Senior Machine Learning Engineer, Canoe

• Minimum of 5 years of experience in applied ML engineering, with a focus on NLP, information extraction, or LLMs
• Proficiency in Python and relevant machine learning libraries (e.g., TensorFlow, PyTorch)
• Strong understanding of MLOps (Docker, Kubernetes, CI/CD for ML, experiment tracking)
• Proficiency with AI-assisted development tools (e.g., GitHub Copilot, Claude Code agent) to accelerate software development, prototyping, testing, and deployment of ML solutions
• Problem-solver with a product mindset and bias toward outcomes
• Excellent communication skills; able to partner across engineering, product, and business teams
• Comfortable in fast-paced, agile startup environments
• Bachelor’s degree in computer science or related field

Desirable

• We are looking for a Senior Machine Learning Engineer to design and deploy models that make sense of highly complex, unstructured financial documents, enabling us to deliver data with unprecedented accuracy, speed, and trust
• You’ll work hands-on with LLM and other ML Models, helping scale Canoe’s platform while shaping how alternative investment firms interact with their data
• Design, train, and evaluate ML models for document classification, entity extraction, summarization, and information retrieval
• Fine-tune and optimize large language models for domain specific use cases, optimizing their performance for accuracy, efficiency, and scalability
• Work closely with data engineering teams to preprocess and engineer features from large datasets to enhance the performance of machine learning models
• Build scalable, production-ready ML services with strong observability, monitoring, and retraining capabilities
• Contribute to Canoe’s MLOps stack, including CI/CD for models, feature stores, evaluation frameworks, and data versioning
• Collaborate with product managers, software engineers, and other stakeholders to integrate machine learning models into end-to-end solutions
• Stay current with advancements in LLMs, Agentic AI, and ML, and translate new research into practical improvements to Canoe’s technology stack
• Conduct code reviews to ensure code quality and provide mentorship to junior members of the machine learning team