Sr. Machine Learning Engineer

Canoe Intelligence; New York (NY); Hybrid; USD 180,000 - 220,000; Full time

Benefits offered by this job

Medical, dental, vision benefits

Flexible PTO

401(k)

Home office stipend

Education assistance

Parental Leave

Job summary

Canoe Intelligence is seeking a Senior Machine Learning Engineer to design and deploy advanced models that enhance the analysis of unstructured financial documents. The role involves collaboration with engineering teams to build scalable systems, while utilizing expertise in machine learning and AI.

With a competitive salary range of $180,000 to $220,000, applicants should have significant experience in applied ML engineering. The position offers flexible hybrid work arrangements between New York and London.

Qualifications

• Minimum of 5 years of experience in applied ML engineering, focus on NLP or LLMs.
• Proficiency with AI‑assisted development tools (e.g., GitHub Copilot).
• Experience training and deploying large language models.

Responsibilities

• Design, train, and evaluate ML models for document classification and information retrieval.
• Fine-tune and optimize large language models for specific use cases.
• Collaborate with teams to integrate machine learning models into solutions.

Skills

Proficiency in Python

Experience in applied ML engineering focused on NLP

Strong understanding of MLOps

Excellent communication skills

Problem solving with a product mindset

Education

Bachelor's degree in computer science or related field

Tools

TensorFlow

PyTorch

Docker

Kubernetes

Job description

COMPANY: Canoe Intelligence

WEBSITE: https://canoeintelligence.com/

TITLE: Sr. Machine Learning Engineer

LOCATION: New York City or London (hybrid) / Fully Remote in the United States or United Kingdom

SALARY: $180,000 - $220,000 (based on NYC, will be adjusted for geo)

The Role

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.

What You’ll Do

• 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.

What We’re Looking For

• 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.

Preferred

• Master Degree or PhD in computer science or related field
• Experience in training and deploying large language models.
• Familiarity with cloud computing platforms and distributed computing.
• Familiarity with modern ML Ops tools such as Modal, Weights and Biases, Sagemaker, etc.
• Experience with LLM fine‑tuning techniques such as LoRA, QLoRA, or parameter‑efficient training frameworks (e.g., Unsloth).

What You’ll Get

• Medical, dental, vision benefits
• Flexible PTO
• 401(k)
• Flexible work from home policy
• Home office stipend
• Employee Assistance Program
• Gym/Wifi reimbursement
• Education assistance
• Parental Leave

Our Values

• Client First —> Listen, and deliver client‑centric solutions
• Be An Owner —> Take initiative, improve situations, drive positive outcomes
• Excellence —> Always set the highest standard for yourself and others
• Win Together —> 1 + 1 = 3

Who We Are

Canoe is reimagining alternative investment data processes for hundreds of leading institutional investors, capital allocators, asset servicing firms and wealth managers. By combining industry expertise with the most sophisticated data capture technologies, Canoe’s technology automates the highly‑frustrating, time‑consuming, and costly manual workflows related to alternative investment document and data management, extraction and delivery. With Canoe, clients can refocus capital and human resources on business performance and growth, increase efficiency, and gain deeper access to their data. Canoe’s AI‑driven platform was developed in 2013 for Portage Partners LLC, a private investment firm.

Canoe is an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.