Sr. 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