ML/AI Engineer

Radley James; San Francisco (CA); On-site; USD 180,000 - 275,000; Full time

Job summary

Radley James in San Francisco, CA, is hiring an ML/AI Engineer – Applied AI for an on-site, full-time role. The team builds the AI systems behind the platform, including LLM orchestration, retrieval, and evaluations, with a focus on reliability and scalable performance.

We seek 4–6+ years in production software/ML, strong Python, and hands-on experience with LLM APIs. Knowledge of RAG, embeddings, evals, and agent workflows is highly valued, along with an ability to turn complex problems into

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

• Build the AI systems behind the platform—from LLM orchestration to evaluations and production reliability.
• Collaborate with engineering and product teams to shape AI features and production readiness.

Skills

Python

LLM APIs

RAG

Embeddings

Evaluations

Agent workflows

AI reliability

Tools

Go

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.