Machine Learning Engineer

Machine Learning Engineer (Reinforcement Learning) • 3-Month Contract • Remote (Must be US based – Eastern Time) • $50-60 W2 / $50-$70 C2C Per Hour This is a high-impact freelance project for an organisation that is actively deploying cutting-edge Reinforcement Learning solutions into production. You will take the lead on complex RL work, operating with a high level of autonomy while still collaborating closely with a senior technical team. Our client is a specialist data and AI consultancy that partners with global enterprises to build advanced, production-grade machine learning solutions. Their projects span experimentation, large-scale deployment and optimisation of models that solve real-world business problems. You will be joining a technically strong team that values clear communication, ownership and high-quality engineering. This is an initial 3 month contract, with strong potential to extend for the right contractor. The Role and Deliverables: • Design, build and deploy large-scale Reinforcement Learning solutions, from data selection and model training through to productionisation. • Interpret and refine high-level or vague requirements, translating them into robust RL models that solve real business problems. • Run ML and RL experiments using Python and modern machine learning libraries, validating approaches with real-world data. • Optimise RL solutions for performance and scalability, ensuring they can operate reliably in production environments. • Implement bespoke RL code and architectures, leveraging Google Cloud tools and services where appropriate. • Contribute to surrounding data engineering and MLOps practices, supporting efficient data flows and automated ML workflows where needed. Your Skills & Experience: • Strong experience as a Machine Learning Engineer with a clear focus on Reinforcement Learning in real-world projects. • Proven track record designing and implementing RL algorithms on non-synthetic datasets, with a solid understanding of data quantity, type and schema requirements. • Deep understanding of RL training procedures, including timelines, stability considerations and evaluation. • Hands-on experience selecting, adapting and applying RL algorithms such as SAC, DQN, PPO or similar for novel use cases. • Proficiency in Python for backend development, delivering production-ready, well-tested code within CI/CD pipelines. • Practical experience building RL solutions with Python-based ML libraries such as PyTorch or TensorFlow, ideally including distributed RL frameworks such as Ray RLLib. • Experience applying RL to Computer Vision problems or working with Computer Vision data. • Ability to lead by example, working highly independently, communicating clearly and supporting other team members when needed. • Strong communication and stakeholder skills, comfortable explaining complex RL concepts to both technical and non-technical audiences. • Bonus points for experience in the gaming industry, cloud platforms such as Google Cloud, AWS or Azure, exposure to MLOps and data engineering, and cloud certifications.