Staff Machine Learning Engineer

We are looking for a Staff Machine Learning Engineer to drive high impact end-to-end ML company initiatives.

At Klue, we build 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. We utilize a broad array of ML techniques, including classification, clustering, recommendation, summarization, prompt engineering, vector search, and retrieval augmented generation.

💡Klue + You?

Q: Klue who?
A: We’re Klue and from a technical perspective, Klue’s mission is to descale huge amounts of data to the human level, so people can process it and make use of it. Klue is that trusted intermediary, right now it’s proven for sales enablement, but tomorrow it’s all teams enablement.

Q: What level of experience are we looking for?
A: Right now, we are looking for a tenured Staff Machine Learning Engineer.

Q: What is the focus of this team?
A: Creating services for collecting, processing and generating timely, relevant intel that is accurately linked to competitors, products, industries, and people. Our Machine Learning Engineers are primarily focused on the training, evaluation, and deployment of models to label, score, cluster, and generate insights.

Q: What projects is the team working on?
A: 1. Retrieval Augmented Generation for Compete - An end-to-end system that ingests a real-time feed of compete content from various sources such as Slack, reviews, websites, news, interviews, sales calls, and documents, to generate relevant and factually accurate content.

1. RAG Evaluation: Dedicated efforts to develop strategies for assessing the quality of RAG systems, focusing on both retrieval and generation and keeping abreast of the rapidly evolving field.
2. Relevancy Scorer: Models that score the relevance of content for each customer, leveraging both implicit and explicit feedback to support recommendation and autojunking.

Q: What technologies do we use?
A: Python, Transformers, Pytorch, Hugging Face, Spacy, Sklearn, Pinecone, Kubeflow, Vertex AI, BentoML, Aporia, JS, PostgreSQL, Elasticsearch, Redis, GCP, BigQuery, Docker/Kubernetes, Github, GPT.
We believe in using whatever tools make sense to get the job done and support our game-changing innovation.

Q: What you'll do:

• Work on multi-year, cross-team technical ML projects.
• Own initiatives end to end: from prototyping to production.
• Set the technical direction throughout the ML development lifecycle: writing project proposals that are both highly impactful and technically feasible.
• Lead technical design discussions, write and review technical design documents, and provide technical guidance and directions to the larger engineering team.
• Head up + be a key contributor to our ML guild: contributing to thought leadership for the ML team and across Klue.

Q: What skills do you bring?

• A: You are an expert on the landscape of transformer models and are proficient with popular ML frameworks such as PyTorch, Hugging Face, or Scikit-Learn.
• You have experience deploying systems that use NLP such as Information Retrieval (IR), Recommender Systems (RecSys), or other NLP Applications
• You stay up to date on recent advances with LLMs and you demonstrate astute judgment in deciding whether to train a model with a custom architecture or leverage GPT.
• You ensure your experiments are reproducible, balancing swift discovery with scientific rigor.
• You are proficient in designing, implementing, and deploying RESTful APIs and you have experience with (non)relational and vector databases.
• You demonstrate good judgment when making architectural decisions and you understand how those decisions fit into the bigger picture.

Q: How do teams work?

• Hybrid. Best of both worlds (remote & in-office)
• Our main Canadian hubs are in Vancouver and Toronto, and most of our teams are located in EST and PST.
• You and your team will be in office at least 2 days per week.

Q: What about total compensation & benefits?

• Benefits. We currently have extended health benefits starting on your 1st day.
• Time off. Take what you need. We want the team to prioritize wellness and avoid burnout. Vacation usually falls into 3 categories: recharging, life-event, & keeping a work-life balance. Just ensure the required work gets done and clear it with your team in advance. You need to take at least two weeks off every year. The average Klue team member takes 2-4 weeks of PTO per year.

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Lastly, 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. Reach out and let’s see if there is a home here for you now or in the future.

We’ve made a commitment to support and contribute to a diverse environment; on our teams and in our community. We’re early in our journey; we've started employee led resource groups, committed to Pay Up For Progress, and use success profiles for roles instead of 'years of experience'. We continue to scale our efforts as Klue grows. We’re proud to be an equal opportunity employer and have dedicated that commitment to our current and future #kluecrew. During the interview process, please let us know if there is anything we need to make more accessible or accommodate to support you to be successful.

All interviews will be conducted via video calls. We work in a hybrid model of WFH (remote) and in-office. We’re excited to meet you and in the meantime, get to know us:

🌈 Pay Up For Progress & 50 - 30 Challenge & Klue Blog
✅✅ Win-Loss Acquisition (2023)
🅰️ Series A (2020)
🐅 Series B (2021)
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