Applied AI engineer in causal inference

Referment; United States; A distancia; USD 120.000 - 201.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Founding-team equity

Descripción de la vacante

Referment ищет опытного инженера/исследователя для разработки каузальнойLayer платформы: оценка эффектов, тесты на реальных данных и выстраивание системной проверки результатов. Требуется глубокий опыт продакшн AI и экспериментов.

Работа удалённо из Великобритании, полный рабочий день, предусматривается доля в стартапе. Учитываются знания LangChain/LlamaIndex, LangGraph и опыта в Databricks.

Formación

• Опыт построения AI/ML систем в продакшене.
• Умение применять каузальный вывод на реальных данных.
• Статистическая уверенность и калибровка оценок.
• Сильный Python (асинхронно, с типами, тестируемый код) и SQL.
• Опыт работы в AWS или Azure и развёртывание систем.
• Глубокие знания в LangChain, LangGraph и LlamaIndex.

Responsabilidades

• Строить каузальный слой платформы и картировать драйверы цифр клиента.
• Оценивать эффекты от вмешательств и давать честные диапазоны.
• Дизайн экспериментов с клиентами и прогнозы с диапазонами.
• Владеть методикой атрибуции для связки решения и результата.
• Разрабатывать оценки и верифицировать их через слепые тесты и бенчмарки.
• Учиться на реальных вопросах клиентов в первые 90 дней.

Conocimientos

Causal inference

Python

SQL

AWS/Azure

LLM tooling

LangChain

LangGraph

LlamaIndex

Experiment design

Communication with CFO

Educación

Top-tier AI/ML degree

Herramientas

LangChain

LangGraph

LlamaIndex

Databricks

Descripción del empleo

Описание:

Referment is working with an early-stage, venture-backed AI company whose platform turns enterprise data and live market signals into governed, ranked recommendations for strategic decisions — the kind a board signs off on. Every recommendation carries its evidence and its limits, each decision is logged so the business can later prove what worked, and the platform runs entirely inside the client's own cloud environment.

Задачи:

Building the causal layer of the platform: map what drives each client's numbers, estimate effects from natural experiments, and refute every estimate before it's used Returning effects as honest ranges — how much, and with what lag — so nothing prescriptive ships until it's calibrated Designing experiments with clients, such as a price change on a single segment, and turning forecasts and causal estimates into "if we do X, here's the range" Owning the attribution method that credits each decision with the result that followed Designing the evaluation that decides what ships: benchmarks, scoring rubrics and blind holdouts, plus the statistics that make them trustworthy In your first 90 days you'll learn the platform on real client questions, ship a first causal estimate with honest ranges on a live client problem, and move the evaluation from a single quality score to rubric-based checks with a blind holdout.

Требования:

Around five or more years building AI or ML systems that run in production Causal inference in practice — this is non-negotiable. You've estimated the effect of a real intervention on business data (difference-in-differences, synthetic control, uplift, marketing-mix models or experiments) and you know when each method breaks on short histories Strong statistics: uncertainty, calibration and experiment design Evaluation design: you've built benchmarks that decided what shipped, and you know how to check that the evaluation itself can be trusted Very strong Python (async, typed, tested) and solid SQL, with systems you've deployed and maintained on AWS or Azure Real LLM depth: at least one RAG, tool-using or text-to-SQL system taken properly beyond a demo. You know LangChain, LangGraph and LlamaIndex, and when you're better off writing it yourself AI-native working habits — tools like Claude Code in daily use — and you own every line you merge A degree from a top-tier institution, with AI-native training and learning You write your design down before you write code, and you can explain cause and effect to a CFO in plain English Будет плюсом: Useful but not essential: time series forecasting, Bayesian methods, fine-tuning, ML served and monitored in production, Databricks, published research or open-source work, or experience leading a small team

Условия:

The role is remote and UK-based, full-time, with founding-team equity