Role Description
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.
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 building public benchmarks and evals.
- 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 job
Our team works at the intersection of Engineering, Product, and Community. You will work in the emerging benchmark ecosystem to onboard external benchmarks to Kaggle. Through collaborative engineering, joint benchmarking, and feedback loops, we run systematic evaluations to accelerate model capability understanding.
The Forward Deployed Engineer is the technical bridge between DeepMind’s most strategic partners and our model research teams. This role is embedded in the partner’s engineering journey, building joint evals, unblocking complex integrations, and synthesizing deep technical signals to drive model improvements.
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 diverse 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 - $232000 (USD) + 15% bonus target
Learn more about benefits at Google.Responsibilities
- Own the end to end onboarding of critical new benchmarks to the Kaggle platform.
- Drive joint evaluations and benchmarking, build and run systematic evals on partner-specific datasets to deliver high-fidelity performance signals directly to modeling teams.
- 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.
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.
About Forward Deployed Engineering
Forward Deployed Engineers are embedded directly with customers to build custom solutions, integrate products into existing infrastructure, and bridge the gap between product engineering and customer success. The role combines deep technical skills with the ability to operate in client environments and translate business requirements into working software.
Originally pioneered by Palantir, the FDE model has spread across AI, enterprise SaaS, and cloud infrastructure companies. FDEs write production code, architect integrations, train customer teams, and feed product insights back to the core engineering organization. At companies like OpenAI, Salesforce, and Databricks, FDE teams are treated as elite engineering units that can ship custom solutions in days rather than quarters.
Typical FDE stack: Python, TypeScript, SQL, REST/GraphQL APIs, cloud platforms (AWS/GCP/Azure), and increasingly LLM APIs and AI orchestration frameworks. Strong communication and the ability to context-switch between technical and business conversations are as important as coding ability.
Similar Roles
Get the FDE Pulse Brief
Weekly market intelligence for Forward Deployed Engineers. Job trends, salary data, and who's hiring. Free.