Staff ML Engineer: Build End-to-End AI Systems

Primer.ai; Pasadena (CA); Sur place; USD 180 000 - 260 000; Plein temps

Résumé du poste

Primer.ai seeks a Staff Machine Learning Engineer to own AI-driven products end to end, from prototype to production. You will work across LLMs, agentic systems, retrieval, embeddings, fine-tuned models, and evaluation harnesses to ensure reliable, scalable AI.

You’ll design distributed, tool-using agents and complex orchestrations, collaborate with product and engineering, and ship ML features that support high-stakes decisions with fast, grounded results.

Qualifications

• 6+ years building production backend software shipping ML-driven functionality.
• Mastery of data structures and algorithms, translating user needs into practical solutions.
• Hands-on depth with LLMs and agentic systems (prompt/context engineering, tool use, retrieval and RAG) and ML toolkit like PyTorch.
• Fluency authoring production APIs in Python (Rust a plus).

Responsabilités

• Set both technical and product direction across LLM, agentic, and NLP systems.
• Design and build distributed, agentic systems at scale: tool-using agents, multi-turn context, retrieval-grounded reasoning.
• Turn real-world data into trustworthy signals through entity linking, relation extraction, summarization, semantic search, and knowledge graphs.
• Take models from checkpoint to production: package, deploy, and operate low-latency, high-concurrency inference.
• Build evals and labeled-data flywheels to drive evidence-based decisions.
• Partner with cross-functional teams to shape the technical roadmap and address incidents.
• Raise engineering bar with patterns and practices.

Connaissances

Production backend ML

ML systems

Python/API design

PyTorch

LLMs & agents

Distributed systems

Evaluation frameworks

Formation

BS/MS/PhD in CS or related field

Outils

PyTorch

Triton

vLLM

Rust

Description du poste

Primer.ai seeks a Staff Machine Learning Engineer to own AI-driven products end to end, from prototype to production. You will work across LLMs, agentic systems, retrieval, embeddings, fine-tuned models, and evaluation harnesses to ensure reliable, scalable AI.

You’ll design distributed, tool-using agents and complex orchestrations, collaborate with product and engineering, and ship ML features that support high-stakes decisions with fast, grounded results.