Machine Learning Engineer

About Us Founded in 2011, Hatch Innovations is based in Vancouver and has an international team with over 80 people located across the globe. We’re creative thinkers who have a passion for technology and are constantly thinking of innovative solutions to help better serve your business and customers. Our clients include video game publishers such as Electronic Arts, Epic Games and Krafton. EA - Project Air: Project Air is a new kind of mobile platform — part creative stage, part storytelling tool, part social experience — powered by AI and real-time interaction. Imagine the creative freedom and discovery of TikTok, the emotional texture of Pixar, and the emergent storytelling of the Sims, fused into something entirely new. We're building a space where people don't just watch or post — they create, perform, and co-invent new characters, stories, and worlds. It's expressive, emotional, and deeply interactive. About the Role We are seeking a highly skilled Machine Learning Engineer focused on MLOps to bridge the gap between model research and real-world application. Your primary responsibility will be to design, build, and maintain the robust, scalable infrastructure and pipelines required to deploy, monitor, and manage machine learning models in production environments. If you excel at transforming prototypes into reliable, low-latency services used by millions, this role is for you. You will: - Production Deployment: Take cutting-edge models and prototypes developed by Data Scientists and Researchers and transform them into production-ready, high-availability services (e.g., APIs, microservices). - ML Pipeline Development: Architect and build automated, scalable pipelines for the entire ML lifecycle, including data ingestion, feature engineering, model training, validation, testing, and deployment. - Performance Optimization: Optimize models and serving infrastructure for low latency, high throughput, and efficient resource utilization at scale. - MLOps & Observability: Implement comprehensive MLOps practices, including monitoring tools for model drift, data quality, and service health; build systems for automated retraining; and ensure robust logging and observability. - Infrastructure: Work closely with engineering teams to leverage and integrate models into our core services and applications. - Maintainability & Quality: Write clean, well-tested, and maintainable code, adhering to software engineering best practices and contributing to code reviews. You Have: Core Experience - 3+ years of professional experience in a software engineering or machine learning engineering role. - Strong foundational skills in Software Engineering and Systems Design (Golang, Python, or similar). - Proven experience building and operating robust data and ML pipelines using tools like Apache Airflow, Kubeflow, or similar orchestration platforms. - Familiarity with deep learning frameworks such as TensorFlow or PyTorch. MLOps & Infrastructure - Expertise with cloud platforms (e.g., AWS, GCP, Azure) and their managed ML services. - Experience with containerization technologies (Docker) and orchestration platforms (Kubernetes). - Strong understanding and experience with CI/CD pipelines for both software and ML model deployment. - Familiarity with ML lifecycle management tools like MLflow, Weights & Biases, or DVC. Nice to Haves: - A pragmatic mindset, focused on operational reliability and scalability (asking: "How do we make this model work reliably in production at scale?"). - Solid understanding of statistical concepts and machine learning fundamentals. - Excellent communication skills, capable of collaborating effectively with data scientists, product managers, and software engineers. - Bachelor's degree in Computer Science, Engineering, or a related quantitative field, or equivalent practical experience. How to Apply: - To apply, please send your PDF resume and Github profile. Job Types: Permanent, Full-time Schedule: Monday to Friday Pay: $150K to $200k per year We Offer: - Health Spending Account - Disability insurance - Life insurance - Paid time off - Work from home #J-18808-Ljbffr