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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.
Our team will reach out to qualified candidates and discuss in further detail.

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.

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.
  Follow Starboard Recruitment on LinkedIn for ongoing job opportunities, market updates and advice: https://www.linkedin.com/company/starboard-recruitmen

• 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.