Senior Machine Learning Engineer

Starboard Recruitment; Canada; Remote; CAD 100,000 - 150,000; Full time

Job summary

A leading tech recruitment agency is seeking an experienced Senior Machine Learning Engineer for a high-growth AI startup in Canada. The successful candidate will define AI strategies, manage machine learning lifecycles, and implement innovative ML solutions. The role requires extensive experience in ML engineering, with expert-level skills in Python, and familiarity with geospatial data analysis tools. A Master's or Ph.D. in Computer Science or a related field is essential.

Qualifications

• 7+ years of hands-on experience in machine learning engineering.
• Expert-level Python programming skills.
• Deep knowledge of ML frameworks and algorithms.

Responsibilities

• Define and execute AI strategy focused on geoscientific applications.
• Manage the entire machine learning lifecycle.
• Drive the adoption of MLOps frameworks.

Skills

Python programming

Machine Learning frameworks (PyTorch, scikit-learn)

Cloud computing (AWS)

Data security and privacy

Geospatial data analysis

Education

Master’s or Ph.D. in Computer Science or Machine Learning

Tools

PostGIS

GeoPandas

CI/CD tools

Job description

Opportunity is with one of Canada's fastest growing, well-funded, Series-B tech startups in the AI / ML domain.

Starboard Recruitment, on behalf of our client, is searching for an experienced Sr Machine Learning Engineer.

Key Responsibilities

• AI Strategy Development – Partner with the Director of R&D to define and execute the company’s AI strategy, focusing on geoscientific applications.

• Full-Cycle ML Leadership – Manage all aspects of the machine learning lifecycle, from data preprocessing to model deployment and performance monitoring, ensuring a streamlined and effective process.

• Innovative ML Architectures – Design and implement a broad spectrum of machine learning solutions, spanning computer vision, time series forecasting, and geospatial data analysis, while integrating cutting-edge technologies and methodologies.

• MLOps Best Practices – Drive the adoption of robust MLOps frameworks, including CI/CD pipelines for ML models, to enable smooth and scalable AI deployments.

• AI Infrastructure & Optimization – Enhance AI infrastructure and workflows, focusing on performance, scalability, data pipeline efficiency, and automation across all ML processes.

• Cross-Disciplinary Collaboration – Work closely with data engineers, scientists, and geoscientists to establish a well-integrated, end-to-end ML ecosystem within the company.

• Continuous AI Advancement – Regularly improve the efficiency, reliability, and impact of AI-driven systems through iterative optimizations and refinements.

• Geospatial ML Expertise – Familiarity with geospatial databases such as PostGIS and GeoPandas is highly desirable.

Qualifications

Experience:

• At least 7 years of hands-on experience in machine learning engineering, with a strong record of successfully deploying ML solutions into production environments.

Technical Proficiency:

• Expert-level Python programming skills and deep knowledge of ML frameworks, including PyTorch, scikit-learn, and inference engines like ONNX Runtime and OpenVINO.

• Strong grasp of various ML algorithms, architectures, and their real-world applications.

• Experience working with large-scale datasets and cloud computing environments, particularly AWS.

• Proficiency in software engineering best practices, version control systems, and CI/CD methodologies.

• Hands-on experience with containerization, orchestration, and microservices-based architectures.

• Solid understanding of data security, privacy considerations, and compliance requirements in AI-driven applications.

Leadership & Soft Skills:

• Proven ability to lead and mentor ML teams through complex projects.

• Strong analytical and strategic thinking skills to solve challenging AI problems.

• Exceptional communication skills, capable of conveying technical concepts to both technical teams and executive stakeholders.

• Strong project management capabilities, with the ability to oversee multiple initiatives simultaneously.

• Passion for continuous learning and adaptability in the ever-evolving field of machine learning.

Education:

• Master’s or Ph.D. in Computer Science, Machine Learning, or a related discipline. Industry certifications and contributions to the ML community (such as research publications or open-source projects) are a strong plus.