Toloka AI·16 сент.
Senior ML Researcher
Ориентир по рынку≈ 250 000 – 400 000 ₽наш грубый ориентир по 189 вакансиям этого грейда на рынке, без учёта специальности: у работодателя вилка не указана
🔄 ГибридSeniorПолная занятость
Стоит ли откликаться
Стоит откликнуться, но спросите про деньги
Вместо вилки в объявлении «Competitive compensation»: что это значит в цифрах, придётся выяснять. В описании есть настораживающие места, они разобраны ниже.
3 из 4 в норме
Вилку не назвали: какую сумму назвать вам и ниже какой не соглашаться
Деньги
Вместо вилки в объявлении «Competitive compensation». Грубый ориентир: столько платят в 189 вакансиях уровня Senior по всему рынку, без учёта специальности.
Живость
Открыта 33 дня, и признаков фиктивного найма у неё нет.
Описание
Обязанности: 6, требования: 17. Понятно, что за работа, ещё до отклика.
- Известно
- продукт делают с нуля
- Не сказано
- сколько дней в офисе
- Настораживает
- «24/7»: работать придётся и вне обычного дня.
Что требуют
ОбязательноMLLLMPythonPyTorch
Надбавка к зарплате на этом уровне: ML +43%, LLM +43%, Python +22%.
Полный разбор в Pro
Это короткий разбор. В Pro полный: под эту вакансию и ваше резюме
Сколько просить
Не продешевите: сумма под ваше резюме, нижняя граница и готовые фразы для торга
Ваше резюме против требований
Пройдёт ли ваше резюме фильтр: что совпало с требованиями и что дописать до отклика
Спросить у работодателя
Что стоит спросить: вопросы именно к этой вакансии для первого созвона
Когда откликаться
Стоит ли спешить: за сколько дней эта компания обычно закрывает вакансии
30 дней Pro: полный разбор каждой вакансииЧто ещё входит в Pro
Вакансия на английскомПереведёт заголовок и описание вакансии на русский
Наша компания
We are the ML team inside Toloka — we build the machine-learning products that power the platform itself, so every project running on Toloka is faster, cheaper, and more reliable.
Чем предстоит заниматься
Own end-to-end fine-tuning pipelines: data prep, SFT/LoRA training, distillation from frontier models to smaller ones, evaluation, and serving handoff — both as self-serve platform capabilities and in hands-on client engagements
Extend our post-training stack beyond SFT into RL (RFT/GRPO-style methods, reward modeling, LLM-judge-based rewards) and help design the user-facing RL flow on the platform — this part is greenfield
Build and calibrate evaluation harnesses: LLM-as-judge setups calibrated against human labels, golden datasets, regression evals for optimization runs
Improve the platform's guiding agent: prompt and tool design, eval-driven improvement loops, stress-testing scenarios and fixing what breaks
Run experiments for client projects (e.g. prompt compression vs. fine-tuning trade-off studies) and turn the results into repeatable platform features
Work closely with platform engineers on the SDK/API surface so that training and eval jobs are callable from a developer's existing workflow
Наши требования
4+ years in ML engineering or applied research, with at least 1–2 years hands-on with LLMs in production or research settings
Practical experience fine-tuning open-weight models (LoRA/full FT), including data curation and knowing when fine-tuning is the wrong answer
Solid grasp of LLM evaluation: building evals from scratch, LLM-as-judge pitfalls, calibration against human judgments
Strong Python engineering: you write code others can run, not just notebooks; comfortable with the training/inference stack (PyTorch, HF ecosystem, vLLM or similar)
Product mindset: you'll often be the ML person closest to a client problem, so you need to reason about what's worth building, not just what's possible
Comfortable with ambiguity — priorities shift as we learn from pilots
Language: Fluency in English (B2 or above)
Hands-on RL for LLMs: GRPO/PPO-style post-training, reward modeling, RLHF/RLAIF pipelines
Prompt optimization frameworks (DSPy/GEPA or similar) or prompt-compression research (gisting)
Experience with distillation and quantization for cost/latency optimization
Experience building agentic systems (tool use, multi-step workflows) or shipping ML features in a self-serve product
What we can offer
You will be part of an international, dynamic environment that drives innovation and sets new standards in the AI and technology sector
Competitive compensation package including base salary, bonus, and ESOP
Paid PTO and benefits will vary depending on location
We offer a full remote or hybrid model (if you are based in NL or Serbia)
IT setup and home office allowances
Дополнительно
LLM QA — the core technology behind Toloka's automated quality-check mechanism. Every annotation flowing through Self-Service is reviewed by an LLM agent we design, train, and operate
Model distillation and fine-tuning — adapting frontier and open-source models to Toloka's tasks to hit the right quality at the right cost
Evaluation, benchmarking, cost modeling, and model selection across providers
We own the full chain. The same team designs the ML solution, ships it to production, keeps it running 24/7, analyzes the results coming back from real projects, and feeds that signal into the next iteration. No hand-off between research, engineering, and operations — it's all us
About the Position
You will own Toloka’s end-to-end fine-tuning, RL, and evaluation stack, bridging applied research and product engineering. In this role, you will spearhead greenfield post-training initiatives (such as GRPO and reward modeling), transform complex ML experiments into scalable platform features, and occasionally author technical write-ups on your findings for the AI community
[Important Notice] Scam Alert Regarding Fake Job Postings
It has come to our attention that an individual or group is fraudulently impersonating Toloka to post fake jobs and solicit personal information from applicants. Please be aware
Official Communication: Our recruiting team will only contact you from an official "toloka.ai" email address. We will NEVER use Gmail, Yahoo, Tolokainc, toloka.inc, or other personal or seemingly business email accounts
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Thank you for your vigilance!
About Toloka
At Toloka AI we create data that powers leading GenAI models and innovations. We work with frontier labs, big tech, renowned AI startups, enterprises and non-profit research organizations worldwide. We use a combination of Experts + Crowd + Tech Platform to teach AI models to reason and evaluate their efficacy and safety. We have experts in more than 50 different domains—from doctors and lawyers to physicists and engineers—and boast one of the most diverse global crowds, representing over 100 countries and speaking 40+ languages. We are a well-funded startup with an enviable portfolio of clients including Anthropic, Amazon, Microsoft, Poolside, Recraft, and Shopify
Recently, we secured strategic investment led by Bezos Expeditions and Nebius Group with participation from Mikhail Parakhin, CTO of Shopify and board advisor to leading GenAI companies, who now serves as our Chairman of the Board. Our remote-first team is globally distributed around the world: USA, UK, the Netherlands, Serbia, and more