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.

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• 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)