E

Data Scientist - AI Safety

ElevenLabs is an AI research and product company transforming how we interact with technology.

elevenlabs · На сервисе с: 03.09.26 10:11

Зарплата не указанаВеликобританияЛондонУдалёнка

About ElevenLabs

ElevenLabs is an AI research and product company transforming how we interact with technology.

We launched in January 2023 with the first human-like AI voice model. Today, we serve millions of users and thousands of businesses - from fast-growing startups to large enterprises like Deutsche Telekom and Meta. Our investors are some of the world's most prominent, including Andreessen Horowitz, ICONIQ Growth and Sequoia. We've raised $781M in funding and our last valuation was $11B - multiples of 11, always.

We have expanded from voice into three main platforms:

  • ElevenAgents enables businesses to deliver seamless and intelligent customer experiences, with the integrations, testing, monitoring, and reliability necessary to deploy voice and chat agents at scale.

  • ElevenCreative empowers creators and marketers to generate and edit speech, music, image, and video across 70+ languages.

  • ElevenAPI gives developers access to our leading AI audio foundational models.

Everything we do is the result of the creativity and commitment of our team - builders doing the best work of their lives. We are researchers, engineers, and operators. IOI medalists and ex-founders. If you want to work hard and create lasting positive impact, we want to hear from you.

How we work

  • High-velocity: Rapid experimentation, lean autonomous teams, and minimal bureaucracy.

  • Impact not job titles: We don’t have job titles. Instead, it’s about the impact you have. No task is above or beneath you.

  • AI first: We use AI to move faster with higher-quality results. We do this across the whole company—from engineering to growth to operations.

  • Excellence everywhere: Everything we do should match the quality of our AI models.

  • Global team: We prioritize your talent, not your location.

What we offer

  • Innovative culture: You’ll be part of a generational opportunity to define the trajectory of AI, surrounded by a team pushing the boundaries of what’s possible.

  • Growth paths: Joining ElevenLabs means joining a dynamic team with countless opportunities to drive impact - beyond your immediate role and responsibilities.

  • Learning & development: ElevenLabs proactively supports professional development through an annual discretionary stipend.

  • Social travel: We also provide an annual discretionary stipend to meet up with colleagues each year, however you choose.

  • Annual company offsite: Each year, we bring the entire team together in a new location - past offsites have included Croatia and Italy.

  • Co-working: If you’re not located near one of our main hubs, we offer a monthly co-working stipend.

About the role

We're looking for a Data Scientist to take ownership of the datasets and evaluation workflows that underpin our AI Safety work. You'll turn real-world product data into reliable, well-structured datasets for training and evaluating models, and help build the processes and infrastructure to continuously assess how those models perform in production.

This is a hands-on role at the intersection of data science, ML, AI safety, and policy. You'll work closely with safety researchers, engineers, and policy specialists to translate complex safety requirements into practical data and evaluation systems - starting hands-on with collection, analysis, and labelling, then building the methodologies, contributor networks, and quality standards that let this work scale.

In this role, you will

  • Own safety datasets end-to-end: collection, cleaning, labelling, quality control, versioning, and readiness for training and evaluation.

  • Translate safety policy into clear, consistent labelling and evaluation criteria, working closely with policy specialists.

  • Design and manage labelling processes, including sourcing, onboarding, and overseeing external contributors to a high quality bar.

  • Build evaluation workflows for models in production, using real-world data to track performance and surface issues.

  • Develop lightweight Python/SQL pipelines and tooling to make data work faster and reproducible, partnering with ML engineers on what "training-ready" looks like.

Who you are

  • Experience as a Data Scientist, or similar, working with real-world datasets and ML models.

  • Strong grasp of what makes a good dataset: collection, cleaning, sampling, labelling, quality control, evaluation.

  • Comfortable with Python and SQL for analysis and practical data workflows.

  • Strong critical thinking and judgement - comfortable with ambiguity and nuance, and able to turn complex guidelines into consistent, scalable decisions.

  • Autonomous and a clear communicator, able to work across disciplines (policy, engineering, research) in a fast-moving, still-forming environment.

Bonus points

  • Experience in AI safety, trust & safety, content moderation, or fraud/abuse detection.

  • Experience building datasets or evaluation frameworks, or with adversarial/safety-focused ML.

  • Experience managing human-in-the-loop labelling processes, dataset versioning, or ML pipeline tooling.

  • Experience operationalising guidelines with policy or legal teams.

Location

This role is based in London, where you'll work closely with our AI Safety and Policy teams.

We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or other legally protected statuses.

Похожие вакансии Data Science & ML

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S

Staff Data Scientist, Watchlist

socureНа сервисе с: 24.09.26 18:12
Зарплата не указанаСШАУдалёнка

Why Socure?

Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.

We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.

WHY SOCURE?

Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.

We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won't be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.

ABOUT THE ROLE

We are looking for a Staff Data Scientist to join Socure's Watchlist Data Science team. Watchlist sits at the heart of global AML compliance — our platform screens hundreds of millions of entities in real time across sanctions lists, PEP databases, and adverse media sources for banks, fintechs, and payment companies worldwide.

As a Staff Data Scientist, you will work on the hardest problems in entity matching and classification: scaling our patented real-time matching engine, building advanced Natural Language Processing (NLP) models for Named Entity Recognition (NER) and Information Extraction, and bringing next-generation research to production. This is a senior individual contributor role with broad technical ownership and direct impact on a product that helps the world's financial institutions manage sanctions and AML risk.

WHAT YOU'LL DO

Data Quality & Enrichment

  • Improve the quality, coverage, and freshness of Watchlist's underlying data through next-generation ingestion pipelines.

  • Design and execute rigorous data quality analysis pipelines to identify anomalies, evaluate dataset health, and ensure high-fidelity inputs for downstream model training.

  • Apply NLP and AI to classify and enrich raw source data into normalized schemas — extracting structured entity attributes from unstructured sanctions, PEP, adverse media, and enforcement sources.

  • Expand multilingual capabilities to support global screening across Latin and non-Latin scripts.

Entity Resolution

  • Build and improve NLP systems that consolidate how watchlist identities are represented. Developing Information Extraction and Named Entity Recognition (NER) pipeline to deduplicate entities across lists and resolve aliases into canonical profiles..

  • Develop approaches to handle how entity profiles change over time as names, aliases, and sanctions status evolve.

  • Measure and benchmark entity resolution quality, driving continuous improvement in coverage and accuracy.

Match Engine & Risk Scoring

  • Design and scale advanced NLP models and algorithms that perform real-time name matching and identity classification across diverse, multilingual unstructured data sources.

  • Build multi-signal risk scoring that combines name similarity, entity type, geography, list type, and other attributes into unified, calibrated risk scores.

  • Maintain and improve benchmarking frameworks, golden datasets, and regression tests that keep the match engine at the highest levels of recall and precision.

Analytics, Tuning & Evaluation

  • Build models and analytics that help customers tune their screening thresholds to the right operating point for their risk appetite and entity mix.

  • Develop backtesting and counterfactual analysis capabilities so customers and internal teams can understand how model or threshold changes would affect screening outcomes.

  • Design evaluation frameworks for AI-powered autonomous decision systems — defining correct behavior, calibrating confidence thresholds, and monitoring for drift in production.

AML Risk Detection

  • As Watchlist expands into payment screening, build the mathematical analysis and feature engineering needed to detect AML risk patterns across transaction data and payment message fields.

  • Develop and maintain the AML taxonomy and risk signal library that underlies Watchlist's classification and detection capabilities.

  • Apply graph-based methods to surface indirect risk exposure — identifying entities connected to sanctions risk even when they are not directly listed.




Research & Technical Leadership

  • Lead technical initiatives across Watchlist Data Science and shape the team's long-term approach to entity matching, enrichment, and AI.

  • Collaborate closely with Product and Engineering to translate research into production-grade systems at scale.

  • Stay current with advances in NLP, large language models, and entity resolution; prototype and deploy relevant techniques (e.g., advanced NER, LLM-based extraction) to AML use cases.

  • Mentor peers and contribute to a culture of technical rigor and continuous improvement.

WHAT YOU BRING

  • Master's or PhD in Computer Science, Computational Linguistics, Statistics, Applied Mathematics, or a related field; or equivalent professional experience.

  • 7+ years of experience in data science or machine learning, with meaningful work in NLP, entity resolution, or information extraction.

  • Experience in AML, sanctions screening, adverse media, or financial crime detection is strongly preferred.

  • Hands-on experience building and deploying NLP pipelines for entity extraction, named entity recognition, and record linkage at production scale.

  • Familiarity with multilingual NLP and non-Latin script processing is a strong plus.

  • Experience with LLMs and agentic AI frameworks (e.g., LangChain/LangGraph) is a plus.

  • Strong proficiency in Python and major ML libraries (PyTorch, spaCy, HuggingFace Transformers).

  • Strong SQL proficiency and experience with large-scale data pipelines and production ML systems.

  • Excellent communication skills — able to translate model performance tradeoffs into compliance and business language for non-technical audiences.

Note: We cannot provide Sponsorship at this time.

You must be located in one of our talent hubs: New York, San Francisco, Seattle, or Miami.

Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. If you need an accommodation during any stage of the application or hiring process — including interview or onboarding support — please reach out to your Socure recruiting partner directly.

Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
If you need an accommodation during any stage of the application or hiring process—including interview or onboarding support—please reach out to your Socure recruiting partner directly.



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сбер. it

Senior/middle data scientist (Центр Модельных Рисков)

сбер. itНа сервисе с: 24.09.26 17:03
Зарплата не указанаРоссияМоскваОфис

Управление Модельных Рисков - «by-design» уникальное подразделение, которое «видит» все AI/ML модели Банка, понимает процессы/точки принятия решений моделями, кривая обучения у наших сотрудников «круче», чем в подразделениях, разрабатывающих модели.

Наша цель – помочь Банку зарабатывать больше и улучшать клиентский опыт за счет повышения эффективности моделей и их использования в Бизнес процессах.

Мы ищем DS/Quant с экспертизой на стыке финансовой математики, торговых алгоритмов и ML для работы в напралении Модельного Риска Торговой Книги. Вы будете выступать ключевым звеном контроля качества моделей и агентов, используемых в оценке рисков и ценообразовании финансовых инструментов.

Обязанности

  • оценка качества моделей ClassicML, работающих на финансовых рынках, в том числе: торговые алгоритмы, оценка эластичности по цене, кластеризация клиентов и т.д.
  • проведение независимой оценки качества моделей прайсинга финансовых инструментов, а также риск-моделей (включая VaR, CVA, PFE)
  • анализ корректности математических предпосылок, устойчивости моделей и их соответствия бизнес-требованиям и рыночным условиям
  • оценка и контроль рисков агентов, работающих в торговых алгоритмах, процессах прайсинга финансовых инструментов и оценки рыночных рисков
  • создание инструментов для автоматизации процессов мониторинга моделей и агентов с использованием Python
  • подготовка аналитических заключений по результатам валидации, формирование рекомендаций по доработке моделей
  • презентация результатов работы, аргументация выводов и защита предложенных решений перед разработчиками моделей, риск-менеджментом и бизнес-заказчиками.

Будет плюсом:

  • навыки работы с генеративными AI-моделями; опыт создания AI-агентов и использования их в работе будет преимуществом.

Требования

  • знание теории вероятностей, математической статистики и случайных процессов
  • опыт работы на стороне разработки или валидации ClassicML моделей
  • уверенные навыки программирования на Python, опыт разработки структурированного и поддерживаемого кода
  • знание базовых принципов построения моделей прайсинга производных финансовых инструментов
  • опыт работы с количественными моделями в финансах или смежных областях будет преимуществом
  • участие в летних школах или курсах: Vega, ЦМФ – также является преимуществом.

Условия

  • работа в удобном и современном офисе недалеко от станции метро Кутузовская
  • ежегодный пересмотр зарплаты, годовая премия
  • корпоративный спортзал и зоны отдыха
  • более 400 образовательных программ СберУниверситета для профессионального и карьерного развития
  • расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа
  • гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ
  • подписка Прайм с возможностью совместного использования на трёх близких
  • вознаграждение за рекомендацию друзей в команду Сбера.
Соц.сети
Н

Senior ML Engineer / MLOps Engineer

Неизвестный работодательНа сервисе с: 24.09.26 16:27
Зарплата не указанаПольшаУдалёнка

#вакансия #vacancy
🚀 Senior ML Engineer / MLOps Engineer — 100% Remote
Must-have:
• Strong Python
• AWS / SageMaker
• MLOps, CI/CD
• Docker + Kubernetes
• ML lifecycle tools: MLflow / Kubeflow / SageMaker Pipelines
• Production experience with ML & LLM applications
• Generative AI / LLMOps
• PySpark / Apache Spark
• FastAPI / Flask
• Model training, fine-tuning, evaluation & optimization
📍 100% Remote | 🇬🇧 English B2+
📅 Duration: 3 months + extension
💰 Rate: TBD
⏰ Full-time
📩 Для подачи присылайте CV в telegram: ••••••••

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