About the Role
We are looking for a Full Stack AI Engineer to build intelligent, scalable, and user-centric product experiences across our B2B SaaS platform. In this role, you will work across the stack frontend, backend, and AI/ML-powered services to ship features that embed intelligence directly into workflows. You will have a primary area of strength (frontend or backend) while being comfortable contributing across layers as needed. This role is ideal for engineers who enjoy turning data and models into real product outcomes, not just experiments.
What You’ll Do
- Design, build, and maintain end-to-end product features across frontend, backend, and AI services
- Develop AI-powered capabilities such as recommendations, automation, summarization, classification, or decision support
- Integrate LLMs and ML models into production systems with attention to performance, reliability, and cost
- Build and consume well-designed APIs and services that support scalable AI workflows
- Collaborate closely with Product Managers, Designers, and Data/ML partners to translate business problems into technical solutions
- Ensure high code quality through testing, reviews, and adherence to engineering best practices
- Debug and resolve complex issues across the stack, including data, model behavior, and user experience
- Contribute to improving engineering standards, tooling, and developer experience within the team
Please note: This is a hybrid role that will be based in San Mateo, CA and requires an in-office presence three days per week (Tuesday - Thursday).
Qualifications
Core Engineering Skills
- 5–8 years of professional software engineering experience
- Strong experience in full stack development, with depth in both:
- Frontend: React, TypeScript, modern UI frameworks, or
- Backend: Java, Python, Node.js, REST/gRPC, distributed systems
- Solid understanding of system design, data models, and API contracts
- Experience working with cloud platforms (AWS, GCP, or Azure)
AI / ML Capabilities
- Hands-on experience integrating AI/ML models into production applications
- Practical experience with LLMs (e.g., prompt engineering, RAG patterns, evaluation, cost/performance tradeoffs)
- Familiarity with ML concepts such as embeddings, classification, ranking, or prediction (deep research background not required)
- Experience working with data pipelines, feature stores, or vector databases is a plus
Collaboration & Mindset
- Product-oriented mindset focused on user impact, not just technical elegance
- Comfortable operating in ambiguous problem spaces and iterating toward solutions
- Strong communication skills and ability to work effectively in cross-functional teams
- Willingness to learn and adapt as AI technologies and product needs evolve
What Success Looks Like
- Independently delivers complex features with minimal supervision
- Owns components or services end-to-end, from design to production support
- Makes sound technical decisions balancing speed, quality, and scalability
- Actively contributes to team discussions, design reviews, and code reviews
- Raises the bar on engineering quality without needing to be a people manager
Nice to Have
- Experience in B2B SaaS products with scale (multi-tenant, enterprise, or global users)
- Exposure to observability, model monitoring, and AI safety/guardrails
- Experience working in agile product teams shipping frequently to production