ML/AI Engineer

Jobzhr; San Francisco, Northern (CA, KY); Hybrid; USD 180,000 - 275,000; Full time

Job summary

Jobzhr in San Francisco, CA is seeking an ML/AI Engineer - Applied AI to build the AI systems behind our platform, including LLM orchestration, retrieval and structured extraction, evaluations, quality loops, observability, and production reliability.

You’ll work closely with engineering and product teams and have significant ownership over how AI is evaluated, improved, and shipped in production. The role requires 4-6+ years building production AI systems and strong Python/LLM API experience.

Qualifications

• 4-6+ years building production software, ML, or applied AI systems.
• Strong Python and hands-on experience with LLM APIs.
• Experience with RAG, embeddings, evals, structured outputs, or agent workflows.
• Strong understanding of AI quality, reliability, latency, and cost.
• Ability to turn ambiguous problems into measurable, production-ready solutions.

Responsibilities

• You’ll build the AI systems behind the platform—LLM orchestration, retrieval and structured extraction, and evaluations.
• Ensure quality loops, observability, and production reliability for AI features.

Skills

Production software

Python & LLM APIs

RAG / embeddings / evals

AI quality & latency

Production problem solving

Job description

ML/AI Engineer - Applied AI

San Francisco, CA

On-site · Full-time

$180K-$275K base + equity

Visa sponsorship is not available.

Join an early-stage AI startup building a new category at the intersection of process intelligence and agentic AI. The company is helping large enterprises capture operational knowledge and turn it into reliable, actionable intelligence for AI agents.

You’ll build the AI systems behind the platform - from LLM orchestration, retrieval and structured extraction to evaluations, quality loops, observability, and production reliability.

Looking for:

• 4-6+ years building production software, ML, or applied AI systems
• Strong Python and hands-on experience with LLM APIs
• Experience with RAG, embeddings, evals, structured outputs, or agent workflows
• Strong understanding of AI quality, reliability, latency, and cost
• Ability to turn ambiguous problems into measurable, production-ready solutions

Nice to have:

enterprise AI, process/workflow intelligence, multimodal data, Go, or experience with AI coding tools.

You’ll work closely with engineering and product teams and have significant ownership over how AI is evaluated, improved, and shipped in production.