Reinforcement learning intern Job at Enchanted Tools (Paris)

Research Scientist Intern, Reinforcement Learning

We are committed to advancing the field of artificial intelligence by making fun...

Location

France , Paris

Salary:

Not provided

Research Scientist Intern, Reinforcement Learning

We’re looking for a curious and motivated Reinforcement Learning Intern to help ...

Location

United Kingdom , London

Salary:

Not provided

Wayve

Expiration Date

Until further notice

Requirements

Currently pursuing a PhD or Masters in Computer Science, Robotics, Electrical Engineering, or a related field, with a focus on Machine Learning, AI, or Computer Vision
Experience in research in Reinforcement Learning
Interest in one or more: synthetic data, representation learning, and Offline RL
Comfortable working in Python and libraries like PyTorch, NumPy, and Pandas
A principled mindset: you enjoy brainstorming, making assumptions, building, testing, and iterating on ideas to see what works

Job Responsibility

Help advance the next generation of decision-making systems for autonomous driving
Work embedded in a research team to develop scalable RL algorithms that enable vehicles to learn complex behaviors directly from experience — both in simulation and the real world

What we offer

Competitive compensation and benefits
A dynamic and fast-paced work environment in which you will grow every day - learning on the job, from the a diverse team of the brightest researchers and engineers in this space
A culture that is ego-free, respectful and welcoming
Potential to publish your research work at a top flight conference
The chance to be part of a truly mission driven organisation and an opportunity to shape the future of autonomous driving

PhD Autonomy Engineer Intern - Planning & Controls (Reinforcement Learning)

Skydio builds the world’s most advanced autonomous drones used across inspection...

Location

Switzerland , Zurich

Salary:

50.00 EUR / Hour

Skydio

Expiration Date

Until further notice

Requirements

PhD student in Robotics, Machine Learning, Controls, or related field
Strong fundamentals in RL, control theory, and motion planning
comfort with safety/robustness concepts
Proficient in Python (PyTorch/JAX/Ray RLlib) and at least one of C++ or CUDA
Hands-on experience with robotics simulation (Isaac Lab/MuJoCo/PyBullet) and sim2real techniques
Experience training/deploying policies for navigation, manipulation, or locomotion on real robots or autonomous vehicles

Job Responsibility

Develop and deploy reinforcement learning (and adjacent policy-learning methods) that make Skydio aircraft plan, navigate, and control themselves more intelligently—safely, reliably, and efficiently—across our ecosystem: handheld apps, ground control, cloud autonomy services, and fleet workflows
Navigation & avoidance in the wild: Train policies that adapt online to cluttered 3D scenes (forests, bridges, urban canyons), complementing our geometric stack for robust obstacle avoidance and dynamic goal-seeking
RL-augmented planning: Fuse learned cost shaping / value functions with trajectory optimization for smooth, agile flight with tight safety envelopes and mission constraints
Sim → Real at scale: Build scalable datasets and training loops with Isaac Lab, domain randomization, residual learning, and safety filters
validate on real drones weekly
Human-in-the-loop shared control: Learn assistive policies that blend pilot intent, autonomy priors, and uncertainty-aware behaviors for intuitive control handoffs
Fleet & multi-agent: Explore decentralized coordination for coverage, pursuit, and collaborative mapping with minimal comms

Senior Engineering Manager, Reinforcement Learning Environments (RLE)

We’re expanding our team and seeking a Senior Engineering Manager to lead our Re...

Location

United States , San Francisco

Salary:

230000.00 - 300000.00 USD / Year

Handshake

Expiration Date

Until further notice

Requirements

3+ years of engineering management experience, with increasing scope and ownership
Experience managing senior engineers
experience managing an Engineering Manager (or equivalent scope) strongly preferred
5+ years of prior hands-on engineering experience
Strong technical background in platform systems, distributed systems, or full-stack infrastructure
Experience building internal platforms, data pipelines, or research-facing tools
Proven ability to operate effectively in fast-paced, ambiguous environments
Experience driving cross-functional alignment across engineering, research, and operations
Willingness to work in-office in San Francisco 5 days/week

Job Responsibility

Lead and grow a high-performing team of 8–9 engineers building reinforcement learning environments
Manage, mentor, and develop senior engineers and future engineering leaders
Partner closely with research, product, and operations teams to define roadmap and execution priorities
Drive technical architecture for scalable, reliable, and extensible environment systems
Build plug-and-play environments that integrate seamlessly with model training pipelines
Balance platform rigor with operational complexity and data quality requirements
Establish engineering best practices around reliability, observability, and performance
Foster a culture of ownership, velocity, and high technical standards

What we offer

Equity in a fast-growing company
401(k) match, competitive compensation, financial coaching
Paid parental leave, fertility benefits, parental coaching
Medical, dental, and vision, mental health support, $500 wellness stipend
$2,000 learning stipend, ongoing development
Internet, commuting, and free lunch/gym in our SF office
Flexible PTO, 15 holidays + 2 flex days
Team outings & referral bonuses

Fulltime

Machine Learning Intern

Ema is building next-generation AI to empower every employee in the enterprise t...

Location

United States , San Francisco Bay Area

Salary:

Not provided

Ema

Expiration Date

Until further notice

Requirements

2+ years of industry experience in a software engineering, data science, or ML role (full-time or internship)
Strong coding skills in Python and experience with ML frameworks like PyTorch or TensorFlow
A solid foundation in data structures, algorithms, and software engineering principles
Exposure to NLP, deep learning, reinforcement learning, or retrieval systems
Experience working with real-world data, and comfort with SQL and data processing pipelines
Curiosity about MLOps, cloud infrastructure, and scaling ML models
A collaborative mindset, eagerness to learn, and the ability to thrive in a fast-paced environment

Job Responsibility

Work alongside senior ML engineers to research, build, and deploy machine learning models across NLP, retrieval, ranking, and reasoning
Prototype and experiment with LLM-based architectures and agentic systems
Help process and analyze large-scale structured and unstructured datasets
Build data pipelines, contribute to model training and evaluation, and participate in the deployment of models in production
Assist with validation experiments such as A/B testing and other evaluation methods to ensure robustness and reliability
Collaborate closely with cross-functional teams and participate in technical discussions and code reviews

Fulltime

AI Research Engineer - Reinforcement Learning

At Helsing we deliver AI-based capabilities and the enabling infrastructure that...

Location

Germany , Munich

Salary:

Not provided

Helsing

Expiration Date

Until further notice

Requirements

Hold MSc in machine learning with a speciality in either reinforcement learning, multi-agent systems, automation and control, or robotics
Have excellent communication skills and the ability to report and present research findings clearly and efficiently both internally and externally
Are passionate about keeping up-to-date with current research and enjoy reimplementing / extending papers on state-of-the-art Deep Learning-based approaches
Possess solid software engineering skills, writing clean and well-structured code in Python and/or languages like Rust, Java, or modern C++, and experience deploying AI software to production including testing, QA, and monitoring

Job Responsibility

Design, train and deploy agents in complex multi-agent environments
Contribute to our reinforcement learning stack by implementing, improving and extending the current state of the art in multi-agent reinforcement learning
Be a part of impactful projects and will collaborate with people across several teams and backgrounds to integrate cutting edge ML/AI in our production systems

What we offer

Competitive compensation and stock options
Relocation support
Social and education allowances
Regular company events and all-hands to bring together employees as one team across Europe
A hands-on onboarding program (affectionately labelled “AI-duction”), in which you will be familiarising yourself with our tools and ML pipelines used across the company

Fulltime

Associate Director, Reinforcement Learning (ML)

Lead Amgen’s strategy and execution for Reinforcement Learning from Human Feedba...

Location

United States , Thousand Oaks; Jacksonville

Salary:

Not provided

Amgen

Expiration Date

Until further notice

Requirements

Doctorate degree and 3 years of Computer Science, IT or related field experience
Master’s degree and 5 years of Computer Science, IT or related field experience
Bachelor’s degree and 7 years of Computer Science, IT or related field experience
Associate’s degree and 12 years of Computer Science, IT or related field experience
High school diploma / GED and 14 years of Computer Science, IT or related field experience
Deep, hands-on expertise in Reinforcement Learning from Human Feedback (RLHF) and/or advanced reinforcement learning, including reward modeling, policy optimization, exploration strategies, and offline/online evaluation
Demonstrated experience deploying RLHF or RL systems into production for real-world applications (e.g., large language models, recommendation systems, decision support tools, or workflow automation), ideally in healthcare, life sciences, or other regulated domains
Strong background in modern machine learning and deep learning, with practical experience in Python and frameworks such as PyTorch or TensorFlow, and familiarity with LLM ecosystems and tooling
Experience driving sophisticated, cross-functional initiatives, collaborating with non-technical stakeholders (e.g., physicians, scientists, commercial leaders, compliance, legal) and translating needs into impactful AI solutions
Strong ability to communicate complex technical topics simply, tailoring content to senior executives and non-technical audiences

Job Responsibility

Lead the design and development of RLHF systems including reward modeling, policy optimization, safety and alignment mechanisms, and evaluation frameworks for large language models and other AI systems
Drive hands-on technical execution, particularly for high-impact projects, reviewing architectures, experimentation plans, and code, and helping the team navigate scientific and engineering trade-offs
Establish best-practice pipelines for human feedback, partnering closely with internal customer teams to define feedback protocols, annotation quality standards, and governance for RLHF data
Define and track success metrics for RLHF systems, balancing offline and online evaluation, A/B tests, safety and robustness criteria, and business or scientific outcomes
Collaborate across Amgen leaders to ensure RLHF solutions are aligned with strategy, compliant with policy, and integrated into real workflows
Partner with Data, Platform and Technology teams to ensure that RLHF workloads are supported by scalable data platforms, model hosting, experimentation infrastructure, and MLOps best practices
Champion responsible and compliant AI, working with Legal, Compliance, and Information Security to implement governance around human feedback, data usage, model behavior, transparency, and risk management in a regulated environment
Communicate insights and influence senior stakeholders, creating clear narratives, roadmaps, and recommendations that help executives understand RLHF trade-offs, risks, and opportunities

What we offer

A comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions, group medical, dental and vision coverage, life and disability insurance, and flexible spending accounts
A discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan
Stock-based long-term incentives
Award-winning time-off plans
Flexible work models where possible

Machine Learning Engineering Intern

Airbnb is seeking a Machine Learning Engineer Intern for our 2026 Summer Intern ...

Location

United States

Salary:

20.00 - 50.00 USD / Hour

Airbnb

Expiration Date

Until further notice

Requirements

Doctorate students with at least 1 semester of school remaining after the internship (expected graduation after December 2026)
Studying Computer Science or a related field
Knowledge of AI, especially large language model (LLM) fundamentals including fine tuning, data synthesis, reinforcement learning, etc.
Expertise of building large scale ReAct style AI agents
Proficient in Python and other open source LLM libraries like Huggingface
Work authorization for employment in the United States is required

Job Responsibility

Drive a project from end-to-end that aligns with your technical interest and goals
Exploring how new advancements in AI agents can be applied to Airbnb search and discovery experience
Own and drive a capstone project from beginning to end
Collaborate with multiple team members to achieve project milestones
Communicate with stakeholders across different teams to provide project updates
Seek and provide feedback throughout the internship
Actively participate in and contribute to the Engineering org and broader Airbnb community

What we offer

Employee Travel Credits

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Reinforcement learning intern

Job Description

Job Responsibility

Requirements

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