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Senior AI/ML Engineer, Cloud AI Research

Google
California, USA
Senior · 5+ years experience
Onsite
USD 174000-252000 / year
Discovered 4 days ago
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Full Job Posting

Responsibilities

  • Lead 2–4 week "Strike Sprints" to rapidly prototype advanced agent state machines, tool-use orchestration, and reflection loops, transforming boilerplate programming into intent-driven AI topologies.
  • Partner with product and engineering teams via Forward Deployed Engineering (FDE), embedding into their workflows to translate novel agentic prototypes into scalable enterprise solutions.
  • Architect and build generalizable horizontal agents (e.g., AutoResearch and AutoFounder) to accelerate engineering/researcher velocity.
  • Develop agent architectures, planning frameworks, and self-improving foundation models to advance autonomous AI systems.
  • Collaborate across Google to disseminate technical playbooks and optionally paublish breakthroughs at ML conferences.

Minimum qualifications:

  • Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, or equivalent practical experience
  • 5 years of experience in software development, including coding experience in Python, C++, Java, JavaScript, or R.
  • 3 years of experience in ML system design and optimizing ML infrastructure (e.g., distributed training, model deployment, evaluation, data processing).
  • Experience developing autonomous agents, multi-agent workflows, or agentic frameworks (e.g., AutoGen, CrewAI, LangGraph, ADK, tool-use/function-calling, or multi-agent RL).
  • Experience designing, pre-training, fine-tuning, or evaluating Large Language Models (LLMs) and multimodal foundation models.

Preferred qualifications:

  • Master's degree or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related technical discipline.
  • First-author scientific publication submission(s) on agents in machine learning and NLP for conferences, journals, or public repositories (e.g., NeurIPS, ICML, ICLR, ACL).
  • Proven experience independently driving engineering delivery in rapid SWAT, Pathfinding, or Forward Deployed Engineering (FDE) environments.
  • Experience building complex evaluation benchmarks, test harnesses, or simulation environments.
  • Experience in RL, reward modeling, and human/AI preference alignment techniques (e.g., RLHF, RLAIF, DPO).

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