Senior Machine Learning Engineer, Genies

• Master’s or Bachelor’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field
• 2+ years of hands-on experience developing agentic systems utilizing Large Language Models (LLMs)
• 3+ years of experience building scalable, production-grade backend systems in Python (or Go, Javascript)
• 5+ years of experience in machine learning and statistics, with a proven track record of building products leveraging LLMs, ML, CV, or NLP models
• Strong communication skills, with demonstrated experience collaborating directly with product teams to design and elevate AI-driven features
• A proactive approach to learning, staying informed by taking an active interest in emerging AI research and industry trends

Desirable

• We are seeking a highly motivated and passionate Senior Machine Learning Engineer to join our core AI team in San Francisco, CA
• This is a high-impact role where you will take end-to-end ownership of the research and development behind our core AI avatar chat systems
• You will drive the technical roadmap that defines the intelligence, personality, and interactivity of millions of AI avatars
• By tackling fundamental technical challenges, you will bring fun, autonomous, and inspiring AI companions to life
• Working alongside a dedicated team of engineers and researchers, you will bridge the gap between cutting-edge AI research and real-world, consumer-facing product impact
• Research and implement state-of-the-art solutions using foundation models, prompt engineering, RAG, knowledge graphs, and multi-agent architectures, alongside classical machine learning techniques for a multi-modal AI chat product
• Architect, implement, and deliver scalable AI features and agentic systems within our backend systems
• Design and execute rigorous quantitative benchmarks to evaluate and continuously improve agentic systems and AI features
• Partner with the product team to iterate on and refine AI systems based on user feedback and engagement metrics
• Work closely with AI researchers to seamlessly transition experimental research outputs into robust, production-ready backend systems
• Define, implement, and enforce best practices for AI agent quality assurance, safety, and cost control
• Fine-tune and train LLM/ML models when off-the-shelf options are inadequate
• Remain at the forefront of academic and industry research in LLMs and AI agents, actively identifying and prototyping novel techniques to advance our technology stack