Role Title: Staff / Principal AI/ML Engineer
Compensation: $250,000–$350,000 Base + Equity (Level Dependent)
Location: San Mateo, CA (Hybrid) | Exceptional Remote Candidates Considered
The Opportunity
We're partnered with a well-funded, rapidly scaling enterprise software company that’s building the next generation of AI-powered productivity tools for data teams.
This role sits at the forefront of the shift from traditional software workflows to intelligent AI agents. You’ll help build systems that can understand user intent, reason over complex datasets, generate production-ready workflows, and automate sophisticated engineering tasks that historically required significant manual effort.
The company is investing heavily in AI, has raised over $100M from leading investors, and is expanding its North American AI organization as demand for its platform continues to grow.
If you’re excited by AI agents, retrieval systems, code generation, and deploying machine learning products that real customers rely on every day, this is a rare opportunity to work on some of the hardest and most interesting problems in enterprise AI.
Why You Should Work Here
Build Real AI Products
Everything you build ships directly to customers. This is not a research lab, innovation team, or internal tooling group.
Massive Technical Ownership
You’ll lead large, ambiguous initiatives from concept through production and have significant influence over architecture and product direction.
Small Team, Huge Impact
Join a highly selective AI team where every engineer has meaningful ownership and visibility.
Solve Complex Technical Challenges
Work across AI agents, retrieval systems, code generation, knowledge graphs, semantic search, and large-scale distributed infrastructure.
Strong Growth Trajectory
Backed by top-tier investors with substantial funding and continued investment in AI expansion.
Modern Technical Environment
Build on cutting-edge technologies spanning cloud infrastructure, machine learning, distributed computing, and enterprise-scale AI systems.
The Role
As a Staff or Principal AI/ML Engineer, you’ll own large-scale initiatives focused on building intelligent systems that automate complex technical workflows.
This is a highly hands-on engineering role for someone who enjoys defining problems, experimenting with solutions, and bringing products from early concepts into production. You’ll work closely with senior engineering leadership and help shape the long-term AI strategy of the organization.
The ideal candidate combines deep AI/ML expertise with strong software engineering fundamentals and has a track record of shipping customer-facing AI products into production.
What You’ll Own
- Design and build multi-step AI agents capable of planning, reasoning, and executing complex workflows
- Develop systems for context management, tool usage, error recovery, and autonomous decision making
- Continuously improve agent quality through evaluation and iteration
Retrieval & Knowledge Systems
- Design and scale RAG architectures
- Build semantic search capabilities
- Develop vector database and knowledge graph solutions
- Improve how AI systems discover, retrieve, and reason over information
Code Generation & Automation
- Create AI-powered systems capable of generating and modifying production-ready code
- Build intelligent workflow automation solutions
- Improve reliability, correctness, and scalability of generated outputs
Production AI Infrastructure
- Deploy, monitor, and optimize AI systems operating at enterprise scale
- Improve latency, observability, reliability, and operational performance
- Own systems throughout their full lifecycle
Technical Leadership
- Drive architecture discussions and technical decision-making
- Mentor engineers and influence engineering best practices
- Help define the future direction of the AI platform
What We’re Looking For
Must-Haves
- Proven experience building and deploying AI products into production environments
- Deep understanding of:
- Retrieval systems
- Knowledge graphs
- Semantic search
- Experience with AWS and Kubernetes
- Strong software engineering and system design fundamentals
- Experience owning systems post-deployment, including monitoring, debugging, and optimization
- Comfortable working in highly ambiguous environments with minimal direction
- Strong product mindset with a passion for building customer-facing solutions
Nice-to-Haves
- Experience building AI coding assistants or code-generation products
- Exposure to enterprise data platforms and analytics ecosystems
- Experience with distributed systems and cloud-native architectures
- Familiarity with modern data processing technologies and large-scale data workloads
What Success Looks Like
You’re someone who can clearly explain:
- What AI products you’ve shipped to production
- The technical decisions you personally owned
- How you evaluated model quality and performance
- How success was measured
- Lessons learned and improvements you’d make today
You thrive in fast-paced environments, enjoy solving difficult engineering problems, and are motivated by building products that deliver meaningful value to customers.