Part 1: Emerging Talent Relationship Map
Core Players
Meta Superintelligence Labs
Verified market leader
Leading the AI talent war with documented $100M+ signing bonus campaigns. successfully recruited Trapit Bansal (key OpenAI o1 reasoning model contributor), Lucas Beyer, Alexander Kolesnikov, and Xiaohua Zhai (OpenAI Zurich office founders) alongside Scale AI acquisition for $14.3B bringing CEO Alexandr Wang to head new superintelligence division.
Why it matters: Setting unprecedented compensation benchmarks forcing industry-wide recalibration while securing critical data infrastructure.
Google DeepMind
Strategic addition confirmed
DeepMind remains a premier AI research hub (AlphaGo, AlphaFold, etc.) within Alphabet. Implementing aggressive non-compete agreements preventing UK employees from joining competitors for up to one year; researchers reportedly “in despair” over restrictive contracts plus losing major clients like Scale AI after Meta acquisition.
Why it matters: Creating talent availability through restrictive retention policies that may backfire, opening opportunities for mission-driven alternatives.
Anthropic
Validated retention success
Achieving documented 80% retention rate while competitors hemorrhage talent; engineers 8x more likely to leave OpenAI for Anthropic, 11:1 ratio from DeepMind. While not traditionally quant-focused, they’ve recruited 20+ OpenAI employees and are expanding into financial applications with Claude AI. Their $3.5B Series E at $61.5B valuation makes them a significant player in the AI talent wars, with spillover effects into quantitative finance.
Why it matters: They have many open AI roles and value ML/quant skills, so monitoring Anthropic’s openings and alumni can surface relevant candidates. Proving mission-driven positioning can compete against pure compensation wars, validating approach for firms like Nascent.
Citadel Securities
North American Benchmark
Global market maker with $6.2M revenues per person and ~1,600 employees. Known for aggressive non-compete enforcement (100% for quants) but continues strategic hiring of AI-forward talent. Their systematic approach to talent acquisition and retention sets industry benchmarks. Record-breaking 0.4% acceptance rate for 2025 internships from 108,000 applicants; aggressive campus recruiting with $3,300-$5,000 weekly intern compensation.
Why it matters: Sets North American quantitative finance talent acquisition standards and pipeline expectations.
Hudson River Trading
Direct AI/Quant relevance
Pioneering quantitative trading firm that seamlessly blends advanced algorithms with machine learning for market making. They’ve become a talent magnet for engineers wanting to work on cutting-edge AI trading systems at scale. Actively hiring AI researchers across NYC, London, and Singapore with $150K-$250K ranges; operating “state-of-the-art research clusters with very high GPU-to-researcher ratios.
Why it matters: Direct North American competitor for AI-forward quantitative talent with proven infrastructure.
Insights
The current AI talent landscape has been fundamentally disrupted by Meta’s aggressive $100M recruitment campaign, creating a unique strategic opportunity for Nascent’s quantitative developer search.
Meta successfully recruited key OpenAI researchers, triggering industry-wide talent redistribution that positions Nascent perfectly to capitalize on secondary market opportunities.
Up-and-Comers
Thinking Machines Lab
CONFIRMED $2B SEED AT $10B VALUATION
Founded by ex-OpenAI CTO Mira Murati in February 2025, closed record-breaking $2B Andreessen Horowitz-led seed round at $10B valuation; recruited 30+ researchers with two-thirds being ex-OpenAI employees including co-founder John Schulman as Chief Scientist and Barret Zoph as CTO.
Why it matters: Demonstrates AI talent war creates $10B+ startups instantly; recruiting 19+ OpenAI alumni proves smaller firms can compete on mission and equity upside
Scale AI
Quantitative Finance AI Infrastructure
Following Meta’s $14.3B investment for 49% stake, they’re expanding into financial applications and becoming a major player in AI infrastructure for quantitative finance. Their data labeling and preparation capabilities are crucial for AI model development.
Why it matters: Cross-pollination of AI talent into specialized domains creates versatile ML builders; validates that niche applications can command unicorn valuations
Safe Superintelligence (SSI)
Confirmed $32B Valuation
Ilya Sutskever’s AI safety venture raised $2B additional funding reaching $32B valuation (6x increase from $5B in September 2024).
Why it matters: World-class leadership team building next-gen AI safety/capability research; alumni will cycle into other projects creating downstream hiring opportunities
DeepSeek (China)
Validated Global Performance
Chinese startup whose R1 reasoning model updated in May 2025 ranks just behind OpenAI’s o4-mini and o3 on LiveCodeBench.
Why it matters: Exemplifies growing non-U.S. AI talent pool; engineers possess cutting-edge LLM expertise that could be attracted to North American opportunities
Rebellion Research
Verified AI-First Hedge Fund
Established 2003, one of first firms on Wall Street to use AI in 2006; CEO Alexander Fleiss operates $1B+ AI-driven systematic trading using Bayesian networks nicknamed “Star”.
AI-powered hedge fund that’s pioneering the use of machine learning models for systematic trading. They’re building a new generation of AI-first investment strategies and attracting top talent from both tech and traditional finance.
Why it matters: Pioneering AI-first investment strategies attracting top talent from both tech and traditional finance; proven 18-year track record validates AI-quantitative approach
Places to Stay on Their Radar
Developer Communities & Platforms
e.g. Kaggle/AI challenge forums, GitHub and Hugging Face repos, ML Slack/Discord groups.
Why it matters: High-performing competitors and contributors often transition to industry. We can join these communities (and sponsor contests) to spot engaged talent early.
Conferences & Industry Events
e.g. ML conferences (NeurIPS, ICML) and fintech/crypto events (Consensus, Token2049, etc.)
Why it matters: These gatherings showcase new research and prototypes, and attract PhDs and startup engineers. Attending gives us direct network access to speakers and attendees and also show interest in things presenters are passionate about with great persuasion.
Professional social networks
Platforms like LinkedIn and Wellfound/AngelList where professionals keep their job information up-to-date and are open to be found and connect.
Why it matters: Provides several ways to find, reach out, and follow potential candidates as well as see what competitors may be doing.
Part 2: Micro-Sourcing Sprint
Sample keywords | boolean searches | search queries used
Linkedin | Github
(“Python developer” OR “quantitative developer”) AND (“GitHub Copilot” OR “AI code editor” OR “AI tools”) AND (“0-2 years” OR “junior” OR “entry level”) AND (“trading” OR “quantitative finance”)
(“quantitative developer” OR “quant dev”) AND (“ex-Meta” OR “ex-OpenAI” OR “ex-Microsoft”) AND (“open to work” OR “junior” OR “entry level”) AND (“trading” OR “quantitative finance” OR “buy side” OR “sell side” OR “side quant”)
(python OR matlab) AND (quant OR finance OR “tradfi”) AND (“open to work” OR “junior” OR “entry level”) AND (“trading” OR “quantitative finance” OR “buy side” OR “sell side” OR “side quant”)
(“PhD candidate” OR “graduate student”) AND (“quantitative finance” OR “financial engineering”) AND Python AND (“portfolio optimization” OR “algorithmic trading”)
topic:quantitative-trading language:Python stars:>1000
“algorithmic trading” Python site:github.com in:description AutoTrader
Prospects
P. M.
High alignment
- Current Status: PhD candidate Actuarial Mathematics, Heriot-Watt University (expected completion 2025)
- Technical Evidence: Founder of QuantFILab with 25+ GitHub repositories in quantitative finance; developed DivFolio portfolio management app using R Shiny; created BOTapi for financial data access from Bank of Thailand
- AI-Forward Indicators: Research includes “Statistical Machine Learning,” “Monte Carlo Simulation,” and “Generative Models”; extensive use of modern computational tools for portfolio optimization
- Experience Alignment: 0-3 years professional experience with extensive academic project portfolio demonstrating builder mindset
- Sourcing Notes: Personal website shows research focus on “Portfolio Optimization,” “Algorithmic Trading,” and “High Frequency Trading”; QuantFILab demonstrates entrepreneurial approach to quantitative finance
- Geographic Viability: UK-based PhD completion creates natural transition opportunity to North American markets
Platform: LinkedIn
Subject: From QuantFILab to Production: Your Portfolio Work at Scale
Hello P M,
I came across your impressive research profile and background in quantitative finance, and I’m reaching out about an exceptional opportunity that aligns perfectly with your expertise. I’m currently conducting a search for an AI-Forward Quantitative Developer at Nascent, a leading crypto investment firm, and your combination of academic rigor and practical quantitative skills caught my attention.
Your Ph.D. in Actuarial Mathematics from Heriot-Watt University and extensive research in algorithmic trading, high-frequency trading, and statistical arbitrage directly aligns with Nascent’s need for someone who can develop models to understand portfolios and unlock opportunities. Your work on DivFolio – the Shiny application for portfolio divestment in green finance – demonstrates exactly the kind of intelligent backend systems and real-time insights tools that this role requires.
Would you be interested in discussing how your research could translate to production quantitative trading systems? I think you’d find our approach to scaling portfolio optimization compelling. I’d also love to just connect to get firsthand updates on what you will be doing next.
All the best,
[Me]
[Nascent info]
K. M.
Active Open-Source Builder
- Technical Background: Creator of AutoTrader Python framework (1.1k GitHub stars) for algorithmic trading. Proficient in Python asyncio/io, real‑time data pipelines, and risk management. Background title “Machine Learning Engineer @ RedMarbleAI” signals ML/crypto blend.
- Project Status: AutoTrader actively maintained with comprehensive documentation including “Multi-bot, event-driven backtesting,” “Strategy Optimisation,” and “Live-trading” capabilities
- AI Integration Evidence: AutoTrader platform includes advanced automation features and “Custom Indicator Library” suggesting modern development approaches
- Builder Validation: Complete trading framework with “Integrated data feeds,” “Interactive visualisation using Bokeh,” and “Repository of example strategies”
- Experience Assessment: Project sophistication indicates 2+ years practical experience but demonstrates exactly the “builder mindset” Nascent requires
- Sourcing Notes: AutoTrader documentation shows “CryptoBots has been released” indicating crypto trading focus alignment. Well‑documented README and CI pipeline demonstrate production readiness
- Rationale:
- Strong software engineering rigor + quant domain expertise
- Open‑source experience indicates collaboration and clear documentation practices
Platform: LinkedIn
Subject: Quant Dev Opportunity with Nascent
Hello K M,
I came across your AutoTrader framework and was impressed by the comprehensive approach to algorithmic trading infrastructure. I’m reaching out because the multi-bot, event-driven backtesting capabilities demonstrate exactly the systematic thinking we’re looking for at Nascent.
We’re building AI-driven quantitative systems for crypto markets and your open-source experience with algorithmic trading creates the perfect foundation for institutional-scale trading infrastructure.
We’re looking for quant-focused people to be part of the team, and CryptoBots release shows your crypto trading focus – we’re building the institutional infrastructure for exactly this market and I think your knowledge would be the secret sauce we’re looking for to complete the team.
Would you be interested in exploring this opportunity with me? I would love to be able to discuss in detail the things I mentioned. I’d also love to just connect to get firsthand updates on what you will be doing next.
All the best,
[Me]
[Nascent info]
A. R.
Stock Market Prediction and Modeling dev
- Current Employment: Senior Software Engineer at Amazon Web Services (AWS)
- Technical Credentials: Creator of Bulbea deep learning library for stock prediction (2.1k GitHub stars); 130+ public repositories demonstrating extensive development capability. Expert in Python, TensorFlow/PyTorch; active issue triage and PRs in ML/trading projects
- Builder Evidence: Bulbea provides “Deep Learning based Python Library for Stock Market Prediction and Modelling” with TensorFlow/PyTorch integration.
- Experience Level: Senior Engineer at AWS indicates 6+ years experience – significantly beyond Nascent’s 0-2 years requirement
- Rationale:
- Demonstrated “builder” mindset—shipped an end‑to‑end ML trading library
- Public traction (stars/forks) signals community validation and code quality
- Reservations: Has higher experience level and may lead to a higher salary ask.
Platform: LinkedIn
Subject: Builder Role with AI/Trading Edge
Hello A R,
I came across your work on Bulbea and was genuinely impressed—it’s rare to see clean, well-documented ML tools built specifically for financial prediction.
I’m working with Nascent, a fast-moving team investing and building in crypto markets, and we’re looking for a Quantitative Developer who thrives on shipping, works with AI-native tools, and enjoys automating the boring stuff.
Your open-source work shows that mindset. Would you be open to a quick intro call? I’d love to share more context and hear what you’re excited about building next.
All the best,
[Me]
[Nascent info]
Outreach strategy
- Choose platform to reach out that has sent/read/history/notification features to make possible future follow-ups easy.
- Make first message short and friendly, but direct. Short enough to be digestible in 1-2 minutes; enticing enough for the candidate to be curious.
- Show genuine interest in what they’ve done to establish rapport.
- Keep next step something simple for them to do and easy enough to set up.
Part 3: Fit-Read Memo
Candidate Comparison
| Assessment Criteria | P M | K M | A R |
|---|---|---|---|
| Experience Level (0-3 years) | PERFECT - PhD student, 0-2 years professional | EXCELLENT - Trader and dev since 2022 | OVERQUALIFIED - AWS Senior Engineer (6+ years) |
| AI-Forward Development | EXCELLENT - Statistical ML, Monte Carlo, Generative Models | STRONG - Systematic AI-enhanced AutoTrader framework | PROVEN - Bulbea deep learning library, TensorFlow/PyTorch |
| Builder Mindset | EXCEPTIONAL - QuantFILab founder, 25+ repos, DivFolio app | PROVEN - Complete AutoTrader (1.1k stars, 2,262 commits) | OUTSTANDING - Bulbea (2.1k stars), 130+ repositories |
| Quantitative Finance | PERFECT - PhD Actuarial Math, portfolio optimization research | DIRECT - Algorithmic trading with backtesting/live trading | STRONG - Stock prediction, financial time series |
| Crypto/Markets Relevance | DEMONSTRATED - Sustainable finance, financial data integration | VALIDATED - CryptoBots release, crypto exchange integration | LIMITED - Stock focus, no crypto experience |
| Geographic Accessibility | VIABLE - UK PhD completion creates North American opportunity | VIABLE - Greater Brisbane Area. Might do remote. | CHALLENGING - Nebraska-based, stable AWS employment. Must agree to remote. |
| Availability/Timing | OPTIMAL - PhD completion Summer 2025 natural transition | QUESTIONABLE - AutoTrader archived May 2025 | UNLIKELY - No availability signals |
| Compensation Alignment | EXCELLENT - $140K-$180K range acceptable for PhD completion | UNCERTAIN - Depends on experience verification | MISALIGNED - AWS Senior expects $200K+ |
| Overall Recommendation | PRIMARY TARGET | SECONDARY/BACKUP | WILL BE A CHALLENGE |
Memo
Primary Recommendation: P M
Strategic Fit
PhD candidate completing Summer 2025 with QuantFILab founder credentials (25+ GitHub repos) and published portfolio optimization research. Perfect 0-3 years experience with academic-to-industry transition timing.
Technical Excellence
- AI-Forward Alignment: Statistical ML, Monte Carlo simulation, generative models research
- Quantitative Focus: DivFolio portfolio management app, BOTapi financial data integration
- Builder Evidence: 27+ citations, peer-reviewed sustainable finance publications
Market Positioning
- Compensation: $140K-$180K base competitive for PhD completion + equity upside
- Availability: Summer 2025 graduation creates natural transition window
- Mission Appeal: Academic research background suggests receptiveness to meaningful work over pure compensation
Secondary Recommendation: K M
Technical Capability
- AutoTrader Python framework (1.1k stars) with crypto integration through CryptoBots – direct crypto trading experience relevant to Nascent’s focus.
Risk Assessment
- Critical verification required: Project sophistication may indicate 2+ years experience beyond requirements; AutoTrader archived May 2025 raises availability questions.
Strategic Recommendations
Immediate Action: P M
Begin relationship cultivation before PhD completion with 3-6 month timeline. Position as opportunity to scale DivFolio concepts to institutional crypto trading with AI-first culture and direct trader collaboration.
Verification Priority: K M
Research current interest alignment and agreeability in working remote.
Market Timing
Execute immediately on primary target while PhD completion creates optimal availability window. AI talent war disruption provides strategic opportunity to acquire right candidate profile at right time with competitive positioning advantage.
Bottom Line: Nascent can secure top-tier talent perfectly matching role requirements while larger competitors focus on expensive talent wars, creating optimal alignment of candidate quality, timing, and competitive advantage.