--- title: 'Internship, Robot Learning Research at Humanoid' canonical: 'https://feeny.ai/job/internship-robot-learning-research-humanoid-london-jk4a0q6w77dy' type: 'job' last_seen: '2026-10-15' --- # Internship, Robot Learning Research at Humanoid - **Company:** [Humanoid](https://feeny.ai/companies/humanoid) - **Location:** London, United Kingdom - **Employment:** full-time - **Work type:** onsite - **Posted:** 2026-09-28 - **Last confirmed live:** 2026-10-15 - **Apply:** https://jobs.ashbyhq.com/humanoid/e1a2a9de-ad23-4632-9d93-ee50fd41a221 ## Job description Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further. ## Our Mission We're building software systems that enable robots to operate effectively in the real world, expanding human capability and redefining how work gets done. The Opportunity We're looking for interns who are curious, proactive, and excited to work on real-world robotic systems. Depending on your interests and skills, you will be able to work across our research stack: reinforcement learning, world models, pretraining, and inference & optimisation. That spans everything from training policies in simulation, through building the generative models that let robots predict their world, to squeezing models onto real-time edge compute. You'll collaborate closely with the team to find where you can have the most impact, and we're looking for people who are excited to dive into unfamiliar areas and learn quickly. This is a full-time internship (5 days per week), based in our London office, where you'll contribute to real systems from early on with guidance and support from experienced researchers and engineers. Duration: 12 to 24 weeks | Start date: Flexible | Compensation: Competitive pay and perks ## What you might work on Reinforcement Learning - Train language-vision conditioned manipulation policies via RL in the real world - Construct challenging and diverse suites of manipulation tasks and RL models in simulation (Isaac Sim, MuJoCo) - Experiment with ways of bringing policies trained in simulation to the real world World Models - Action-conditioned video prediction and dynamics models that stay physically consistent over long horizons - Use world models as learned simulators: score candidate policies offline and generate synthetic rollouts for training - Build fidelity metrics that quantify where the world model can be trusted Pre- and post-training - In-context learning - Short and long term memory - Post-training VLA models on specific production-grade use cases - Different data modalities, closing embodiment gap between human and robot data, data diversity and attribution. Inference & Optimisation - Optimise models for real-time edge inference on robot hardware: profiling, quantisation, and latency/throughput trade-offs - Improve training and data-loading performance across distributed GPU infrastructure ## What We're Looking For - Candidates pursuing or holding a master’s or PhD in computer science, machine learning, robotics, or a related field. - Strong foundations in machine learning; strong Python and hands-on experience with PyTorch or JAX. - Interest in one or more of: reinforcement learning, world models and generative video, VLA/multimodal models, or ML systems and inference optimisation. - Experience running experiments and interpreting results with rigour. - Ability to take ownership and iterate with guidance. - Strong problem-solving skills and attention to detail. - Fast learner, comfortable in a research-driven, fast-moving environment. ## What We Offer - Free daily breakfast, catered lunch, and snacks in-office. - Work at the frontier - collaborate daily with world-class engineers, researchers, and product experts building the next generation of AI and humanoid robotics. - Real ownership - direct access to founding leadership, meaningful input on product direction, and the ability to drive key initiatives from day one. How to apply Complete the challenge below and submit your solution as a public GitHub repository. You will be able to include your GitHub repository URL when you fill out the application form, alongside your name and CV. You have two weeks to complete the challenge and submit your solution. The deadline for submission is Friday, 16 October 2026, 23:59 BST. We're not looking for standard solutions, we're looking for how you think. The strongest submissions are creative, original, and push beyond the obvious. Intern Challenge: The goal of the challenge is to use real data collected by an applicant to drive a robotic manipulator in a simple simulation environment (e.g. Libero). The applicant is welcome to use a simple phone to record a small manipulation dataset and use it creatively showcasing their knowledge with VLA and/or World Models. Here is an example of how it might look like: Left: a snapshot from an egocentric hand-manipulation video sample; Right: simulation environment where we use that data to drive a Panda arm in Libero simulator with a trained policy based on SmolVLA. Some suggestions on how you can develop your project: - use your recorded egocentric data to post-train a policy on a simple task - explore creative retargeting strategies, e.g. adapt your data to challenging embodiments - bootstrap a policy and use any form of RL to get better performance - use world modelling to showcase video/state prediction, less focusing on policy performance - optimise a standard policy to run considerably faster than a baseline However, we don’t want to limit your imagination: in the age of AI agents, a standard task can now be easily achieved. You are welcome to use any resources at your disposal as long as the main constraint is achieved: you use the data that you personally collected. At the same time, we tried to design the challenge so that it could be hand-coded as well. We also checked that many ideas do not require access to large compute and could be done on Google Colab GPU notebooks. What to submit Complete the challenge above and submit your solution as a public GitHub repository by Friday, 16 October 2026, 23:59 BST. Include a README/Presentation with instructions to run your system, example outputs, and a note on your design choices, what worked and what didn’t. ## What we care about - Creativity in approach, while satisfying the constraint: the data you collected must play a role in the approach - Performance of your policy in simulation and/or quality of WM predictions - Implementation simplicity and clear presentation of results without AI slop Make something you’re proud of! ## About Humanoid ## Company Overview - **One-liner**: Humanoid builds commercially scalable, safe, and reliable humanoid robots designed to automate labor-intensive industrial tasks across warehousing, logistics, manufacturing, and retail. - **Entity Type**: Private (Seed stage) - **Headquarters**: London, United Kingdom (HQ) with offices in Cambridge, MA, USA; Burnaby, BC, Canada; and additional presence in Germany, Georgia, and the UAE. - **Founded**: 2024 - **Founders**: Artem Sokolov (CEO & Founder) ## Core Business - **Primary industry/industries**: Robotics Engineering, Humanoid Robotics, Industrial Automation, Artificial Intelligence - **Target customers**: B2B / Enterprise — industrial sectors including retail, e-commerce, third-party logistics (3PL), manufacturing, and automotive. - **Mission or purpose statement**: “Empowering humanity by building the most reliable, safe, and helpful humanoid robots.” The company aims to free people from repetitive, dangerous, and tedious work, allowing them to engage in more creative and meaningful activities. ## Products & Services - **HMND 01 Alpha Wheeled**: A robust industrial-grade wheeled humanoid robot designed for material handling, pick-and-place, and visual inspection in warehouses and factories. Prioritizes reliability, low total cost of ownership (TCO), and minimal maintenance. - **HMND 01 Alpha Bipedal**: A bipedal humanoid robot for more complex environments requiring advanced locomotion, navigation of confined spaces, and higher dexterity. Features human-level manipulation speeds and walking speeds up to 1.5 m/s. - **KinetIQ**: An AI framework for orchestrating fleets of humanoid robots across both wheeled and bipedal platforms, enabling centralized control and task optimization. - **Modular Garments**: Exchangeable protective “garments” that allow HMND 01 robots to adapt to different use cases and environments, minimizing contamination and collision damage while enabling functional customization. ## Market Standing - **Valuation/Market Cap**: Not disclosed. - **Key Metric**: Total funding of **$50 million** (Seed round closed October 2025, with 1 lead investor). - **Notable Investors/Partners**: Strategic partners include leading global companies in AI, cloud computing, and research. Specific partner names are not publicly listed on the website, but the company highlights collaboration with commercial partners to scale deployment. - **Growth Signals**: - Headcount grew **139.2% YoY** to ~182 employees (as of mid-2026), with 65 active job postings. - Opened offices in London, Boston, and Vancouver within its first year. - Team includes talent recruited from major robotics and AI firms: Sanctuary AI, Boston Dynamics, Dyson, Arrival, Ocado Technology, 1X, Wayve, Apptronik, and Brain Corp. - Focus on initial target sectors representing a combined $12 trillion in annual payroll and 250 million workers. ## Competitive Advantages - **Human-centric design**: The world is already designed for humans, so humanoids can seamlessly integrate into existing environments without costly redesign. - **Modularity**: HMND 01’s hardware/software modularity enables multi-task performance and easy repurposing, reducing the need for specialized robots. - **Reliability & low TCO**: Prioritizing hardware and software stability over raw performance to deliver industrial-grade robustness suitable for mass commercialization. - **KinetIQ fleet orchestration AI**: Proprietary framework for managing mixed fleets of wheeled and bipedal robots, differentiating from competitors focused on single-form-factor robots. - **Experienced leadership**: CEO Artem Sokolov previously scaled a family manufacturing business into a billion-dollar enterprise (EY Entrepreneur of the Year 2021). The CTO, Jarad Cannon, has extensive experience translating research into deployment-ready robotics systems. ## Strategic Focus - **Near-term (2027)**: Deploy robots for physical tasks in manufacturing, warehousing, logistics, and retail — tackling repetitive, dangerous, and tedious manual work. - **Medium-term (2029)**: Expand into the service sector, targeting the 70-80% of the global economy that will be services, with a focus on assisting an aging population (1.4B people over 60). - **Long-term (2031+)**: Enter households as assistants and companions, potentially revolutionizing everyday life across 3.5 billion homes. - **Core strategy**: Partner with leading global companies in AI, cloud, and research for technology development, and with commercial partners for scaled deployment. The company believes the humanoid TAM will reach $38 billion by 2035 and $1 trillion by 2050. ## Why Work Here - **High-growth environment**: Company grew headcount by 139% in a single year, indicating rapid scaling and opportunity for career advancement. 65+ open roles suggest strong hiring momentum. - **Talent from top robotics firms**: Team includes alumni from Boston Dynamics, Sanctuary AI, Dyson, Ocado Technology, Arrival, Wayve, and others — offering a deep learning and collaboration environment. - **Technical focus**: Engineering and technical roles represent 38% of the workforce, with senior-level hires at 23% of the team. The company emphasizes hardware, AI, machine learning, and control systems. - **Mission-driven**: Clear purpose to automate undesirable, unsafe, and repetitive work — appealing to candidates who want to make a tangible societal impact. - **Global presence**: Offices in London (HQ), Cambridge (USA), and Burnaby (Canada), plus operations in 16 countries — offering potential for international mobility and remote collaboration. - **Innovation culture**: Described as a place where engineers and researchers “move from demo to deployment” and “not just in the lab.” The CTO emphasizes building systems that perform in the real world from day one. - **Office/Hybrid policy**: Not explicitly stated, but the company has physical offices in three countries and a distributed team across 16 nations — likely supports hybrid/remote with in-office collaboration for engineering roles. ## Sources 1. [thehumanoid.ai](https://thehumanoid.ai/) 2. [thehumanoid.ai/about-us/](https://thehumanoid.ai/about-us/) 3. [thehumanoid.ai/team/](https://thehumanoid.ai/team/) 4. [thehumanoid.ai/careers/](https://thehumanoid.ai/careers/) 5. [LinkedIn Company Page](https://www.linkedin.com/company/humanoidai) 6. [Jobs.ashbyhq.com/humanoid](https://jobs.ashbyhq.com/humanoid) ## Other roles at Humanoid - [Senior Machine Learning Research Engineer](https://feeny.ai/job/senior-machine-learning-research-engineer-humanoid-london-hxe7d1dzwkq2) — London, United Kingdom - [Senior Electrical Design Engineer](https://feeny.ai/job/senior-electrical-design-engineer-humanoid-us-boston-xsws5bc26e70) — US Boston, MA - [Autonomy Engineer - VLA Pre-training](https://feeny.ai/job/autonomy-engineer-vla-pre-training-humanoid-us-san-diego-axm9pwqdfhbv) — US San Diego, CA - [Prototype Engineer](https://feeny.ai/job/prototype-engineer-humanoid-canada-vancouver-8q47n6t9cttp) — Canada Vancouver, Canada - [Senior Test and Validation Engineer](https://feeny.ai/job/senior-test-and-validation-engineer-humanoid-canada-vancouver-z93vt2r30q49) — Canada Vancouver, Canada - [Warehouse Operative Specialist](https://feeny.ai/job/warehouse-operative-specialist-humanoid-canada-vancouver-q6hg0sxc5x2k) — Canada Vancouver, Canada - [Senior General Mechanical Design Engineer](https://feeny.ai/job/senior-general-mechanical-design-engineer-humanoid-canada-vancouver-egfdqs5n1nek) — Canada Vancouver, Canada - [Senior Actuator Mechanical Design Engineer](https://feeny.ai/job/senior-actuator-mechanical-design-engineer-humanoid-canada-vancouver-2wybjffgzk3c) — Canada Vancouver, Canada - [Senior Firmware and Embedded Software Engineer](https://feeny.ai/job/senior-firmware-and-embedded-software-engineer-humanoid-canada-vancouver-k66gncrpem53) — Canada Vancouver, Canada - [Mechanical Design Engineer - Robotic Hand](https://feeny.ai/job/mechanical-design-engineer-robotic-hand-humanoid-canada-vancouver-gh8tcpr351rr) — Canada Vancouver, Canada