement learning position?

To thrive in a Reinforcement Learning role, you need a solid background in mathematics, statistics, machine learning, and programming (commonly with Python), typically supported by a relevant degree such as in computer science or engineering. Experience with frameworks like TensorFlow, PyTorch, OpenAI Gym, and familiarity with large-scale computing systems are highly valued. Strong problem-solving abilities, curiosity, and effective collaboration and communication skills help you excel in multidisciplinary research and project teams. These capabilities are crucial for designing, implementing, and refining complex algorithms that learn from interaction to solve real-world problems.

What is a reinforcement learning?

A Reinforcement Learning (RL) job involves designing, developing, and optimizing algorithms that enable machines to learn from interactions with their environment. RL professionals work on applications in robotics, finance, gaming, and autonomous systems, leveraging techniques like deep reinforcement learning and policy optimization. Responsibilities often include researching new models, implementing RL algorithms, and improving AI performance. Strong programming skills, knowledge of machine learning frameworks, and an understanding of mathematical concepts like probability and optimization are essential.

What are the most commonly searched types of Reinforcement Learning jobs in Philadelphia, PA?

The most popular types of Reinforcement Learning jobs in Philadelphia, PA are:

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

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Reinforcement Learning jobs near you
Infographic showing various Reinforcement Learning job openings in Philadelphia, PA as of August 2026, with employment types broken down into 63% Full Time, 28% Part Time, and 9% Contract. Highlights an 100% In-person job distribution, with an average salary of $58,877 per year, or $28.3 per hour.

Sr Applied ML Engineer - Physics-Driven Systems & Optimization

Keysight Technologies, Inc.

Harrisonville, NJ • On-site

$103K - $142K/yr

Full-time

Re-posted 18 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 157 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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