Google DeepMind · 23 hours ago
Machine Learning Software Engineer, GeminiApp Agents and Tool Use
Google DeepMind is a team of scientists and engineers focused on advancing artificial intelligence for public benefit. They are seeking a Machine Learning Software Engineer to enhance the Gemini platform by integrating 1P and 3P tools, improving model quality, and building new agentic experiences.
Artificial Intelligence (AI)Business DevelopmentFoundational AIMachine Learning
Responsibilities
Improving the model’s ability to reason through complex requests, call various tools to fulfill the user request, including complex scenarios like chaining multiple tools across multiple turns
Building new agentic experiences powered by our core tools with multi-modal input in GeminiLive with camera and screenshare
Identifying gaps based on user feedback, and improving the model’s capability for function calling
Building data collection and eval infra to collect eval data in scalably and autoraters, autousers, automatic prompt optimization to help scale hill-climb and push the frontiers of what Gemini can do with MCP integrations
Qualification
Required
BS, MS or PhD degree in computer science, mathematics, applied stats, machine learning or similar experience working in industry
Experience working on software engineering projects from proof-of-concept through to implementation
Proven knowledge and experience of Python, C++, GCL in production environments
Experience in applying experimental ideas to applied problems
Great communication skills and interpersonal skills
Knowledge of machine learning and statistics
Preferred
Experience productionizing state-of-the-art large language and multimodal models a plus
Experience fine-tuning large models (e.g. SFT, RLHF, prompt optimization) a plus
Benefits
Bonus
Equity
Benefits
Company
Google DeepMind
Google DeepMind aims to research and build safe artificial intelligence system to solve intelligence and advance science and humanity. It is a sub-organization of Google.
Funding
Current Stage
Late StageTotal Funding
unknown2014-01-26Acquired
2011-02-01Series A
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