Senior Machine Learning Engineer – Reinforcement Learning

**Job Title:** **Senior Machine Learning Engineer – Reinforcement Learning** **Location: 100% remote anywhere in Canada** **Duration:** **Full\-time/Contract** **Job Description:** **Job Title: Role Overview** We are seeking a highly skilled **Senior Machine Learning Engineer** with deep expertise in **Reinforcement Learning (RL)** to design, build, and deploy scalable ML solutions for real\-world applications. This role requires strong foundations in machine learning and data science, combined with hands\-on experience in developing and productionizing RL models. You will work closely with cross\-functional teams to translate complex business problems into AI\-driven solutions, leveraging modern cloud platforms and MLOps practices. **Key Responsibilities** **Reinforcement Learning \& Model Development** * Design, develop, and deploy **Reinforcement Learning solutions at scale** for real\-world use cases. * Implement and customize RL algorithms such as **PPO, DQN, SAC** , and others based on problem requirements. * Build end\-to\-end ML pipelines including **data selection, feature engineering, model training, evaluation, and deployment** . **Architecture \& Optimization** * Architect scalable RL/ML systems using **cloud\-native tools and distributed computing frameworks** . * Optimize models for **performance, latency, and scalability** in production environments. * Develop custom ML/RL code tailored to business\-specific challenges. **Production \& Engineering Excellence** * Build and deploy **production\-grade ML systems** with strong emphasis on reliability and maintainability. * Integrate ML models into backend systems via APIs or microservices. * Ensure adherence to **CI/CD pipelines, testing frameworks, and version control best practices** . **Data Science \& Experimentation** * Conduct experiments using Python\-based ML libraries to validate model performance. * Analyze datasets and define **data requirements (volume, structure, quality)** for RL models. * Apply a hypothesis\-driven approach to improve model outcomes. **Collaboration \& Consulting** * Translate ambiguous business requirements into **scalable ML solutions** . * Collaborate with engineering, product, and business teams to deliver impactful outcomes. * Communicate complex technical concepts clearly to both technical and non\-technical stakeholders. **Required Qualifications** * 5\+ years of experience in **Machine Learning Engineering** , with strong focus on **Reinforcement Learning** * Proven experience building and deploying **RL models in real\-world applications** * Deep understanding of RL training processes, reward design, and convergence challenges * Hands\-on experience with RL algorithms such as **PPO, DQN, SAC, or similar** * Strong proficiency in **Python** and ML frameworks such as **PyTorch or TensorFlow** * Experience with **distributed RL frameworks** (e.g., Ray RLlib) is highly preferred * Solid understanding of **data pipelines, feature engineering, and ML experimentation workflows** * Experience building **scalable backend systems and APIs** for ML integration **Preferred Qualifications** * Experience with **cloud platforms** such as AWS, GCP, or Azure * Familiarity with **MLOps practices** (model versioning, monitoring, reproducibility, pipelines) * Experience integrating ML models using frameworks such as **Flask or FastAPI** * Exposure to **Computer Vision applications** or multi\-modal data in RL contexts * Experience in **high\-scale or fast\-growing (startup/scale\-up) environments** * Relevant certifications (e.g., **Google Cloud ML Engineer, AWS Solutions Architect** )