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Reinforcement Learning Engineer Jobs in Austin, TX

Senior Machine Learning Engineer, DevOps/SRE

Austin, TX · On-site

$128K - $165K/yr

We use Machine Learning, Reinforcement Learning, AI, Control and Optimization Systems, and Auction ... About the role We are seeking a talented and experienced Senior Software Engineer, MLOps/DevOps, to ...

By hiring incredible engineers, we drive precision. And through our collaborative process, we build ... Experience with Deep neural networks and reinforcement learning is a plus Solid math background and ...

By hiring incredible engineers, we drive precision. And through our collaborative process, we build ... Experience with Deep neural networks and reinforcement learning is a plus Solid math background and ...

Senior AI Model Fine-Tuning Engineer

Austin, TX · On-site

$103K - $142K/yr

... Reinforcement Learning from Human Feedback (RLHF) and other behavioral fine-tuning methods to ... RLHF, prompt engineering, and zero-shot learning. • Experience with popular transformer ...

Proficiency in Python and experience with asynchronous programming (AsyncIO) for ML serving frameworks. Experience with Ray for distributed compute and managing Reinforcement Learning (RL) workloads.

Showing results 41-60

Reinforcement Learning Engineer information

See Austin, TX salary details

$37.7K

$114.8K

$189.8K

How much do reinforcement learning engineer jobs pay per year?

As of Aug 8, 2026, the average yearly pay for reinforcement learning engineer in Austin, TX is $114,846.00, according to ZipRecruiter salary data. Most workers in this role earn between $82,300.00 and $150,200.00 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 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 Austin, TX? For Reinforcement Learning Engineer jobs in Austin, TX, the most frequently searched job titles are:
What cities near Austin, TX are hiring for Reinforcement Learning Engineer jobs? Cities near Austin, TX with the most Reinforcement Learning Engineer job openings:
Infographic showing various Reinforcement Learning Engineer job openings in Austin, TX 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 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $114,846 per year, or $55.2 per hour.

AI/ML Engineer (GenAI), G&A Solutions Engineering (GSE)

Apple

Austin, TX • On-site

Full-time

Re-posted 9 days ago


Apple rating

8.0

Company rating: 8.0 out of 10

Based on 677 frontline employees who took The Breakroom Quiz

7th of 30 rated technology retailers


Job description

Imagine what you could do here. At Apple, great ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. The G&A Solutions Engineering organization at Apple primarily focuses on creative ways to engineer business solutions to meet growing needs of Apple's Finance, iTunes, Sales, Retail, and Services organizations. At core, our portfolio comprises of engineered custom solutions to process high volume transactions from Apple Pay, iTunes, Ads, App Store, iPhone Activations to Sales from Retail, Online, and Resellers. These solutions are based on cutting edge enterprise technologies ranging from Distributed Systems, Microservices, Java, Spring/Boot, Oracle, MongoDB, AWS services to AI/ML, Generative AI, and Blockchain. Accurately processing such high volume transactions is our core strength.
Description
The iRecon Payments team is seeking a highly motivated AI/ML Engineer to help build our next-generation payments platform. In this role, you will blend classical ML with cutting-edge Generative and Agentic AI to transform how we process transactional data at scale.
Minimum Qualifications
2+ years of experience building machine learning solutions using supervised/unsupervised learning, classification, recommendation systems, and clustering algorithms
In-depth knowledge of transformer architecture, LLMs, and Agentic AI concepts
Hands-on experience fine-tuning Large Language Models (LLMs) using PEFT/LoRA for domain-specific tasks
Proven experience building and extending RAG, MCP (Model Context Protocol), or multi-agent frameworks (e.g., LangChain, LlamaIndex, AutoGen)
Bachelor's degree in Computer Science, AI, Machine Learning, or relevant work experience
Preferred Qualifications
3+ years deploying production-grade AI/ML solutions in the FinTech domain
2+ years building conversational assistants or autonomous agents using advanced techniques (LangGraph, CrewAI, A2A, CoT, ReAct, Reflection)
Experience with the full LLM lifecycle including pre-training, SFT, and Reinforcement Learning techniques (RLHF, PPO, GRPO)
Demonstrated ability to quickly master emerging AI tools and integrate them into legacy stacks
Strong written and verbal communication skills with the ability to explain complex AI concepts to business stakeholders

What Apple employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom


Apple logo

About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976