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Backend Engineer (AI/ML Infrastructure) - San Francisco

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Join an AI team building backend infrastructure for agent evaluation and continuous improvement. Focus on designing data models, developing integrations, and owning full-stack features. Requires 2-4 years of full-stack/backend experience with TypeScript, PostgreSQL, and ML engineering knowledge.

Key Highlights
Backend-focused full-stack role with ownership of architecture and product decisions
Hands-on ML engineering experience with LLM evaluation and agent infrastructure
On-site requirement in San Francisco with competitive salary ($120K–$180K)
Key Responsibilities
Build and maintain backend systems for agent evaluation and continuous improvement
Design data models for evaluation datasets, experiment runs, agent traces, and quality criteria
Develop and support enterprise integrations (authentication, OAuth, webhooks, etc.)
Own features end-to-end from technical design to deployment and iteration
Work directly with customers to diagnose failures and improve system reliability
Technical Skills Required
TypeScript PostgreSQL Machine Learning Engineering
Benefits & Perks
Visa sponsorship (H-1B, OPT, O-1)
Base salary range: $120,000–$180,000 USD annually
Nice to Have
Experience with distributed workflow debugging
Open-source contributions or shipped AI projects
Familiarity with nondeterministic system evaluation

Job Description

About the Role

Join an AI team building backend infrastructure for agent evaluation and continuous improvement. This backend-leaning full-stack role offers ownership of architecture and product decisions while turning complex systems into useful customer-facing features.

What You'll Do

Build and maintain backend systems that power agent evaluation and continuous improvement.

Design data models for evaluation datasets, experiment runs, agent traces, knowledge versions, findings, and customer-defined quality criteria.

Develop and support enterprise integrations, including authentication, OAuth, webhooks, incremental syncs, backfills, rate limits, retries, and monitoring.

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