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Staff Backend Engineer, AI/ML

👋 Klue Engineering is hiring!

We're looking for a Staff 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 Backend and ML fundamentals who wants to dive deep into practical LLM applications. 

💡 FAQ

Q: Klue who?

A: Klue is a VC-backed, capital-efficient growing SaaS company. Tiger Global and Salesforce Ventures led our US$62m Series B in the fall of 2021. We’re creating the category of competitive enablement: helping companies understand their market and outmaneuver their competition. We benefit from having an experienced leadership team working alongside several hundred risk-taking builders who elevate every day.

We’re one of Canada’s Most Admired Corporate Cultures by Waterstone HC, a Deloitte Technology Fast 50 & Fast 500 winner, and recipient of both the Startup of the Year and Tech Culture of the Year awards at the Technology Impact Awards.

Q: What are the responsibilities, and how will I spend my time? 

As a member of our team, you'll be leading the design and implementation of 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. 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 will collaborate cross teams to identify LLM solution needs and shape the team’s technical roadmap

You will be responsible for building machine learning services and data pipelines to automatically extract insights about competitors from both public and internal data sources. Every day, our services process millions of data points, including news articles, press releases, webpage changes, Slack posts, emails, reviews, CRM opportunities, and user actions. You will maintain and develop services that utilize a broad array of ML techniques, including classification, clustering, recommendation, summarization, prompt engineering, vector search, RAG and agentic workflows.

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.

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.

Q: What experience are we looking for? 

  • Expertise in Python

  • 5+ years of software engineering experience 

  • Proven experience leading large cross team initiatives

  • 3+ years building and optimizing retrieval systems

  • Deep understanding of LLMs, retrieval metrics and their trade-offs

  • Experience implementing 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

  • Track record of staying current with ML research and breakthrough papers

Q: What makes you thrive at Klue? 

A: We're looking for builders who:

  • 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

Q: What technologies do we use? 

  • LLM platforms: OpenAI, Anthropic, open-source models

  • ML frameworks: PyTorch, Transformers, spaCy

  • Search/Vector DBs: Elasticsearch, Pinecone, PostgreSQL

  • MLOps tools: Weights & Biases, MLflow, Langfuse

  • Infrastructure: Docker, Kubernetes, GCP

  • Development: Python, Git, CI/CD

How We Work at Klue:

  • Hybrid. Best of both worlds (remote & in-office)

  • Our main Canadian hubs are in Vancouver and Toronto. Ideally, this role would be located in Toronto.

  • You and your team will be in office at least 2 days per week.

Q: What about Compensation & Benefits:

  • Competitive base salary

  • Benefits. Extended health & dental benefits that kick in Day 1

  • Options. Opportunity to participate in our Employee Stock Option Plan

  • Time off. Take what you need. Just ensure the required work gets done and clear it with your team in advance. The average Klue team member takes 2-4 weeks of PTO per year.

  • Direct access to our leadership team, including our CEO

⬇️ ⬇️ ⬇️ ⬇️ ⬇️ ⬇️ ⬇️ ⬇️ ⬇️ ⬇️

Not ticking every box? That’s okay. We take potential into consideration. An equivalent combination of education and experience may be accepted in lieu of the specifics listed above. If you know you have what it takes, even if that’s different from what we’ve described, be sure to explain why in your application.

At Klue, we're dedicated to creating an inclusive, equitable and diverse workplace as an equal-opportunity employer. Our commitment is to build a high-performing team where people feel a strong sense of belonging, can be their authentic selves, and are able to reach their full potential. If there’s anything we can do to make our hiring process more accessible or to better support you, please let us know, we’re happy to accommodate.

We’re excited to meet you and in the meantime, get to know us:

🌈 Pay Up For Progress & 50 - 30 Challenge

✅✅ Win-Loss Acquisition (2023)

🐅 Series B (2021)

🏆 Culture, culture, culture!

🎧 Winning as Women & More!

🐝 About Us

🥅 Product Demo Arena

🔍 Glassdoor

🎥 Youtube

☕️ LinkedIn

🦄 Wellfound (AngelList)

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What You Should Know About Staff Backend Engineer, AI/ML, Klue

Are you ready to take on a pivotal role at Klue as a Staff Backend Engineer, AI/ML? Based in the vibrant city of Toronto, you'll be part of our ML Foundation and Platform team, diving into the fascinating world of large language models (LLMs) to create cutting-edge AI solutions. At Klue, we’re on a mission to revolutionize how companies gain competitive insights, and we need your expertise to lead the charge. In this dynamic position, you'll spearhead the design and implementation of LLM-powered agents, transforming abstract concepts into practical applications that automate workflows for users. Say goodbye to mundane tasks as you leverage your backend and machine learning skills to build robust systems that manage massive data streams and process millions of data points daily. Collaborating with cross-functional teams, you'll develop scalable APIs, create ML services, and optimize retrieval systems for improved efficiency. Your hands-on experience with Python and profound understanding of LLMs will play an essential role in ensuring our platform not only meets but exceeds our clients' needs. Klue isn't just another tech company; we are an award-winning SaaS provider committed to fostering a culture of innovation and inclusivity. Join us on this exciting journey to shape the future of competitive enablement!

Frequently Asked Questions (FAQs) for Staff Backend Engineer, AI/ML Role at Klue
What responsibilities can I expect as a Staff Backend Engineer at Klue?

As a Staff Backend Engineer at Klue, you will lead the design and implementation of LLM-powered agents, focusing on developing a robust platform for ML services. This involves creating APIs, optimizing retrieval systems, and building scalable tools to facilitate machine learning training and inference. Your role will also include collaborating across teams to gauge LLM application needs and guiding the technical roadmap accordingly.

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What qualifications are needed for the Staff Backend Engineer position at Klue?

To be a successful candidate for the Staff Backend Engineer role at Klue, you should have over 5 years of software engineering experience, particularly in backend development. You will need to demonstrate deep knowledge of machine learning concepts and large language models, as well as expertise in Python. Experience in building scalable systems and CI/CD pipelines is crucial, along with a track record of leading cross-functional initiatives.

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What technologies does Klue use for its machine learning projects?

At Klue, we leverage state-of-the-art technologies including LLM platforms like OpenAI and Anthropic, along with ML frameworks such as PyTorch and spaCy. For storage, we utilize Elasticsearch and PostgreSQL, ensuring optimal performance for our data-intensive applications. Familiarity with tools like Docker, Kubernetes, and cloud services like GCP is also beneficial in this role.

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What can I expect in terms of work culture at Klue?

Klue prides itself on a hybrid work environment where flexibility is a core value. Our Toronto hub encourages collaboration while allowing remote work options. We emphasize inclusivity and diversity, maintaining a corporate culture that empowers all employees to contribute their unique skills and perspectives.

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What are the compensation and benefits offered for the Staff Backend Engineer role at Klue?

As a Staff Backend Engineer at Klue, you can expect a competitive salary, immediate health and dental benefits, and the chance to participate in our Employee Stock Option Plan. We also value work-life balance with a supportive vacation policy where teammates can take time off as needed, provided it's coordinated with the team.

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Common Interview Questions for Staff Backend Engineer, AI/ML
Can you describe your experience with large language models?

When discussing your experience with large language models, focus on the specific projects you've worked on, the technologies you used, such as Transformer models, and any challenges you faced. Highlight how you applied LLMs to solve real-world problems, and make sure to mention any performance metrics or outcomes that demonstrate your impact.

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How do you approach designing scalable API services?

To tackle the design of scalable API services, emphasize your understanding of RESTful principles and microservices architecture. Discuss your experience with load balancing, caching strategies, and database design. Share examples of APIs you have built, paying attention to how you ensured they remained robust and efficient under varying loads.

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What techniques do you use to optimize retrieval systems?

Discuss the strategies you employ to optimize retrieval systems, like BM25 and semantic search enhancements. Highlight your experience with ranking algorithms and how you assess retrieval effectiveness using various metrics. Offering specific examples of retrieval systems you have improved will showcase your ability to enhance system performance.

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Can you explain the concept of zero-shot and few-shot learning?

When addressing zero-shot and few-shot learning, define them clearly and explain their significance in machine learning. Discuss any practical applications you’ve implemented these techniques in, and elaborate on the methodologies you used to ensure effective results, such as prompt engineering.

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How do you handle data pipeline automation?

Explain your experience with automating data pipelines, mentioning tools and frameworks you've worked with, such as Apache Airflow or MLflow. Talk about your approach to error handling, logging, and monitoring during the automation process, ensuring that you highlight how you maintain data integrity and pipeline performance.

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What are some best practices for software testing in ML applications?

In your response, highlight methods like unit testing, integration testing, and end-to-end testing specific to ML models. Discuss your familiarity with testing frameworks and strategies for validating model performance, as well as the importance of keeping your testing suite updated as your models evolve.

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How do you balance scientific rigor with practical delivery?

When answering this question, illustrate your approach to ensuring scientific rigor while meeting delivery deadlines. Discuss how you prioritize tasks, involve stakeholders early, and iteratively test and validate your solutions to achieve both high-quality outputs and timely delivery.

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What is your experience with CI/CD pipelines in ML projects?

Talk about your hands-on experience setting up CI/CD pipelines for deploying ML models. Highlight the tools you've used, the challenges you encountered, and your strategies for integrating continuous monitoring and feedback loops. This illustrates your understanding of the entire ML lifecycle and how you ensure smooth deployments.

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Can you share an example of a machine learning project you led?

When sharing a project example, provide context around the problem you aimed to solve, the data you used, the methods and models you applied, and the outcome of the project. Highlight how your leadership skills contributed to the project's success, including team collaboration and communication.

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How do you keep updated with the latest ML research?

Share specific strategies you employ to stay current with ML advancements, such as following influential researchers, attending conferences, or participating in online courses and webinars. Discuss how you apply this knowledge in your work and how it influences your decision-making and problem-solving.

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