Staff Applied ML Engineer, AI Infrastructure

Mixpeek; San Mateo (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

zaimler seeks a staff-level AI/ML engineer to bridge research and production, translating cutting-edge ideas into robust systems and setting the strategic direction for model training, evaluation, and deployment.

You will own training infrastructure, tuning pipelines, and feature stores, optimize end-to-end agentic workflows for performance and scale, and define the ML roadmap with leadership as zaimler grows.

Qualifications

• PhD preferred; Master's with exceptional experience considered.
• 8–10 years in ML/AI engineering, with platform or lab experience.
• Built and owned AI/ML infrastructure end-to-end.
• Experience with training pipelines and feature stores.
• Hands-on optimization of agentic or multi-step LLM workflows.
• Led AI teams or heads of AI in startups or autonomous teams.

Responsibilities

• Bridge research and applied engineering, ensuring ideas move from concept to production with rigor and speed.
• Own training and evaluation infrastructure, including tuning, modeling pipelines, and evaluation frameworks.
• Build and evolve feature stores that serve both model development and production workloads.
• Optimize agentic workflows end‑to‑end for performance, reliability, and scale.
• Set technical strategy for how zaimler trains, evaluates, and deploys models as the platform grows.
• Partner closely with leadership to define the ML roadmap and make key architectural decisions.

Skills

Bridge research

Training pipelines

Feature stores

LLM workflows

AI infrastructure

Strategic leadership

Education

PhD preferred

Master's degree

Tools

Knowledge graphs

NLP

GPU optimization

vLLM

Ray

Semantic graphs

Job description

zaimler seeks a staff-level AI/ML engineer to bridge research and production, translating cutting-edge ideas into robust systems and setting the strategic direction for model training, evaluation, and deployment.

You will own training infrastructure, tuning pipelines, and feature stores, optimize end-to-end agentic workflows for performance and scale, and define the ML roadmap with leadership as zaimler grows.