Senior Machine Learning Engineer – Reinforcement Learning

Job Title

Senior Machine Learning Engineer – Reinforcement Learning

Location

100% remote anywhere in Canada

Duration

Full-time/Contract

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 )