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X Applicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.Note: By applying to this position you will have an opportunity to share your preferred working location from the following:
Mountain View, CA, USA; New York, NY, USA; San Francisco, CA, USA.
Minimum qualifications: - Bachelor's degree or equivalent practical experience.
- 5 years of experience with software development and experience with coding in Python, JavaScript/TypeScript, or similar languages.
- 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
- Experience with Machine Learning systems or Large Language Models (LLMs).
- Experience in a customer-facing role, managing client relationships or external stakeholders.
Preferred qualifications: - Experience working with developer tools, APIs, SDKs, or platform integration.
- Ability to collaborate directly with external customers or partners in a technical capacity.
- Ability to work independently with minimal oversight across global time zones.
- Excellent technical writing and communication skills, along with the ability to produce documentation, tutorials, or engineering case studies.
About the jobThe team works at the intersection of Engineering, Product, and Community. We advocate partner success and model excellence by embedding with DeepMinds (DM's) most strategic developers. Through collaborative engineering, joint benchmarking, and feedback loops, we run systematic evaluations (evals) to accelerate model improvements, unblock key integrations, and showcase Gemini's capabilities at scale.
As a Forward Deployed Engineer (FDE), you will serve as the technical bridge between DeepMind's model teams and our most strategic partners. Embedded directly in the partner's engineering journey, you will design joint evaluations, unblock complex integrations, and synthesize high-fidelity technical signals to directly influence model development. This role demands a combination of technical expertise, customer empathy, and the autonomy to operate effectively across global time zones.Artificial intelligence will be one of humanity's most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.
We are pushing the boundaries across multiple domains. Our global teams offer learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $253000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Embed with strategic partners, own the technical relationship with top strategic partners, helping them architect, optimize, and build production-grade GenAI applications. Serve as the primary technical escalation path for strategic partners during critical launch windows and production issues.
- Drive joint evaluations and benchmarking, build and run systematic evals on partner-specific datasets to deliver high-fidelity performance signals directly to modeling teams.
- Optimize GenAI workloads, guide partners on advanced implementation techniques, including prompt engineering, complex Retrieval-Augmented Generation (RAG) architectures, and multimodal integrations.
- Author high-visibility case studies, developer blogs, and reference implementations to showcase Gemini's production capabilities.
- Build feedback loop tooling, collaborate with product engineering to develop tooling that aggregates and surfaces developer feedback patterns at scale.
