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Applied Machine Learning Engineer, Economist
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  5. Applied Machine Learning Engineer, Economist

Mercor·San Francisco·30 июня

Applied Machine Learning Engineer, Economist

🏢 ОфисMiddleПолная занятостьРелокация
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Наша компания

Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents. Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices.

О роли

As an Economist on the Marketplace team, you will bring economic theory and rigorous empirical methods to the core decision systems that govern how talent and opportunities meet on Mercor. You'll study and shape marketplace dynamics — matching efficiency, pricing, incentives, liquidity, and supply/demand balance — and turn those insights into mechanisms and metrics that directly affect fill rate, hiring speed, earnings, and revenue

Чем предстоит заниматься

This is a high-impact, applied role at the intersection of economics, data science, and engineering. You'll help design the incentive structures and allocation mechanisms of a rapidly scaling two-sided labor market, and partner closely with ML, product, and engineering to put them into production
Design pricing and incentive mechanisms that improve liquidity without sacrificing quality or margin
Quantify and mitigate marketplace failure modes: cold start, congestion, thinness, and supply/demand imbalance
Measure the causal impact of matching and routing changes when market participants interfere with one another
Build supply/demand forecasts that drive capacity planning and sourcing decisions
Define the objective functions and guardrail metrics the marketplace optimizes toward

Наши требования

Advanced degree (PhD or Master's) in Economics or a related quantitative field, or equivalent applied experience
Strong foundation in microeconomics / market design and in causal inference and experimentation
Proficiency with data and code (SQL plus Python or R) to run analyses end-to-end on real data
Ability to translate economic theory into mechanisms and metrics that ship in a live product
Clear communication of rigorous analysis to both technical and business audiences
Experience with marketplaces, pricing, ranking/matching, or two-sided platforms (labor, ads, ridesharing, etc.)
Familiarity with experimentation under interference (network or marketplace experiments)
Experience partnering with ML and engineering teams to productionize models or mechanisms
WHY THIS ROLE
Mercor is a two-sided marketplace at its core. This role owns the economic logic of that marketplace — the incentives, prices, and allocation rules that determine who gets matched, how fast, and at what value. Your work will shape fundamental marketplace outcomes across quality, speed, earnings, and revenue

Мы предлагаем

Bi-annual performance bonus structure
Generous equity grant vested over 4 years
Up to $15k Relocation bonus
$10K housing bonus (if you live within 0.5 miles of our office)
$1.5K monthly stipend for meals
Free Equinox membership
$200 monthly laundry reimbursement
$200 monthly personal wellness reimbursement
Health, Dental, Vision insurance

Дополнительно

Marketplace mechanism design: pricing, incentives, and allocation rules that balance supply and demand
Causal measurement of marketplace health — liquidity, match quality, fill rate, time-to-hire, and earnings
Experimentation: A/B and marketplace/switchback experiments to evaluate interventions under interference
Forecasting and modeling of supply, demand, and capacity across the talent network
Economic framing of ranking, matching, and routing objectives, in partnership with ML and engineering
M
Mercor
San Francisco

ГрейдMiddle
ЗанятостьПолная занятость
РегионНе Россия
ФорматОфис
ИсточникСкрыто
Опубликовано30 июня
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