Reinforcement Learning

Reinforcement Learning Developer Jobs

17 jobs found

2d ago

Machine Learning Engineer - LLMs, Agent Systems, and Simulation Tooling, Siri Core Modeling

Apple Cupertino, CA

Cupertino, CA

2d ago

$150k - $278k/yr Full time Agentic Ai Ai Agent Ai Agent Platform

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Job Description

Join Apple’s Siri organization in Cupertino, CA (onsite) to help define how next-generation intelligent agents reason, plan, and interact through natural voice and multimodal experiences. In this senior Machine Learning Engineering role, you will build scalable LLM reasoning systems and the simulation and evaluation tooling that supports agentic voice capabilities across client and backend environments.

What you’ll do

Design and implement scalable simulation and evaluation frameworks for LLM-based agents.
Develop infrastructure for training and experimentation with agentic behaviors, including how agents plan, adapt, and evaluate actions in realistic environments.
Build scalable evaluation pipelines and create reliable tooling to accelerate iteration for prototyping and deployment.
Integrate agent reasoning capabilities across client-side and backend environments.
Collaborate with ML researchers and applied researchers to translate novel reasoning approaches into production-grade systems.
Run experiments in simulated environments to advance robust, trustworthy agent behavior.

What you bring

Experience deploying LLM models in research or production contexts.
Knowledge of adaptive feedback loops, reinforcement learning, or interactive agent design.
Familiarity with client-backend integration for AI-driven applications.
Strong Python skills (preferred) and proficiency in at least one other programming language.
Proven ML engineering background, including system design, training pipelines, and deployment workflows.
Deep understanding of agent-based simulation, agentic RAG systems, and LLM evaluation methodologies.
Ability to balance long-term platform vision with pragmatic short-term delivery in fast-paced environments.
Minimum education/experience: Bachelor’s degree in Computer Science, Machine Learning, or a related quantitative field, plus 3+ years of relevant industry experience.
Advanced degree preferred: MS or PhD in Computer Science, Machine Learning, or a related field.

Technologies you’ll work with

Python
Reinforcement learning
Agentic RAG systems
LLM evaluation methodologies

Pay & benefits

Base pay range: USD 150,400 - 277,600 per year, determined based on skills, qualifications, experience, and location.

Comprehensive medical and dental coverage
Retirement benefits
A range of discounted products and free services
Reimbursement for certain educational expenses, including tuition
Opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs
Eligible for discretionary restricted stock unit awards
Can purchase Apple stock at a discount if voluntarily participating in the Employee Stock Purchase Plan
Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation

Note: Apple benefit, compensation, and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

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3d ago

Machine Learning Engineer - E-Commerce Recommendation Live

TikTok Seattle, WA

Seattle, WA

3d ago

$207k - $368k/yr Full time Data Pipeline Generative Ai Engineer Generative Recommendations

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Job Description

Join TikTok in Seattle to build the end-to-end recommendation stack powering TikTok Shop LIVE.

Responsibilities

Scale Large Recommendation Models (LRM): Push scaling for a unified ranking foundation model, including model capacity, sparse MoE, ultra-long user sequences, and pre-training with teacher-student distillation.
Convert offline scaling gains into online wins under strict latency and compute budgets using dynamic compute allocation and on-device real-time inference.
Generative recommendation on multimodal Semantic IDs: Design semantic IDs that fuse visual, speech, text, collaborative, and product signals (including what is being showcased, pinned, or auctioned in a live room).
Power generative retrieval with near-real-time SID serving.
LLM4Rec and MLLM cross-domain modeling: Use multimodal LLMs to represent live rooms, short videos, and products in one semantic space.
Inject world knowledge to transfer user interests across content, search, and shopping, and address cold start for new creators, new products, and new markets.
Reward models and post-training for recommenders: Build reward models for conversion, long-term value, and user experience; align recommenders with SFT and DPO/GRPO-style RL post-training.
Apply generator-evaluator decoding and listwise objectives for training and optimization.
Agent x Recommendation: Build LLM agents to auto-tune multi-objective value weights and traffic mechanisms, design and read experiments, and diagnose online cases.
Move from hand-tuned heuristics toward agentic optimization and multi-step RL.
Long-term value and ecosystem: Model LTV, uplift, and multi-touch attribution; design value and traffic mechanisms for live auctions, brands, and emerging creators.
Balance short-term GMV with user experience and a healthy, growing creator ecosystem.
Own it end to end: Drive work from problem framing and data pipelines through large-scale training, online serving, A/B testing, and launch; partner with engineering, product, and data science.

Requirements

Education: Bachelor’s degree or above in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related field.
Experience: 3+ years in recommendation, search, advertising, or other large-scale applied machine learning with a record of shipped models that improved online metrics.
ML depth: Strong machine learning fundamentals and hands-on experience in at least one area such as retrieval and ranking, sequential user modeling, multi-task learning, generative recommendation, or LLM/multimodal modeling.
Programming + frameworks: Proficiency in Python and/or C++, plus solid experience with PyTorch or TensorFlow, including distributed training and model optimization.
Execution: Ability to turn ambiguous business problems into modeling solutions and own iteration from offline experiments to online A/B results.

Technologies

Python
C++
PyTorch
TensorFlow

Benefits

Day one access to medical, dental, and vision insurance
401(k) savings plan with company match
Paid parental leave
Short-term and long-term disability coverage
Life insurance
Wellbeing benefits
10 paid holidays per year
10 paid sick days per year
17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure)
Additional discretionary bonuses/incentives (eligible may apply)
Restricted stock units (eligible may apply)

About the Team

Live commerce is described as the hardest recommendation problem at TikTok.
The Global E-commerce Recommendation Live Algorithm team owns the end-to-end recommendation stack for TikTok Shop LIVE, from retrieval and pre-ranking to ranking, mixed ranking, and long-term value modeling.
A unified stack serves seven core surfaces, covering public discovery and private follower relationships.
The team is rebuilding the stack around foundation models, including:
- Large recommendation models following scaling laws
- Generative retrieval on multimodal semantic IDs
- Multimodal LLMs connecting content and commerce
- Reward models and RL post-training aligned to long-term value
- AI agents that tune, debug, and evolve the system
What the team ships directly drives GMV, buyer growth, and creator success for TikTok Shop in the US and globally.

Location

Seattle, WA (onsite)

Compensation

USD 207,480 - 368,220 per year
Base salary range in the selected city: $207480 - $368220 annually
Employees may be eligible for additional discretionary bonuses/incentives and restricted stock units
Benefits may vary depending on the nature of employment and the country work location

Job Details

Job code: A254886
For Pay Transparency

Preferred Qualifications

Experience in e-commerce or live-streaming recommendation, ads ranking, or other real-time conversion-driven systems
Hands-on experience with large recommendation models or generative recommendation, including scaling laws, MoE, long-sequence modeling, pre-training, or distillation
Experience in LLM/MLLM post-training (SFT, reward modeling, RLHF, DPO, GRPO, or other RL-based alignment), ideally for recommendation or ranking
Experience building LLM agents (tool use, planning, automated experimentation, or parameter tuning) or applying reinforcement learning to multi-step decision-making like traffic allocation
Experience with cross-domain transfer, LTV/long-term value modeling, uplift modeling and causal inference, or multi-objective optimization
Experience with large-scale training and inference efficiency (mixed precision, model parallelism, GPU serving optimization)
Publications at top venues such as NeurIPS, ICML, ICLR, KDD, SIGIR, WWW, RecSys, ACL, or CVPR, or strong results in major ML competitions

Accommodation

TikTok provides reasonable accommodations in recruitment for candidates with disabilities, pregnancy, sincerely held religious beliefs, or other protected reasons.
Request assistance here: https://tinyurl.com/RA-request

About TikTok

TikTok is described as the leading destination for short-form mobile video.
Global headquarters: Los Angeles and Singapore.
Additional offices: New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.

Los Angeles County (Unincorporated) Fair Chance

Qualified applicants with arrest or conviction records will be considered in accordance with federal, state, and local laws, including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.
Criminal history may have a direct, adverse, and negative relationship on certain job duties and could result in withdrawal of a conditional offer.
Role may involve interacting and occasionally having unsupervised contact with internal/external clients or colleagues.
Role requires appropriately handling confidential information and access to IT systems.
Requires exercising sound judgment.

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4d ago

Senior Machine Learning Engineer - PhD Early Career

roblox San Mateo, CA

San Mateo, CA

4d ago

$197k - $243k/yr Full time Senior Artificial Intelligence Engineer

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Job Description

Roblox is seeking a Senior Machine Learning Engineer (PhD Early Career) to lead high-impact AI and machine learning work from early ideation through production deployment, with an emphasis on academic publication. This role applies advanced research to build scalable systems for platform engagement, safety, and growth of the content ecosystem, operating at world-scale concurrency.

Onsite Location

San Mateo, CA, United States (onsite). Office-based presence is scheduled for Tuesday, Wednesday, and Thursday, with optional attendance on Monday and Friday unless otherwise noted.

Compensation

Annual Salary Range: USD 196,750 - 243,290

The starting base pay shown for this position is dependent on job-related factors including professional background, training, work experience, location, business needs, and market demand. Full-time employees are also eligible for equity compensation and benefits as described on the company’s benefits page. The role includes equity compensation.

Core Responsibilities

Prototype and implement advanced AI and machine learning solutions that support core platform features and innovation at scale.
Develop and deploy cutting-edge models, including deep learning, generative AI, and multimodal architectures, to address complex, high-impact technical challenges.
Partner with applied researchers, engineers, and product teams to turn research ideas into production-ready systems.
Convert complex research into scalable and robust infrastructure that influences hundreds of millions of daily active users.
Start from user and product needs, architect ML solutions, and deliver improvements across platform engagement, safety, and content ecosystem growth.

Required Qualifications

Possess a PhD in computer science, engineering, or a related field, or be pursuing one, with a thesis aligned to Roblox’s research areas.
Demonstrate deep expertise and curiosity in a relevant technical area such as deep learning, computer vision, NLP, reinforcement learning, or distributed systems.
Proficiency in one or more programming languages, such as Python, C++, Go, or Java, along with experience building and optimizing large-scale systems.
A strong research record supported by multiple publications and presentations in rigorous peer-reviewed venues.

Technologies

Python
C++
Go
Java
Deep learning
Generative AI
Multimodal architectures
Computer vision
NLP
Reinforcement learning
Distributed systems

Benefits

Equity compensation
Benefits (as described on this page)

Application Process Information

As you apply, you can find more information about the process by signing up for SPEAK_. You will receive access to a practice assessment, comprehensive guides, FAQs, and modules designed to support preparation for the hiring process.

Work Authorization Note

For roles based in the United States only: the company may not be able to employ candidates for this role who have United States work authorization related to certain U.S. visa categories, or support future H-1B sponsorship at this time.

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5d ago

Machine Learning Engineer

Tesla Palo Alto, CA

Palo Alto, CA

5d ago

$124k - $420k/yr Full time Agentic Ai Ai Agent Ai Agent Platform

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Job Description

Machine learning engineer role within Tesla’s Digital Optimus Gaming Track, focused on building real-time agentic systems that can operate computers, learn tasks across web, apps, and games, and strengthen long-horizon reasoning, video understanding, and multi-agent collaboration. The work supports Tesla’s broader robotics and autonomy ecosystem.

Responsibilities

Design and own experiments that improve long-horizon agentic gameplay, emphasizing memory and real-time control
Lead and/or make key contributions to large-scale training runs, including data mixture, scaling, infrastructure, and algorithms
Enhance the post-training stack, including scalable environments, rewards, and behavior evaluations
Develop architectures for efficient context management, real-time reactivity, and intelligent planning, including systems capable of completing tasks over many days
Create rigorous benchmarks and evaluation techniques for agent performance in games and to measure transfer across different games, embodiments, and general computer use
Work with research, infrastructure, and production teams to integrate agent systems into Full Self-Driving (FSD) and physical Optimus
Iterate rapidly on prototypes, debug complex multimodal ML stacks, and contribute to new research directions

Requirements

Strong technical understanding across the full model development lifecycle, from pre-training through inference
Demonstrated contributions to at least one large multimodal training run
Experience applying planning and/or memory architectures for long-horizon or agentic systems
A track record of moving research from ideation to publication and/or production
Hands-on experience with VLMs, VLAs, RL, and large-scale transformers
Commitment to shipping models that solve difficult, high-impact problems

Technologies

VLMs
VLAs
RL
Large-scale transformers

What to Expect

Develop real-time agents that operate computers at superhuman performance levels
Build models that browse the web, use professional apps, and play AAA games
Contribute to reasoning and planning, video understanding, and multi-agent collaboration with direct relevance to Tesla’s robotics and autonomy ecosystem
Join a small group of engineers working on novel architectures, owning large training runs, and delivering research outcomes
Work at the frontier on systems that advance state-of-the-art capabilities

Compensation

USD 124,000 - 420,000 per year + cash and stock awards and benefits. Pay may vary based on job-related knowledge, skills, experience, and market location. The total compensation package may also include other elements depending on the position offered; details will be provided with an employment offer.

Benefits

Medical plans (options with $0 payroll deduction)
Family-building, fertility, adoption, and surrogacy benefits
Dental (including orthodontic coverage) and vision plans (options with $0 paycheck contribution)
Company Paid HSA Contribution when enrolled in the High-Deductible medical plan with HSA
Healthcare and Dependent Care Flexible Spending Accounts (FSA)
401(k) with employer match, Employee Stock Purchase Plans, and other financial benefits
Company paid Basic Life, AD&D
Short-term and long-term disability insurance with a 90 day waiting period
Employee Assistance Program
Sick and Vacation time (Flex time for salary positions, Accrued hours for Hourly positions) and Paid Holidays
Back-up childcare and parenting support resources
Voluntary benefits: critical illness, hospital indemnity, accident insurance, theft & legal services, and pet insurance
Weight Loss and Tobacco Cessation Programs
Tesla Babies program
Commuter benefits
Employee discounts and perks program

Location

Palo Alto, CA (onsite)

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5d ago

Senior / Staff Machine Learning Engineer, Applied AI

Lila Sciences San Francisco, CA

San Francisco, CA

5d ago

$180k - $298k/yr Full time Senior Ai Ml Artificial Intelligence

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Job Description

Lila Sciences is seeking a Senior / Staff Machine Learning Engineer, Applied AI to reduce the gap between frontier model capabilities and customer-specific scientific workflows. This role centers on post-training, evaluation loops, model debugging, and integration of model behavior into end-to-end product workflows.

Key Responsibilities

Use hands-on expertise to close the last-mile gap between Lila’s model capabilities and customer-specific scientific workflows.
Lead post-training execution using methods such as SFT and RL, including DPO, PPO/GRPO, to align model behavior with customer requirements and feedback.
Run evaluation loops that assess model quality, reliability, and fit for customer use cases.
Convert customer learnings, data signals, and evaluation outcomes into repeatable model improvement cycles.
Partner with AI researchers to translate model advances into reliable, usable capabilities.
Coordinate with Software to integrate model behavior into end-to-end product workflows.
Debug complex model failures using traces, evaluations, customer context, and scientific feedback.

Required Qualifications

Minimum 1-2 years of hands-on post-training experience, including SFT and RL methods such as DPO/PPO/GRPO, along with evaluation system design developed through repeated, real-world problem solving.
Strong software engineering skills in Python and modern ML frameworks such as PyTorch.
Proven ability to debug ambiguous, high-stakes model behavior quickly using data, traces, logs, and qualitative feedback.
Deep familiarity with large language models, multi-modal models, or agentic AI systems.
Clear communication skills for translating customer needs into technical approaches and explaining complex model behavior to technical and non-technical audiences.

Technologies

Python
PyTorch
SFT
RL
DPO
PPO
GRPO

Bonus Points

Experience adapting models for customer-facing or production workflows, preferably in scientific, technical, or data-intensive domains.
Experience with RL post-training such as RLHF, GRPO, or tool-augmented RL.
Experience building evaluation harnesses, model monitoring, or quality dashboards.
Experience training MoE architectures.
A history of mentoring engineers and serving as a go-to technical expert, rather than functioning primarily as a people manager or strategy owner.

Role Details

Location: San Francisco, CA (onsite)

Compensation: USD 180,000 - 298,000 per yearly

Minimum Experience: 1 years

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11d ago

Data Scientist/Machine Learning Engineer

The Aerospace Corporation El Segundo, CA

El Segundo, CA

11d ago

$70k - $105k/yr Full time Artificial Intelligence Data Analysis Data Pipeline

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Job Description

The Aerospace Corporation is seeking a Data Scientist / Machine Learning Engineer to develop and evaluate AI and ML capabilities that address aerospace and national security mission-relevant problems. This role focuses on advancing model performance and transitioning successful technologies to program customers, with work conducted 100% onsite in El Segundo, CA.

What you’ll do

Develop and evaluate advanced AI/ML algorithms for mission needs.
Design and implement reinforcement learning (RL) solutions for decision making, autonomy, planning, optimization, and control.
Apply deep learning, computer vision, foundation models, generative AI, and multimodal learning to mission-relevant problems.
Build data processing, model training, inference, and evaluation pipelines.
Design experiments to assess performance, robustness, generalization, and failure modes of AI/ML systems.
Support projects for primary customers from USSF, NRO, and IRAD through program office efforts.
Focus on developing and advancing AI/ML models and transitioning successful capabilities to customers.

Key requirements

Bachelor’s degree in Computer Science, Software Engineering, or other related STEM majors.
0-1 years or more of experience in AI/ML, data science, and related work.
Proven ability in AI/ML statistical analysis, data science, and modeling.
Demonstrated programming experience in Python, with proven ability in ML frameworks such as PyTorch and TensorFlow.
Working knowledge of Reinforcement Learning concepts and algorithms.
Willingness to travel approximately 10% of the time for customer meetings and technical collaboration.
Ability to obtain and maintain a security clearance, issued by the U.S. government. U.S. citizenship is required to obtain a security clearance.

Technologies

Python
PyTorch
TensorFlow
Reinforcement Learning (RL)
Computer vision
Foundation models
Generative AI
Multimodal learning

What you can expect

Comprehensive health care and wellness plans.
Paid holidays, sick time, and vacation.
Standard and alternate work schedules, including telework options.
401(k) Plan: total company-paid benefit of 8%, 10%, or 12% of eligible compensation based on years of service, with matching contributions; immediately eligible and vested upon hire.
Flexible spending accounts.
Variable pay program for exceptional contributions.
Relocation assistance.
Professional growth and development programs.
Education assistance programs.
An inclusive work environment built on teamwork, flexibility, and respect.

Skills that support success

Work well in a team environment and contribute to research initiatives and proposals.
Strong analytical, problem-solving, and critical thinking skills.
Ability to work effectively in a multidisciplinary environment.

How you can stand out

Advanced degree in Data Science, computer vision, or related fields.
Exposure developing, testing, and deploying AI/ML models.
Strong research record with publications in top-tier conferences such as CVPR and NeurIPS.
Experience deploying and optimizing ML models for cloud, HPC, and edge-computing environments.
Experience developing simulation-based AI/ML solutions.

Location: El Segundo, CA (onsite)
Salary: USD 70,000 - 105,000 per year
Education: Bachelor’s degree in Computer Science, Software Engineering, or related STEM major

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19d ago

Machine Learning Engineer /Development Team Lead

Ad hoc Research Chantilly, VA

Chantilly, VA

19d ago

$150k+/yr Full time Manager Application Security Artificial Intelligence

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Job Description

The Development Team Lead and Machine Learning Engineer will provide technical leadership and hands-on execution for AHR’s AI-driven defense and intelligence portfolio on the DarkStax™ platform. The role translates program objectives into delivery plans and supports teams from prototype through operational use, with a focus on building, integrating, and evaluating robust AI/ML capabilities.

Core Responsibilities

Lead day-to-day technical execution of a multidisciplinary development team, including work planning, technical decisions, code and model reviews, risk management, and delivery commitments.
Act as a Scrum Master and/or Product Owner by facilitating Agile ceremonies, maintaining and prioritizing the backlog, refining acceptance criteria, removing impediments, and updating stakeholders on delivery progress.
Design, train, evaluate, and improve machine learning models using reinforcement learning, neural networks, deep learning, and computer vision techniques.
Define experiment plans, performance measures, validation methods, and data requirements that connect model outcomes to operational needs.
Develop reproducible ML pipelines spanning data preparation, training, testing, versioning, deployment, and monitoring.
Integrate models into secure software applications, services, APIs, and mission systems while maintaining reliability, scalability, and traceability.
Evaluate model robustness, failure modes, and vulnerability to adversarial conditions for cyber, adversarial AI, or communications use cases.
Mentor engineers and data scientists, establish practical development standards, and improve team technical and Agile practices.
Collaborate with systems engineers and mission stakeholders to align technical designs, interfaces, demonstrations, and releases with program objectives.
Communicate technical approaches, tradeoffs, results, and risks clearly to engineering teams, program leadership, and government customers.

Required Qualifications

Bachelor’s degree in computer science, machine learning, artificial intelligence, electrical engineering, software engineering, applied mathematics, or a related technical field.
7 to 10 years of relevant experience in ML and software engineering, including experience leading technical teams or workstreams.
Experience serving as a Scrum Master and/or Product Owner on an Agile development team.
Hands-on experience developing and evaluating machine learning solutions using reinforcement learning, neural networks, deep learning, computer vision, or similar methods.
Experience in at least one domain: cyber, adversarial AI, or communication systems.
Software development experience sufficient to build, test, integrate, and maintain production-quality ML capabilities.
Ability to obtain a TS clearance.
Strong Python development skills and familiarity with common machine learning frameworks and scientific computing tools.
Knowledge of data preparation, model training, evaluation, experiment tracking, configuration management, and reproducible development practices.
Ability to design and review software interfaces, APIs, modular services, and integration approaches for ML-enabled systems.
Experience with MLOps, DevSecOps, automated model testing, model monitoring, or data and model governance.
Knowledge of software development, automated testing, CI/CD practices, containers, and deployment workflows.
Ability to translate mission and user needs into a prioritized backlog, measurable technical objectives, and incremental releases.
Ability to lead technical discussions, make sound tradeoffs, mentor team members, and resolve delivery or integration issues.
Clear written and verbal communication with technical teams, program leadership, and government stakeholders.

Technologies

Python
Reinforcement learning
Neural networks
Deep learning
Computer vision
MLOps
DevSecOps
CI/CD
Containers

Desired Qualifications

Active Top Secret (TS) or Top Secret/Sensitive Compartmented Information (TS/SCI) security clearance.
Prior experience supporting defense or intelligence community customers, including the United States Air Force, United States Space Force, National Reconnaissance Office, National Geospatial-Intelligence Agency, or Defense Intelligence Agency.
Experience applying model-based systems engineering (MBSE) methods and tools to define requirements, architecture, interfaces, verification, or digital engineering artifacts.
Experience deploying ML solutions in cloud, edge, constrained, or classified environments.

Location, Employment Type, and Clearance

Work location: In person (Chantilly, VA onsite), with possible placement in the Chantilly, VA / Washington, DC metro region or Plano, TX.
Some travel required.
Job type: Full-time
Salary: USD 150,000 per year (From $150,000.00 per year)
Security clearance: Top Secret (Required)
Ability to commute: Chantilly, VA 20153 (Required)