Machine Learning Engineer, Stripe Assistant

About the Role

The Stripe Assistant team is at the forefront of innovation, leveraging cutting-edge technologies like LLMs and agentic systems to elevate the user experience across Stripe. From the dashboard to support surfaces, the team is committed to delivering exceptional user experiences that anticipate, optimize, and execute on behalf of users. As a Machine Learning Engineer, you will play a critical role in driving this vision forward.

What You Will Do

• Own the end-to-end ML and agent architecture for Stripe Assistant
• Set the strategy for how the Assistant executes high-trust actions, delivers accurate analytical answers, and orchestrates capabilities across many tools and agents
• Drive conversation continuity and personalization across surfaces
• Evolve the Assistant into a proactive partner that anticipates user needs
• Establish rigorous evaluation and SLOs to deliver step-change improvements in quality, latency, cost, and availability
• Collaborate with product management to align on product vision and roadmap
• Mentor and grow engineers, upholding high bars for code quality, security, observability, and operational rigor
• Lead as a tech lead, aligning cross-functionally to ship safely and fast
• Explore and apply optimal machine learning methods to improve Stripe Assistant's overall performance
• Define and evolve the Assistant's capability and governance model across hundreds of tools and agents

What We Are Looking For

• 5+ years of experience in AI/ML and backend engineering
• Applied LLM experience: RAG/embeddings, tool use/function calling, agentic planning/orchestration, fine-tuning, code generation, evaluations
• Proficiency in Python (Ruby is a plus)
• Strong distributed systems fundamentals
• Experience working closely with product management
• Strong understanding of machine learning principles and practices
• Experience with large-scale data processing and storage systems
• Excellent communication and collaboration skills

Nice to Have

• Experience with synthetic data generation and RLHF
• Knowledge of domain-specific embedding and retriever fine-tuning
• Familiarity with automatic prompt tuning and optimal machine learning methods
• Experience with agile development methodologies and version control systems

Benefits and Perks

• Competitive salary and equity package
• Comprehensive health, dental, and vision insurance
• Flexible PTO and remote work options
• Professional development opportunities and conference sponsorships
• Access to cutting-edge technologies and tools
• Collaborative and dynamic work environment
• Opportunity to work on high-impact projects that drive real change
• Recognition and reward for outstanding performance and contributions

• Tip: Showcase your expertise in LLMs, RAG, and embeddings by highlighting specific projects or experiences in your resume and cover letter.
• To stand out, be prepared to discuss your approach to machine learning, including your experience with Python, distributed systems, and evaluation metrics.
• When applying, make sure to tailor your application materials to the specific requirements and qualifications listed in the job description.
• Be prepared to provide examples of your experience working with large-scale data processing and storage systems, as well as your ability to collaborate with cross-functional teams.
• During the interview process, be ready to discuss your experience with agile development methodologies and version control systems, and how you've applied these principles in previous roles.
• Consider creating a portfolio or repository of your work to demonstrate your technical skills and accomplishments.
• When negotiating salary, be sure to research the market rate for similar positions and be prepared to discuss your expectations and requirements.