Senior Machine Learning Engineer

Location: Remote, with flexibility to work in an office environment

Type: Independent Contractor (Part-Time)

Reports To: VP, AI

About Creative Destruction Lab (CDL)

Creative Destruction Lab (CDL) is a nonprofit organization that delivers an objectives-based program for massively scalable, seed-stage, science- and technology-based companies around the world. Our mission is to enhance the commercialization of science for the betterment of humankind.

CDL operates across a global network of sites, programs, and partners. CDL designs and deploys advanced technology systems – including applied machine learning and AI tools – to support program delivery, decision-making, and internal platforms at scale. Founded by Professor Ajay Agrawal in 2012 at the University of Toronto’s Rotman School of Management, the program has expanded and now has 16 sites across ten countries: Toronto, Vancouver, Calgary, Montreal, Halifax, Paris, Madison, Seattle, Estonia, Berlin, Melbourne, College Station, Milan, London, San Sebastian, and Doha.

Position Summary

The Senior Machine Learning Engineer (Part-Time) supports the design, development, and deployment of advanced machine learning and large language model (LLM)–powered systems used across CDL’s internal platforms and applied AI initiatives.

This role is hands-on and execution-focused, working across research, experimentation, and production. The Senior Machine Learning Engineer partners closely with technical and operational stakeholders to translate real-world needs into reliable, observable, and maintainable ML systems.

Key Responsibilities

Model Development & Optimization

  • Design, train, and evaluate machine learning models applied to real-world product, workflow, or decision systems.
  • Apply reinforcement learning techniques to improve model behavior and performance in practical settings.
  • Define evaluation metrics and iterate on models using data-driven feedback loops.

LLM Adaptation & Fine-Tuning

  • Adapt and extend pretrained large language models to improve performance on domain-specific tasks.
  • Apply supervised fine-tuning and reinforcement learning–based fine-tuning approaches where appropriate.
  • Evaluate tradeoffs between prompting, retrieval, fine-tuning, and orchestration strategies.

AI Systems & Production Deployment

  • Design, build, and ship production-ready ML- and LLM-powered applications.
  • Implement multi-step reasoning, tool use, and agent-based workflows using modern orchestration frameworks (e.g., PydanticAI, LangGraph, or equivalent).
  • Ensure solutions are robust, maintainable, and suitable for deployment in live environments.

Evaluation, Observability & Iteration

  • Apply best practices in context engineering and eval-driven development.
  • Integrate logging, monitoring, and observability tools to track system behavior and diagnose issues.
  • Use automated or semi-automated optimization frameworks to improve performance over time.

Collaboration & Technical Support

  • Work closely with CDL’s technology and operations teams to support active projects and initiatives.
  • Translate operational requirements into technical implementations.
  • Contribute to technical documentation and knowledge sharing as needed.

Core Qualifications

  • Strong fundamentals in machine learning, with hands-on experience training reinforcement learning models applied to real-world product, workflow, or decision systems.
  • Demonstrated experience adapting pretrained large language models to improve task performance using approaches such as:
    • Supervised fine-tuning (e.g., task-specific classifiers using models such as GPT-4.1 or equivalent)
    • Reinforcement learning–based fine-tuning (e.g., reinforcing structured or chain-of-thought reasoning using lightweight or specialized models such as o4-mini or equivalent)
  • Proven ability to design, build, and ship production AI agent or LLM-powered applications, including:
    • Tool use
    • Multi-step reasoning
    • Agentic or workflow-based architectures
  • Experience working with modern orchestration frameworks for LLM systems (e.g., PydanticAI, LangGraph, or similar).
  • Strong skills in context engineering and prompt/system design.
  • Comfort applying eval-driven development practices to iteratively improve model and system performance.
  • Experience working with logging and observability platforms for AI systems (e.g., Logfire or equivalent).
  • Familiarity with automated or semi-automated optimization frameworks (e.g., DSPy or similar).
  • Strong Python skills and experience with modern ML tooling and libraries.
  • Ability to work independently and deliver high-quality work in a part-time, asynchronous environment.
  • Clear written and verbal communication skills.

Application Process

Interested candidates should submit a resume, cover letter, and references to amarpreet.kaur@creativedestructionlab.com. Please include “Senior Machine Learning Engineer” in the subject line.

Applications will be reviewed, and candidates will be contacted for an interview if their experience aligns with the role.

At Creative Destruction Lab, we engage skilled individuals regardless of race, religion, colour, national origin, sex, disability, age, sexual orientation, and other protected statuses in keeping with human rights legislation. We are a diverse mix of talented professionals who want to do their best work. We pride ourselves on bringing the best talent to work for our clients, and we know our company runs on the hard work and dedication of our passionate and dedicated staff. Please notify us anytime during the hiring process if you require accommodation, and we will work with you to provide a suitable accommodation solution.