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X 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: - Master's degree in Finance, Economics, quantitative fields, or equivalent practical experience.
- 5 years of institutional finance experience in investigative, modeling-intensive roles (Quantitative Trading, Corporate Finance, or Alternative Asset Management)
- Experience with frontier LLMs, coding agents, or AI evaluation pipelines, including structured prompts and tool-use traces.
- Experience identifying flaws in AI-generated financial models, backtests, or memos, and translating these errors into generalizable grading rules.
Preferred qualifications: - CFA, CPA, MBA, JD, Master of Financial Engineering (MFE), or an advanced quantitative degree in Finance, Computer Science, Math, or Economics.
- Background building benchmarks, rubrics, or SFT/RLHF pipelines at AI labs, fintechs, or quantitative teams.
- Domain-specific excellence in Python and SQL (Quant) or advanced, macro-free spreadsheet software and Python/SQL (Alternative Asset Management/Corporate Finance).
- Familiarity with market data feeds, SEC EDGAR, Capital IQ, FactSet, Bloomberg, ERP/EPM, and tax/audit platforms.
About the jobDeepMind is building frontier financial reasoning and agentic capabilities across Gemini and our enterprise agent harnesses. Moving AI from a generalist research assistant to an agent that can autonomously complete financial workflows requiresmore than raw pre-training scale - it requires deep domain mechanics embedded directly into how models are post-trained, how agent harnesses and tools are architected, and how evaluations measure real-world finance.
As a Finance Subject Matter Experts (SME), you will work with models, evaluations, and post-training data in the following three Finance domains -
- Quantitative Trading
- Corporate Finance - specifically with experience across FP&A, Audit and Tax
- Alternative Asset Management
Unlike traditional advisory roles that stop at high-level feedback or static prompt writing, you will sit directly in the model development loop alongside Research Scientists, Post-Training Engineers, and Product Managers. You will prioritize differentiated financial workflows, design end-to-end financial benchmarks, construct atomic and deterministic grading rubrics, diagnose multi-step agent trajectories, repair near-miss model reasoning traces, design verifiable reward signals, and co-build domain-specific agent skills.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $207000 - $300000 (USD) 20% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Evaluate the AI's step-by-step process across calculations, ledger searches, and code to pinpoint quantitative or accounting errors.
- Create realistic financial tasks (e.g., valuations, backtests, audits) and write "gold-standard" examples to correct model near-misses.
- Build complex prompts featuring vague instructions, GAAP/non-GAAP differences, and conflicting estimates to test AI resilience.
- Create pass/fail checklists for deliverables and draft deceptive, polished-but-wrong answers to stress-test automated graders.
- Teach the AI to efficiently use core financial tools (SQL, etc) and categorize error patterns into reusable templates.
