Staff Software Engineer, Generative AI, Core ML
Job Overview
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Date PostedApril 11, 2026
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Location
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Expiration date--
Job Description
2026-04-07T09:58:45.973Z
114338726301049542
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
- 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 2 years of experience with GenAI techniques (e.g., Large Language Models (LLMs), Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).
- Experience in Python and with ML frameworks (JAX, PyTorch) for large-scale model training.
- Experience in Reinforcement Learning (RLHF, RLAIF) and LLM post-training techniques (SFT, DPO, PPO).
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures and algorithms.
- 3 years of experience in a technical leadership role leading project teams and setting technical direction.
- Experience in multimodal learning or embodied agents, integrating various signals (text, audio, vision) into unified reasoning models.
- Experience building efficient evaluation harnesses, benchmarks, or simulation environments for measuring agent performance.
- Proven track record (publications or production launches) in reward modeling, including dense/mixture of experts (MoE) architectures and hybrid reward systems.
About the job
Google’s software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We’re looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.
Domain Applied ML (DAML) operates as Google’s “Applied AI Layer,” architecting the technical bridge between Google DeepMind’s frontier research and massive-scale product deployment. We define the company-wide strategy for Foundation Model adoption and engineer high-performance solutions in critical domains.
Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
Responsibilities
- Architect and implement advanced Reinforcement Learning (RL) workflows for complex, multi-turn agentic tasks. Develop novel training recipes for reasoning, self-correction, and tool use (e.g., CoT, Tree of Thoughts) to improve model reliability in long-horizon workflows.
- Design robust reward systems and simulation environments (“Digital Twins”) to evaluate and train agents.
- Create the “Intelligence Assets” required to train specialized student models, bridging the gap between generalist teacher models and domain-specific production requirements.
- Contribute to the unified middleware layer that democratizes access to state-of-the-art tuning. Implement efficient adaptation techniques (e.g., LoRA, Distillation, Quantization) to ensure high-performance agents can be deployed under strict latency and cost constraints.
- Partner with Google DeepMind researchers to validate novel algorithmic approaches (e.g., outcome-supervised vs. process-supervised RMs) and scale them from 0-to-1 prototypes into 1-to-N production libraries used across Google.
Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google’s EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.