You will collaborate closely with technical teams to support the development and refinement of new AI capabilities, with a primary focus on quantitative financial risk management domains. Your expertise will drive the selection and rigorous resolution of complex risk-related problems, including market risk modeling, credit and counterparty risk, liquidity and funding risk, operational and model risk, stress testing & scenario analysis, Value at Risk (VaR)/Expected Shortfall (ES), risk attribution, capital allocation (economic/regulatory), and enterprise-wide risk frameworks under regulatory regimes (Basel, Dodd-Frank, IFRS 9, etc.).

This role requires exceptional quantitative rigor, rapid adaptation to evolving guidelines, and the ability to deliver precise, technically sound critiques, derivations, and solutions in a fast-paced environment. As a Finance Risk Expert, you will directly support xAI's mission by helping train and refine frontier AI models. You will teach the models how risk professionals quantify uncertainties, model tail events, assess portfolio vulnerabilities, ensure regulatory compliance, perform stress testing, and make data-driven decisions to protect capital and maintain financial stability.

Your tasks may include recording audio walkthroughs of risk models, participating in video-based scenario reasoning, or producing detailed quantitative risk analysis traces. All outputs are considered work-for-hire and owned by xAI.

Responsibilities:

• Use proprietary annotation and evaluation software to deliver accurate labels, rankings, critiques, and comprehensive solutions on assigned projects
• Consistently produce high-quality, curated data that adheres to strict quantitative and regulatory standards
• Collaborate with engineers and researchers to develop and iterate on new training tasks, risk-specific benchmarks, and evaluation frameworks
• Provide constructive feedback to improve the efficiency, precision, and usability of annotation and data-collection tools
• Select and solve challenging problems from financial risk domains where you have deep expertise

Basic Qualifications:

• Master’s or PhD in a quantitative discipline: Quantitative Finance, Financial Engineering, Financial Mathematics, Statistics, Applied Mathematics, Econometrics, Risk Management, Operations Research, Physics, Computer Science (with risk/finance focus), or closely related field or equivalent professional experience as a quantitative risk analyst, risk modeler, or risk quant
• Excellent written and verbal English communication (technical reports, regulatory documentation, explanatory breakdowns)
• Strong familiarity with financial risk data sources and platforms (Bloomberg, Refinitiv, Moody’s Analytics, S&P Capital IQ, RiskMetrics, internal bank risk systems, regulatory filings, Basel/FRB datasets, etc.)
• Exceptional analytical reasoning, attention to detail, and ability to exercise sound judgment with incomplete or ambiguous data

Preferred Skills and Experience:

• Professional experience in quantitative risk management, model development/validation, or risk analytics at a bank, hedge fund, asset manager, insurance company, regulator, or consulting firm
• Track record of publication(s) or contributions in refereed journals/conferences on risk, econometrics, statistics, or quantitative finance
• Prior teaching, mentoring, or training experience (university, industry workshops, regulatory training)
• Proficiency in Python/R for risk modeling (pandas, NumPy, SciPy, statsmodels, QuantLib, PyTorch/TensorFlow for ML risk models, etc.) and familiarity with risk systems (Murex, Calypso, Numerix, etc.)
• Experience with Monte Carlo simulation, copula models, stochastic processes, time-series analysis, extreme value theory, or machine learning for risk (anomaly detection, credit scoring, etc.)
• Knowledge of regulatory capital frameworks (Basel III/IV, FRB CCAR, SR 11-7 model risk guidance, IFRS 9/CECL, Solvency II)
• CFA, FRM, PRM, CQF, or similar risk-focused certifications
• Previous exposure to large language models, AI safety, or quantitative evaluation pipelines

Location and Other Expectations:

• Tutor roles may be offered as full-time, part-time, or contractor positions, depending on role needs and candidate fit
• For contractor positions, hours will vary widely based on project scope and contractor availability, with no fixed commitments required
• Tutor roles may be performed remotely from any location worldwide, subject to legal eligibility, time-zone compatibility, and role specific needs
• For US based candidates, please note we are unable to hire in the states of Wyoming and Illinois at this time
• We are unable to provide visa sponsorship
• For those who will be working from a personal device, your computer must meet xAI’s minimum hardware requirements

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Your deep domain knowledge will guide the selection and rigorous solving of complex problems in quantitative crypto strategies , including on-chain analysis, DeFi protocols, perpetual futures & derivatives trading, cross-exchange arbitrage, market microstructure in fragmented venues, MEV-aware execution, machine learning for crypto alpha signals, and portfolio/risk management in high-volatility 24/7 markets.

This role demands sharp quantitative thinking, quick adaptation to evolving instructions, and the ability to deliver precise, technically robust critiques and solutions in a dynamic environment.

Responsibilities:

• Utilize proprietary software to deliver accurate labels, rankings, critiques, and in-depth solutions on assigned projects
• Consistently produce high-quality, curated data adhering to rigorous technical and domain standards
• Partner with engineers and researchers to iterate on new training tasks, evaluation frameworks, and crypto-specific benchmarks
• Offer actionable feedback to enhance the efficiency, accuracy, and usability of annotation and data-collection interfaces
• Identify and solve challenging problems from crypto & digital asset domains where you have strong expertise , examples include:

• On-chain metrics analysis and wallet/flow clustering for alpha generation
• DeFi yield farming, liquidity provision, and impermanent loss modeling
• Cross-exchange / CEX-DEX arbitrage and triangular opportunities
• Perpetual futures funding rate strategies and basis trading
• Market microstructure in crypto order books (fragmented liquidity, MEV, sandwich attacks)
• Machine learning models for price prediction, sentiment from social/on-chain, volatility forecasting
• Tokenomics evaluation, airdrop/IDO quantitative assessment, and risk premia in altcoins
• Portfolio optimization and risk management in 24/7 high-volatility environments

• Provide rigorous critiques of model outputs, alternative quantitative approaches, mathematical derivations, code snippets, and step-by-step crypto reasoning
• Efficiently interpret, analyze, and complete tasks based on detailed (and evolving) guidelines

Basic Qualifications:

• Master’s or PhD in a quantitative discipline: Quantitative Finance, Financial Engineering, Computer Science (with crypto/blockchain focus), Statistics, Applied Mathematics, Economics (quantitative), Physics, Operations Research, Data Science, or closely related field or equivalent professional experience as a quantitative crypto trader, systematic strategist, or on-chain analyst
• Superior written and verbal English communication (technical papers, explanatory breakdowns, professional correspondence)
• Extensive hands-on familiarity with crypto data sources and tools (CoinGecko, CoinMarketCap, Dune Analytics, Glassnode, Nansen, Chainalysis, Messari, DefiLlama, The Graph, blockchain explorers, CEX APIs, on-chain datasets, etc.)
• Outstanding analytical skills, attention to detail, and sound judgment under partial information

Preferred Skills and Experience:

• Professional experience in quantitative crypto trading, systematic strategies, or on-chain research at a crypto hedge fund, prop desk, market-making firm, DeFi protocol, or digital asset investment firm
• Publications or public analyses in crypto quant topics (e.g., journals, conferences, reputable blogs, GitHub repos with notable traction)
• Teaching, mentoring, or content-creation experience in crypto/quant finance (university, bootcamps, Twitter threads, newsletters)
• Proficiency in Python for crypto analysis (pandas, NumPy, ccxt, web3.py, etherscan APIs, polars, scikit-learn, PyTorch/TensorFlow for ML models, etc.) and/or Rust/Solidity familiarity
• Experience with backtesting crypto strategies, handling tick-level or on-chain data, managing API rate limits, and dealing with 24/7 market quirks
• Knowledge of MEV, flash loans, oracle manipulation risks, liquidation cascades, or other crypto-native phenomena
• CFA, FRM, CQF, or blockchain-specific certifications (e.g., Certified Blockchain Expert)
• Prior involvement with LLMs, reinforcement learning, or AI evaluation in financial/crypto contexts (strong plus)

Location and Other Expectations:

• Tutor roles may be offered as full-time, part-time, or contractor positions, depending on role needs and candidate fit.
• For contractor positions, hours will vary widely based on project scope and contractor availability, with no fixed commitments required. On average most projects may involve at least 10 hours per week to achieve deliverables effectively though this is not a fixed commitment and depends on the scope of work.
• Tutor roles may be performed remotely from any location worldwide, subject to legal eligibility, time-zone compatibility, and role specific needs.
• For US based candidates, please note we are unable to hire in the states of Wyoming and Illinois at this time.
• We are unable to provide visa sponsorship.
• For those who will be working from a personal device, your computer must be a Chromebook, Mac with MacOS 11.0 or later, or Windows 10 or later.

Compensation and Benefits:

US based candidates: $45/hour - $100/hour depending on factors including relevant experience, skills, education, geographic location, and qualifications. International candidates: $25/hour - $75/hour depending on factors including relevant experience, skills, education, geographic location, and qualifications.

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