Staff Machine Learning Scientist

Fin’s interview process is rigorous and spans 3‑4 rounds. The first round tests core ML coding skills and algorithmic thinking, often with live Python coding on platforms like CoderPad. The second round dives into system design and product case studies, where candidates must outline end‑to‑end ML pipelines, data handling, scalability and impact measurement. A leadership interview follows to assess mentorship experience and cultural fit, and finally an HR discussion covers compensation, relocation and benefits. Preparation should focus on real‑world ML productization, performance optimization, and clear communication of trade‑offs.

Fin promotes a hybrid working model with three days a week in the London office, encouraging face‑to‑face collaboration while offering flexibility for remote work. The company emphasizes a fast‑moving, data‑driven culture where engineers are empowered to experiment, ship quickly and iterate based on real customer feedback. Employees benefit from continuous learning programs, mentorship from senior leaders, and clear pathways to senior technical or managerial roles.

• Master's (MSc) or PhD in Machine Learning, Computer Science, Statistics, or a related quantitative field.\n• Minimum 5‑8 years of hands‑on applied ML experience, preferably in a senior or staff role.\n• Proven track record of shipping ML products to production and measuring customer impact.\n• Strong programming proficiency in Python and experience with ML frameworks (PyTorch, TensorFlow).\n• Demonstrated leadership experience – hiring, mentoring and technical decision‑making.\n• Excellent communication skills and ability to work across product, design and engineering teams.\n• Ability to work in a fast‑paced, ambiguous environment and deliver high‑quality results.

Round 1: Technical coding & algorithmic problem solving (Python/ML focused) → Round 2: System design & product case study (ML productization, impact measurement) → Round 3: Leadership & culture fit interview with senior managers → Round 4: HR discussion (compensation, relocation, benefits)

Fin is a fast‑growing AI Customer Agent company founded in 2011. With a portfolio of more than 30,000 global businesses, Fin builds end‑to‑end AI solutions that power customer support, sales and e‑commerce experiences across every channel. The company’s flagship product, the Fin AI Agent, combines proprietary large‑language models, advanced retrieval‑augmented generation, and real‑time orchestration to resolve complex customer issues with minimal human hand‑off. Fin’s culture is built around speed, intensity and relentless value delivery, and it has become one of the largest privately‑held software firms in the world. The Machine Learning team at Fin is a product‑centric group that partners closely with Product, Design and Engineering to turn research ideas into market‑ready features. The team owns the full lifecycle of ML work – from problem framing and data exploration, through algorithm selection, offline validation, and rapid prototyping, to production deployment and impact measurement. As a Staff Machine Learning Scientist you will be a senior technical leader, shaping the roadmap, mentoring engineers, and ensuring that the ML solutions meet the highest standards of reliability, scalability and customer impact. Key responsibilities include: 1. Lead the hiring, onboarding and career development of junior and mid‑level ML engineers and data scientists. 2. Define and enforce technical standards for model performance, monitoring, and operational excellence. 3. Identify high‑value product problems where ML can create measurable business impact. 4. Translate product and design requirements into concrete ML problem statements and research plans. 5. Conduct deep exploratory data analysis, feature engineering and hypothesis testing. 6. Research, prototype and evaluate state‑of‑the‑art algorithms (e.g., transformer‑based NLP, Bayesian methods, reinforcement learning) and select the most pragmatic solution. 7. Work closely with ML product engineers to ship prototypes to beta within weeks and move successful models to production. 8. Design and implement robust offline and online evaluation pipelines to validate model efficacy. 9. Communicate findings, road‑maps and impact metrics to cross‑functional stakeholders. 10. Champion a culture of data‑driven decision making and continuous learning across the team. Tech stack: Python, PyTorch, TensorFlow, Scikit‑learn, HuggingFace Transformers, SQL, Pandas, NumPy, Spark, Kubernetes, Docker, CI/CD pipelines, Grafana/Prometheus for monitoring, and cloud services (AWS/GCP). The role offers a clear growth path toward Principal Scientist or ML Engineering Manager, with exposure to product strategy and direct influence on Fin’s core AI offering. Joining Fin means working on cutting‑edge AI that powers real‑world customer interactions, collaborating with a high‑performing, diverse team, and enjoying a hybrid work model that balances in‑office collaboration with remote flexibility. The company invests heavily in learning, provides competitive compensation, and offers comprehensive health, family and financial benefits.