Staff Machine Learning Scientist

Fin is the AI Customer Agent company on a mission to help businesses provide perfect customer experiences. Our AI Agent Fin is the highest-performing AI Customer Agent on the market today, enabling businesses to deliver impeccable, always-on customer support across the customer journey – from service, to sales, to ecommerce. Powered by our own AI models, Fin resolves complex customer issues end-to-end across every channel, with minimal set-up and integration. Fin can also be combined with our natively integrated Intercom help desk for one single system that is designed to meet the needs of modern day support teams. Founded in 2011, Fin became one of the fastest growing companies and remains one of the largest private software companies in the world with nearly 30,000 global businesses using our products to transform their customer support. Driven by our core values, we push boundaries, build with speed and intensity, and relentlessly deliver incredible value to our customers. What's the opportunity? Fin's Machine Learning team is responsible for defining new ML features, researching appropriate algorithms and technologies, and rapidly getting first prototypes in our customers’ hands. We are an extremely product focussed team. We work in partnership with Product and Design functions of teams we support. Our team's dedicated ML product engineers enable us to move to production fast, often shipping to beta in weeks after a successful offline test. We are very passionate about applying machine learning technology, and have productized everything from classic supervised models, to cutting-edge unsupervised clustering algorithms, to novel applications of transformer neural networks. We test and measure the real customer impact of each model we deploy. What will I be doing? - Play an active role in hiring, mentoring and career development of other engineers - Raise the bar for technical standards, performance, reliability, and operational excellence - Identify areas where ML can create value for our customers - Identify the right ML framing of product problems - Working with teammates and Product and Design stakeholders - Conduct exploratory data analysis and research - Deeply understand the problem area - Research and identify the right algorithms and tools - Being pragmatic, but innovating right to the cutting-edge when needed - Perform offline evaluation to gather evidence an algorithm will work - Work with engineers to bring prototypes to production - Plan, measure & socialize learnings to inform iteration - Partner deeply with the rest of team, and others, to build excellent ML products What skills might I need? - 5-8 years applied ML experience - Previous background in a senior/staff role (data science, software development or academic) - Significant, demonstrated impact that your work has had on the product and/or the teams - Strong programming skills - Experience as the primary technical leader for a team - Strong communication skills, both within engineering teams and across disciplines. - Comfort with ambiguity - Typically have advanced education in ML or related field (e.g. MSc) - Scientific thinking skills Bonus skills & attributes - Track record shipping ML products - PhD or other experience in a research environment - Deep experience in an applicable ML area - E.g. NLP, Deep learning, Bayesian methods, Reinforcement learning, clustering - Strong stats or math background - Visualization, data skills, SQL, matplotlib, etc. Benefits We are a well treated bunch, with awesome benefits! If there’s something important to you that’s not on this list, talk to us! :) - Competitive salary and equity in a fast-growing start-up - We serve lunch every weekday, plus a variety of snack foods and a fully stocked kitchen - Regular compensation reviews - we reward great work! - Pension scheme & match up to 4% - Peace of mind with life assurance, as well as comprehensive health and dental insurance for you and your dependents - Flexible paid time off policy - Paid maternity leave, as well as 6 weeks paternity leave for fathers, to let you spend valuable time with your loved ones - If you’re cycling, we’ve got you covered on the Cycle-to-Work Scheme. With secure bike storage too - MacBooks are our standard, but we’re happy to get you whatever equipment helps you get your job done - Unlimited access to Claude Code and best-in-class AI tools; experimentation & building is encouraged & celebrated - Relocation support for those moving to our offices Policies Fin has a hybrid working policy. We believe that working in person helps us stay connected, collaborate easier and create a great culture while still providing flexibility to work from home. We expect employees to be in the office at least three days per week. We have a radically open and accepting culture at Fin. We avoid spending time on divisive subjects to f

5-8 years applied ML experience. Previous background in a senior/staff role (data science, software development or academic). Significant, demonstrated impact that your work has had on the product and/or the teams. Strong programming skills. Experience as the primary technical leader for a team. Strong communication skills, both within engineering teams and across disciplines. Comfort with ambiguity. Typically have advanced education in ML or related field (e.g. MSc). Scientific thinking skills. Bonus: track record shipping ML products; PhD or other experience in a research environment; deep experience in NLP, Deep learning, Bayesian methods, Reinforcement learning, clustering; strong stats or math background; visualization, data skills, SQL, matplotlib.

Play an active role in hiring, mentoring and career development of other engineers. Raise the bar for technical standards, performance, reliability, and operational excellence. Identify areas where ML can create value for our customers. Identify the right ML framing of product problems - Working with teammates and Product and Design stakeholders. Conduct exploratory data analysis and research - Deeply understand the problem area. Research and identify the right algorithms and tools - Being pragmatic, but innovating right to the cutting-edge when needed. Perform offline evaluation to gather evidence an algorithm will work. Work with engineers to bring prototypes to production. Plan, measure & socialize learnings to inform iteration. Partner deeply with the rest of team, and others, to build excellent ML products.