Machine Learning Engineer

Summary

Join CoLab, a groundbreaking team driving innovation in the tech industry, as a Machine Learning Engineer. You will play a pivotal role in developing and deploying cutting-edge machine learning models, ensuring they are production-ready, scalable, and maintainable. This role involves collaborating with engineering, platform, and product teams to integrate ML solutions and achieve key business objectives. You will work in a fast-paced SaaS environment, blending data science, engineering, and operational excellence. CoLab offers competitive compensation, comprehensive benefits, and a strong commitment to work-life balance, including remote work options within Canada. We encourage applications even if you don't meet every qualification; your potential is what matters most.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Statistics, or a related field
• 3+ years of experience in machine learning and data science in a SaaS environment
• Machine Learning expertise: Proficiency in Python and ML libraries (e.g., PyTorch, Hugging Face, Scikit-Learn) to develop, train, and deploy machine learning models
• Advanced ML techniques: Experience with computer vision and NLP, including modern deep learning architectures like transformers and CNNs
• Database expertise: Hands-on experience with vector databases (e.g., Pinecone, Chroma), relational databases (e.g., PostgreSQL, MySQL), and querying frameworks/APIs
• Cloud and MLOps tools: Hands-on experience with cloud platforms (AWS, GCP, Azure) and tools like MLflow, Kubeflow, and wandb for managing workflows
• Production deployment: Hands-on experience deploying and scaling ML models using cloud-based AI services with a focus on performance and reliability
• Data pipelines: Experience with ETL tools/frameworks for preprocessing and building pipelines
• DevOps practices: Experience with CI/CD pipelines, version control (e.g., Git), and automated testing
• Excellent problem-solving abilities, attention to detail, and the ability to work autonomously in a fast-paced environment
• Strong communication skills with the ability to explain complex concepts to both technical and non-technical audiences
• A growth mindset with a willingness to mentor teammates and improve processes, technology, ways of working, and team culture

Responsibilities

• Build and Train Models: Design, implement, and deploy machine learning models to drive insights and automate business processes
• Feature Engineering: Develop and optimize features for model training using large, complex datasets
• Experimentation: Lead hypothesis-driven analysis and A/B testing to inform model and product development
• Data Storytelling: Communicate findings through compelling visualizations and presentations, translating data into actionable insights for stakeholders
• Model Deployment and Monitoring: Oversee end-to-end model deployment using MLOps best practices, ensuring models are robust, reproducible, and scalable
• Pipeline Automation: Work with Platform Engineering to develop and maintain automated data pipelines to support continuous integration and deployment (CI/CD) for machine learning workflows
• Model Monitoring and Maintenance: Set up monitoring and alerting for model drift, accuracy, and performance to maintain high-quality predictions in production
• Optimize Infrastructure: Work with engineering and platform teams to optimize cloud infrastructure, model serving, and resource allocation
• Cross-functional Collaboration: Partner with product managers, engineers, and other data scientists to integrate ML solutions into the product and deliver on key business objectives
• Data Governance and Security: Ensure compliance with data privacy and security regulations in all aspects of data processing and model deployment
• Continuous Improvement: Advocate for best practices and contribute to the development of reusable frameworks and processes that accelerate the ML lifecycle

Preferred Qualifications

• Familiarity with 3D data and CAD formats for specialized applications
• Experience working on SaaS, large-scale distributed systems would be considered an asset
• Consistent track record of building and maintaining highly scalable products would be considered an asset

Benefits

• This is a full-time, permanent position with an attractive compensation package that includes a stock options package
• This role offers an extended health and benefits package that includes unlimited paid vacation and RRSP matching
• Our main office location is in St. John’s, NL where we offer hybrid and remote opportunities. This role has the flexibility to work from anywhere within Canada