Finance Subject Matter Expert, AI Models, Evaluations and Data, DeepMind

DeepMind 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.

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.

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.

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.