Machine Learning Engineer

Machine Learning Engineer – Reinforcement Learning & Simulation

Working Arrangement: Full-time, office-based

Compensation: $150,000–$220,000 base salary + equity

We’re partnering with an ambitious, well-funded AI startup working at the intersection of machine learning, reinforcement learning, simulation, and robotics.

The company is building infrastructure to train, evaluate, and improve next-generation AI models, combining advanced simulation environments with substantial real-world robotics infrastructure. Having established a significant operational foundation, the team is now focused on scaling the engineering systems that enable reinforcement learning environments to be developed, tested, and deployed at pace.

This is an opportunity to join a small, exceptionally driven technical team at an early stage, with significant ownership over core systems and the chance to help shape engineering standards as the company grows.

We’re looking for a strong Machine Learning Engineer with excellent technical fundamentals and an interest in reinforcement learning, simulation, and AI systems.

Your primary focus will be scaling the production of reinforcement learning environments, turning research requirements into robust, efficient, and maintainable software systems. You’ll work closely with researchers to build the infrastructure needed to run simulations at scale and support the training and evaluation of advanced AI models.

This is a highly engineering-focused role. You won’t necessarily need to develop novel reinforcement learning algorithms or have an extensive robotics background. Instead, we’re looking for someone who can build excellent software, solve difficult technical problems, and learn new concepts quickly.

For more experienced candidates, there is also scope to take on greater technical ownership, mentor other engineers, and help establish the engineering practices that will support the team's next phase of growth.

• Build, scale, and product ionise reinforcement learning environments used to train and evaluate advanced AI models.
• Translate research ideas and experimental requirements into reliable, high-performance engineering systems.
• Develop infrastructure to run simulation environments efficiently and at scale.
• Improve software quality, maintainability, testing, and engineering standards across the team.
• Collaborate with research colleagues to identify which environments and approaches deliver measurable improvements in model performance.
• Support model post-training, evaluation, and benchmarking where appropriate.
• Help validate simulation results through real-world model evaluation and testing on robotic systems.
• Identify opportunities to improve performance, scalability, and the efficiency of development workflows.
• Take ownership of technical challenges in a fast-moving environment where priorities can evolve quickly.

The team places significant emphasis on technical ability, problem-solving, learning velocity, and ownership rather than a rigid checklist of previous experience.

You’ll ideally bring:

• Strong software engineering fundamentals and the ability to build clean, reliable, production-quality systems.
• A strong general technical background, with an interest in machine learning, AI, or related disciplines.
• Excellent analytical and problem-solving skills.
• The ability to learn unfamiliar technologies and technical concepts quickly.
• A practical, hands-on approach to engineering, with the initiative to take projects from concept through implementation.
• High levels of ownership, adaptability, and intellectual curiosity.
• The motivation to work closely with a small, ambitious team tackling technically challenging problems.