Affinity stitches together billions of data points from massive datasets to create a powerful, accurate representation of the world's professional relationship graph. Based on this data, we offer our users the insights and visibility they need to nurture and tap into the opportunities in their team's network.

Reporting to our Director of Data & AI, you'll help create the magic that underlies Affinity's industry-leading relationship intelligence. You'll define how Affinity uses our massive datasets (including email messages, calendar events, and data pulled from around the public internet) to extract actionable information and create insights for our users to help them leverage their networks to effectively prospect and source deals, and more! We are early on in our journey of providing these actionable insights for our customers, and this role will be instrumental in driving our progress on this front.

This is an applied machine learning position with a heavy focus on engineering and not a research role. The expectation is that the candidate will be driving the development of our ML Ops and ML Engineering capabilities, as well as doing modeling work. We value candidates who keep up with the latest trends and developments in the LLM ecosystem while delivering projects in iterations to make sure we deploy and ship features to our application. Our iterations are short and deliver value to our customers.

What you’ll be doing:

• Take projects from ideation to production, including feature engineering, model construction, deployment, and model observability and evaluation.
• Have access to and work with Large Language Models.
• Help define our data roadmap. Which problems are amenable to AI/ML techniques, and which are better solved via simpler non-statistical methods? How might we define and evaluate data and model quality? Which models and techniques are best suited to our domain? You'll collaborate with our product and engineering teams to help answer these questions and more.
• Collaborate with the rest of our data pipeline and ML engineering teams to build robust, high-scale systems that underlie all of our data processing and ML Operations.

Qualifications

Don’t meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every qualification. At Affinity, we are dedicated to building a diverse, inclusive, and authentic workplace, so if you’re excited about this role, but your past experience doesn’t perfectly align with the qualifications above, we encourage you to apply anyways. You may be just the right candidate for this or other roles.

Required:

• You have 8+ years of experience in software engineering and/or Machine Learning, with at least 5 years of experience with applied machine learning in production, focused on ML Engineering and ML Ops along with excellent architectural design experience.
• You have 3+ years of experience with NLP techniques.
• You have deep technical experience working with technologies related to multimodal, image extraction/generation, Natural Language Processing, from model fine-tuning to prompt engineering.
• Hands-on experience working with LLMs and/or frameworks like Langchain, Hugging Face, Open AI, Anthropic etc.
• Extensive experience with tools including pandas, scikit-learn, Tensorflow/PyTorch.
• Experience with vector databases.
• Experience with live A/B testing at scale.
• You are a builder and care deeply about building systems and products that deliver meaningful value to users quickly and iteratively.
• You have experience working with large, multi-terabyte datasets, and are comfortable with tools for high-scale data ingestion, transformation, analysis, and prediction.
• You're excited to work collaboratively within engineering and across cross-functional teams.
• You’re eager to contribute your ideas and experiences to help Affinity continuously improve as a product and as a company.

Nice to Have:

• Experience with Databricks
• Experience with the AWS ecosystem
• Familiarity with using ML to improve data quality
• Prior Data engineering experience.
• You have experience with recommendation systems and transfer learning.
• Familiarity with the LLM ecosystem, prompt engineering techniques, evaluation and observability to improve the user experience.

How we work:

Our culture is a key part of how we operate as well as our hiring process:

• We iterate quickly. As such, you must be comfortable embracing ambiguity, be able to cut through it, and deliver incremental value to our customers each sprint.
• We are candid, transparent, and speak our minds while simultaneously caring personally with each person we interact with.
• We make data-driven decisions and make the best decision for the moment based on the information available.

Join us in enabling every professional on the planet to succeed by harnessing the power of their relationships.

If you’d want to learn more about our values click here.

What you’ll enjoy at Affinity:

• We live our values as playmakers, obsessed with learning, care personally about our colleagues and clients, are radically open-minded, and take pride in everything we do.
• Health Care coverage and flexible personal & sick days. We want our team to be happy and healthy :)
• We provide an annual budget for you to spend on education and offer a comprehensive L&D program – after all, one of our core values is that we’re #obsessedwithlearning!
• We support our employee’s overall health and well-being and reimburse monthly for things such as; Transportation, Home Internet, Meals, and Wellness memberships/equipment.
• Virtual team building and socials. Keeping people connected is essential.

Please note that the role compensation details below reflect the base salary only and do not include any equity or benefits. This represents the salary range that Affinity believes, in good faith, at the time of this posting, that it will pay for the posted job.

A reasonable estimate of the current range is $113,800 to $187,800 CAD. Within the range, individual pay depends on various factors including geographical location and review of experience, knowledge, skills, abilities of the applicant.