Senior Machine Learning Engineer, Klue
CA$170-200k
+ Equity
More information about location
2+ days a week in office
Competitive enablement software
Job no longer available
Competitive enablement software
101-200 employees
Job no longer available
CA$170-200k
+ Equity
More information about location
2+ days a week in office
101-200 employees
Company mission
To enable revenue teams with the insights they need to win, when they need it, and wherever they live.
Role
Who you are
- Masters or PhD in Machine Learning, NLP, or related field
- 2+ years building and optimizing retrieval systems
- 2+ years training/fine-tuning transformer models
- Deep understanding of LLMs, retrieval metrics and their trade-offs
- Implement memory and tool-use strategies to enhance LLM-based agent capabilities
- Experience building end-to-end systems as a Platform Engineer, MLOps Engineer, or Data Engineer
- Strong understanding of software testing, benchmarking, and continuous integration
- Build scalable, production-ready ML pipelines for training, evaluation, deployment and monitoring
- Develop and implement CI/CD pipelines. Automate the deployment and monitoring of ML models
- Knowledge of query augmentation and content enrichment strategies
- Expertise in automated LLM evaluation, including LLM-as-judge methodologies
- Skilled at prompt engineering - including zero-shot, few-shot, and chain-of-though
- Proven ability to balance scientific rigor with driving business impact
- Take ownership and run with ambiguous problems
- Jump into new areas and rapidly learn what's needed to deliver solutions
- Bring scientific rigor while maintaining a pragmatic delivery focus
- See unclear requirements as an opportunity to shape the solution
What the job involves
- We're looking for a Senior Machine Learning Engineer to join our ML Foundation and Platform team in Toronto, focusing on building and optimizing state-of-the-art LLM-powered agents that can reason, plan and automate workflows for users
- You'll be joining us at an exciting time as we reinvent our insight generation systems, making this an excellent opportunity for someone with strong ML and IR fundamentals who wants to dive deep into practical LLM applications
- As a member of our team, you'll be focusing on optimizing LLM-based agents, creating a platform for other teams to utilize ML capabilities and deploying ML services to production
- You'll measure and improve retrieval systems across the spectrum from BM25 to semantic search and develop comprehensive evaluation metrics to measure their performance
- A key challenge will be developing optimal chunking and enrichment strategies for diverse data sources including news articles, website changes, documents, CRM entries, call recordings and internal communications
- You'll explore how different data types and formats impact retrieval performance and develop strategies to maintain high relevance across all sources
- Beyond agents and retrieval, you'll work on building a platform for other teams to effectively utilize LLM tools and take advantage of prompt engineering
- This includes developing APIs and scalable systems, developing scalable tools and services to handle machine learning training and inference for our clients, writing zero-shot and few-shot prompts with structured inputs/outputs, and implementing benchmarking systems for prompts
- You'll also work on training and fine-tuning smaller, more efficient models that can match the performance of LLMs at a fraction of the cost
- This includes creating labeled datasets (sometimes using prompts), conducting careful hyperparameter optimizations, and building automated training pipelines
- You'll also deploy and monitor these models in production, optimize their latency, and implement comprehensive offline/online metrics to track their performance
- Throughout all this work, you'll apply your deep understanding of the latest breakthroughs to build scalable, production-ready systems that turn cutting-edge ML experiments into reliable business value
Our take
The integration of market data into the many functions of enterprise has been a consistent trend in SaaS markets for a number of years. Klue is taking a similar approach for teams looking to understand competitive landscapes.
The startup is uniquely attempting to create a new category of ‘competitive enablement software’. Whereas other software companies sought to fix a company’s business intelligence needs or content placement strategies, Klue brings all of these services together under the lens of competitive positioning.
Building on this vision, Klue is now doubling down on an AI-first approach to make competitive enablement more automated and scalable. Recent acquisitions such as Ignition and Goldpan are strengthening Klue's agentic AI capabilities, allowing the platform to surface competitive insights, buyer feedback, and win-loss analysis with far less manual input.
While Klue has historically leaned toward enterprise customer supported by specialists, it's shift toward AI-driven delivery signals an effort to reduce complexity, improve efficiency, and extend the value of competitive enablement to a broader range of companies.
Freddie
Company Specialist at Welcome to the Jungle
Insights
9% employee growth in 12 months
Company
Funding (last 2 of 6 rounds)
Mar 2023
$2m
GRANT
Dec 2021
$62m
SERIES B
Total funding: $89.3m
Company HQ
Downtown Vancouver, Vancouver, BC
Leadership
Jason Smith
(Co-Founder & CEO)Key roles include President at Vision Critical, a global market research software leader, and co-founder of Columbus Group, a digital agency pioneer acquired by TELUS. Advisor and consultant to early-stage companies. Stint as a VP at Electronic Arts.
Sarathay Naicker
(Co-Founder & CTO)Prior to Klue, Sarathay rose to become the Chief Technologist at major cloud company, Sophos. Prior to that Sarathay was a senior developer at ActiveState until 2003.
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