Requirements

University degree in Computer Science, Machine Learning, Data Science, or a related field
3+ years’ experience building and deploying production-grade machine learning models
Strong software engineering mindset — ability to write clean, reusable, and scalable code in Python
Experience integrating ML/AI models into production software systems
Solid understanding of MLOps practices, CI/CD pipelines, and automated testing frameworks
Hands-on experience with ML frameworks (e.g., TensorFlow, PyTorch, Hugging Face)

Nice to Haves

Background in data science: comfort with experimentation, A/B testing, and measuring ROI/impact of ML projects (not just accuracy)
Experience with NLP or generative AI techniques
Familiarity with cloud-based ML infrastructure (AWS, Snowflake, SageMaker, etc.)

Design, develop, and deploy robust machine learning and AI systems for a range of products and use cases, including generative AI
Integrate ML and AI models into production systems, ensuring scalability, reliability, and maintainability
Deploy and monitor machine learning models and systems, including CI/CD pipelines, automated testing, monitoring, and model versioning
Leverage cloud platforms (AWS + Snowflake) and ML infrastructure (e.g., SageMaker, feature stores) for scalable deployment
Collaborate with cross-functional teams (Product, Customer Experience, and other engineering teams) to deliver AI-driven features and insights
Ensure all ML/AI solutions adhere to best practices in data privacy, security, and ethical standards
Contribute to a culture of technical excellence, knowledge sharing, and continuous learning

Receive a competitive reward package – reevaluated each year – that includes salary, benefits, and pre-IPO equity
Enjoy 28 days of paid vacation, plus an additional day after 2 and 4 years
Make an impact on the environment and society with 1 (fully paid) Impact Day
Receive generous family leave, child support, mental health support, and sabbatical opportunities
On-site collaboration with 2 days a week in the London office, with flexibility to work remotely for the remaining days
20 Flex Days per year to work remotely from other locations
Engage in team gatherings, cultural initiatives, and events like Summer Sessions and year-end celebrations
Access to healthy snacks, drinks, and a weekly catered lunch