Senior Machine Learning Research Engineer

Secure Consult; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Job summary

Secure Consult in London is seeking a Senior Machine Learning Research Engineer to develop learning-based locomotion and whole-body control for dynamic humanoid robots. You will collaborate with interdisciplinary engineers to build robust motion capabilities for real industrial settings, advancing simulation-to-real transfer and real-time policy optimization.

The role requires 3+ years in ML, strong deep learning and policy architectures knowledge, and hands-on RL/IL experience.

Qualifications

• Degree in Robotics, Machine Learning, Computer Science, or related technical field (BS, MS, or PhD).
• 3+ years developing machine learning systems in industry or research.
• Strong understanding of deep learning and modern policy architectures including training, inference, experimentation, and debugging.
• Hands-on experience with reinforcement learning and at least one additional area such as imitation learning, generative models, sequence models, or large-scale representation learning.

Responsibilities

• Design and train learning-based locomotion and whole-body control policies for dynamic robotic behaviors.
• Develop motion primitives using learning-based generative models.
• Apply reinforcement learning, imitation learning, and other learning-based approaches to motion generation and control.
• Build scalable simulation, data generation, and training pipelines using platforms like Isaac Lab and MuJoCo.
• Develop methods for robustness, adaptation, and sim-to-real transfer including domain randomization and learning from real-world robot data.
• Deploy, evaluate, debug, and fine-tune learned policies on physical humanoid robots.
• Optimize policy inference and training systems for performance and latency in real-time control.
• Integrate learned policies into the robot control and autonomy stack.

Skills

Python

PyTorch

Reinforcement learning

Imitation learning

Gen models

Sequence models

Policy architectures

Deep learning

Education

BS/MS/PhD in Robotics, ML, CS

Tools

Isaac Lab

MuJoCo

Isaac Sim

Job description

Senior Machine Learning Research Engineer

London On-site SC-3A99D9AAB93D220F96C9

The opportunity

Join a cutting-edge humanoid robotics team to develop and implement learning-based algorithms for dynamic locomotion and whole-body control. Work with interdisciplinary engineers to create robust, reusable motion capabilities that enable advanced robot movement and interaction in real industrial settings.

What you could work on

• Design and train learning-based locomotion and whole-body control policies for dynamic robotic behaviors
• Develop motion primitives using learning-based generative models
• Apply reinforcement learning, imitation learning, and other learning-based approaches to motion generation and control
• Build scalable simulation, data generation, and training pipelines using platforms like Isaac Lab and MuJoCo
• Develop methods for robustness, adaptation, and sim-to-real transfer including domain randomization and learning from real-world robot data
• Deploy, evaluate, debug, and fine-tune learned policies on physical humanoid robots
• Optimize policy inference and training systems for performance and latency in real-time control
• Integrate learned policies into the robot control and autonomy stack
• Degree in Robotics, Machine Learning, Computer Science, or related technical field (BS, MS, or PhD)
• 3+ years developing machine learning systems in industry or research
• Strong understanding of deep learning and modern policy architectures including training, inference, experimentation, and debugging
• Hands-on experience with reinforcement learning and at least one additional area such as imitation learning, generative models, sequence models, or large-scale representation learning
• Proficiency in Python and PyTorch
• Experience with physics-based simulation environments such as Isaac Lab, Isaac Sim, or MuJoCo
• Ability to apply learning algorithms in the context of dynamics, control, and physical systems

Security vetting

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Working arrangements

London, On-site. Confirm employment terms and any clearance requirements with your consultant.

Machine Learning Robotics Reinforcement Learning Python PyTorch Simulation Control Systems Research Engineer