Robotics & Embodied AI

Recruiting the teams behind next-generation robotics and embodied AI platforms

DeepRec.ai partners with leading teams to deliver end-to-end recruitment services across robotics and embodied AI. From humanoid systems to autonomous drones, quadrupeds, and self-driving platforms, we help the innovators pushing embodied intelligence out of simulation and into real-world deployment.

Talent is scarce and highly specialised. When project success hinges on critical hires in complex environments, teams need a recruitment partner who lives and breathes the deep tech market.

DeepRec.ai's specialised consultants understand the technical nuance, delivery risk, and market dynamics that make hiring in robotics and embodied AI uniquely challenging.

Whether you're scaling your team or looking for your next career move, our team helps clients and candidates thrive across the full lifecycle of intelligent physical systems:

Hire exceptional robotics & embodied AI talent:

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Jobs in Robotics & Embedded AI

Why Choose DeepRec.ai as a Talent Partner?

A Dedicated Robotics & Embodied AI Division

DeepRec.ai operates through specialist divisions, each focused on a specific area of deep tech. This structure enables us to build deeper relationships with candidates and a strong technical fluency that generalist agencies can't replicate.

Credibility Through Delivery

We work closely with teams developing and deploying today's most complex systems. Our proximity to the bleeding-edge of real-world robotics gives us a strong understanding of evolving role requirements in a technically constrained market. This is underscored by our client net promoter score (NPS) of +100.

Responsible Recruitment

As part of Trinnovo Group, DeepRec.ai is B Corp certified, a member of a global community of organisations committed to putting people and the planet before profit. For our clients, this translates into ethical and responsible recruitment practices built on trust, accountability, and long-term impact.

Community-Driven Recruitment

Much of the most in-demand robotics and autonomy talent is not active on traditional hiring channels. Through sustained engagement with the deep tech ecosystem - including events, collaboration, and industry-led initiatives - DeepRec.ai maintains access to engineers and researchers working on cutting-edge embodied systems. Check out our community page here: https://www.deeprec.ai/community

A Long-Term Talent Partner

We don’t take a transactional approach to hiring. Instead, we work as a long-term talent partner, supporting teams as technologies mature, projects evolve, and organisational needs change.

Our delivery model flexes to match the challenge. From executive search for critical leadership hires, to embedded and retained recruitment for high-volume hiring projects or time-sensitive buildouts, through to permanent and contract hiring across highly specialised roles. This allows us to provide continuity, context, and consistency across multiple hiring cycles, rather than resetting the process each time a new role opens.

Core Technical Domains

DeepRec.ai works with teams operating across the full embodied stack. Our consultants have the means to help you navigate shifting hiring requirements, whether that's system maturity, deployment context, or risk profile.

Our core technical domains (areas we specialise in recruiting for) include:

Robotics Platforms & Embodiment

We work with organisations developing physical systems that operate in dynamic environments. This includes research-driven humanoid programmes and logistics platforms designed and deployed at scale:

Humanoid robots
Service robots
Soft robotics
Quadrupeds and bipedal robots
Mobile manipulation systems

Learning & Control for Robotics

Hiring in this area requires an understanding of how learned behaviours transfer beyond controlled settings. We recruit specialists working on learning-based control for applications such as autonomous navigation, manipulation, and adaptive behaviour, including:

Reinforcement Learning (RL)
Imitation & Demonstration Learning
Sim-to-Real Transfer
End-to-end learning
Vision-Language-Action (VLA) models for control
Policy learning from foundation models

Perception, Vision & Sensor Fusion

Reliable autonomy depends on perception systems that perform under uncertainty. We work with teams hiring for:

Object recognition and tracking
SLAM (Simultaneous Localisation and Mapping)
3D vision & depth sensing
Tactile and proprioceptive sensing
Vision-Language Models (VLMs) for robotic perception

Foundation Models & Generative AI for Physical Systems

As foundation models move into physical domains, hiring demands shift accordingly. We recruit across:

Vision-Language Models (VLMs)
Vision-Language-Action (VLA) models
Diffusion models for motion, grasping & trajectory generation
Large-scale robot foundation models
World models & predictive simulation

Simulation, Data & Digital Twins

For many robotics programmes, simulation is the primary environment for training, testing, and validation. We work with teams building and relying on:

Physics-based simulators
Synthetic data generation
Digital twins
Simulation environments for large-scale robot learning

Human-Robot Interaction (HRI) & Collaboration

Systems operating alongside people introduce additional complexity around safety, usability, and trust. We recruit for teams focused on applications such as collaborative manufacturing, assistive robotics, and service environments, including:

Collaborative robots (cobots)
Gesture, intent & emotion recognition
Natural language and multimodal interaction
Safety-critical human-in-the-loop systems

Autonomous Systems

We also partner with organisations deploying autonomy at scale, from advanced mobility programmes to industrial automation. This includes teams working on:

Self-driving cars
Autonomous drones
Mobile robots in logistics & industry

Contact DeepRec.ai Directly

Robotics and embodied AI demand a different hiring conversation. The work is interdisciplinary, the timelines are long, and the margin for error is small. We exist to support that reality, bringing context and consistency to one of the most technically demanding talent markets.

Tell us what you're looking for, and we'll connect you with the right consultant as soon as possible:

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ROBOTICS & EMBODIED AI CONSULTANTS

Meet Anthony

Anthony Kelly

Co-Founder & MD EU/UK

Meet Paddy

Paddy Hobson

Senior Consultant | DACH

LATEST JOBS

Berlin Kreuzberg, Berlin, Germany

Computer Vision Engineer - MLOps

Permanent€85000 - €100000 per annum

Computer Vision Engineer - MLOps Location: Remote - (Must be EU based) Salary: Base salary equity We are hiring a Computer Vision Engineer to join a robotics company building intelligent, flexible robots for real manufacturing environments. This role is focused on production systems. You will be working on perception pipelines that run 24/7 on deployed robotic cells. The robots follow a modular architecture where new capabilities are continuously added, such as object recognition, grasp point estimation, anomaly detection, and task specific behaviours. As the system scales, the ML and MLOps foundations become critical. This role exists to own and extend those foundations. What you will be doingDesign, build, and deploy 2D and 3D computer vision systems used in live production environments. This includes image classification, object detection, semantic and instance segmentation, metric learning, and smart filtering.Take models end to end, from data and training through to deployment, optimisation, and monitoring.Contribute directly to an in house MLOps platform that supports data ingestion, experiment tracking, model versioning, deployment, and observability across multiple robotic capabilities.Work closely with robotics and hardware focused teams and help ensure models run efficiently and reliably on edge and production hardware. Over time, this includes model conversion and optimisation using tools such as ONNX and TensorRT. What we are looking for This is a production engineering role. We are looking for someone who has built ML systems before.Non-negotiable experience:Hands-on experience working with visual data in production systems (2D and/or 3D computer vision).Proven production ML experience: you have taken models from training through deployment and supported them in live environments.Strong Linux fundamentals, including working over SSH and operating production infrastructure.You have built MLOps systems, not just used them. This includes ownership of data pipelines, experiment tracking, model versioning, deployment, and monitoring.Solid understanding of how models actually work under the hood. You are comfortable reasoning about backpropagation, gradients, network architectures, and debugging model behaviour when things go wrong.Nice to have:Experience with robotics, autonomous systems, or other edge-deployed ML.Synthetic data generation and the ability to design efficient data collection strategies.Model conversion and optimisation workflows using ONNX and/or TensorRT.Experience with ROS, Kubernetes, and cloud platforms.Why apply?This role offers a rare combination of real-world impact and deep technical ownership. You are not optimising isolated models or working on disconnected experiments. You are helping define the perception and MLOps foundations for intelligent robotic systems that are already operating in production and will continue to scale over time. Engineers work in pods with clear ownership and the opportunity to grow into leading entire problem areas. Progression and compensation are tied to impact rather than tenure. The environment is fast-moving and flexible, with intense periods of work when needed, and a strong emphasis on transparency and alignment. This is a role for someone who wants to see their work move quickly from code to real machines on real factory floors.

Jacob Graham

Posted 13 days ago

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Madrid, Spain

Deep Reinforcement Learning Engineer

Permanent€60000 - €100000 per annum

Location: Europe (strong preference for Spain, ideally Madrid) Type: Full-time About the Company We're working with a high-growth startup developing AI systems that allow industrial robots to perform tasks they currently cannot, starting with complex warehouse operations like mixed palletizing. Their technology combines deep reinforcement learning (DRL) with modern sequence modeling to tackle control and combinatorial optimization problems where classical approaches fail.They are a small, highly skilled team. Joining us means having direct impact, minimal bureaucracy, and ownership over core technology that will be deployed in real-world, high-throughput environments. Role Overview As the second hire in the DRL team, you will own the end-to-end reinforcement learning stack: from problem formulation to algorithm design, large-scale training, evaluation, and deployment. You will work closely with the technical leadership to translate cutting-edge DRL research into practical production throughput at operational sites. This role is highly autonomous, requiring a hands-on expert capable of leading experiments, troubleshooting complex issues, and establishing best practices for algorithm development and deployment. Key ResponsibilitiesDesign, implement, and ship DRL algorithms (e.g., PPO, SAC, DDQN and variants) incorporating advanced architectures such as encoders, cross-attention, and pointer networksOptimize stability and sample efficiency using techniques such as GAE, reward shaping, normalization, entropy/KL control, curriculum learning, and distributional/value-loss tuningSet up and manage large-scale training pipelines: multi-GPU training, parallel rollouts, efficient replay/storage, reproducible experimentsProductionize algorithms with clean, maintainable PyTorch code, profiling, Dockerized services, cloud deployments (AWS), experiment tracking, and dashboardsCollaborate with leadership to align technology with business goals and customer needsMentor and grow future team members, fostering a culture of technical excellence and innovationRequired QualificationsProven track record delivering DRL systems beyond academic demos: led at least one end-to-end DRL system from concept to production or achieved a state-of-the-art benchmark in the last 3–5 yearsDeep expertise in reinforcement learning and deep learning, with strong PyTorch skillsSolid understanding of DRL theory: MDPs, Bellman operators, policy gradients, trust-region/KL methods, λ-returns, stability and regularization in on-policy/off-policy regimesSystems experience: Python, Linux, multi-GPU training, Docker, cloud deployments (AWS preferred)Comfortable taking ownership of experiments, code quality, and results in a small, high-impact teamPhD or equivalent experience in DRL is acceptable; strong academic-only candidates considered if they demonstrate deep expertiseNice to HaveRobotics experience is not requiredProduction system deployment experience is beneficial but not mandatoryLocation & TravelEU-based (CET ±1) with occasional travel to customer sitesPreference for candidates in Spain; otherwise, EuropeCompetitive Compensation & Real Equity Offered.Interview ProcessDeep Technical Session – with CTO, focused on past DRL work (no coding tests, no homework)Traits & Skills Interviews – Two × 1-hour sessions with co-founders to assess problem-solving, communication, and startup fitTeam Meet & Offer – final discussion and reference checkWhy This Role is ExcitingWork at the frontier of DRL robotics in real-world, high-throughput industrial applicationsHigh autonomy, technical ownership, and direct impact on deployed AI systemsSmall, experienced founding team and strong early customer traction reduces commercial risk while maximizing technical challengeOpportunity to join a founding-stage team with equity and influence over core product and technology

Paddy Hobson

Posted 22 days ago

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