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Toloka

Human-in-the-loop data labeling platform

Principal ML Researcher

Natural Language Processing & LLMsConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • At least 6 years of experience in machine-learning engineering or applied research, including at least 3 years leading large-scale LLM post-training, fine-tuning, or alignment initiatives.
  • Deep theoretical and practical mastery of LLM alignment, including supervised fine-tuning, LoRA/parameter-efficient fine-tuning, RLHF/RLAIF (GRPO, DPO, PPO), reward modeling, and reasoning-oriented post-training.
  • A proven history of building evaluation pipelines from scratch, addressing biases in LLM-as-a-judge systems, and calibrating automated evaluations against human gold standards.
  • Strong proficiency in Python, PyTorch, distributed training frameworks (DeepSpeed, Megatron, FSDP), and high-performance inference engines (vLLM, TensorRT-LLM).
  • Demonstrated ability to translate ambiguous technical challenges into robust platform features while balancing state-of-the-art research with production reliability and cost constraints.
  • Proven experience acting as a technical leader, mentoring senior researchers or engineers, and influencing cross-functional roadmaps.
  • Fluent spoken and written English at C1 level, with the ability to explain complex technical concepts to internal teams and external clients or the research community.

Responsibilities

  • Define the multi-quarter strategy and architecture for Toloka’s post-training stack, keeping fine-tuning, reinforcement learning, and evaluation capabilities aligned with industry developments.
  • Lead the transition beyond basic supervised fine-tuning into advanced reinforcement-learning frameworks, including GRPO, PPO/DPO, reward modeling, Process Reward Models, and RLAIF, for platform-wide LLM alignment and agent behavior.
  • Design and calibrate automated evaluation ecosystems, including human-aligned LLM-as-a-judge frameworks, dynamic benchmarking suites, and regression-control setups.
  • Lead research initiatives on model distillation, quantization, context compression such as gisting, and speculative decoding to optimize latency-cost trade-offs at platform scale.
  • Architect autonomous guiding agents and tool-use workflows, establishing frameworks for multi-step reasoning, self-correction, and evaluation-driven feedback loops.
  • Provide architectural design reviews and hands-on mentoring to Senior ML Researchers, and establish best practices for reproducible machine-learning research and development.
  • Partner with Product and Engineering directors to translate client challenges into scalable platform architecture.
  • Author research write-ups and blog posts, and give external technology talks.

About the Company

Toloka

Human-in-the-loop data labeling platform

Toloka provides data services with human-in-the-loop for AI development. It connects AI developers with a global pool of over 200,000 contributors across more than 100 countries to perform microtasks like data annotation, content moderation, and sentiment analysis. The platform routes tasks to contributors, collects verified results, and offers specialized services such as dataset creation, model evaluation, and safety testing including red-teaming. Its goal is to deliver reliable, human-verified training data and testing environments that help AI developers build, evaluate, and safeguard advanced models.

Company size
1,001-5,000
Company stage
Late Stage VC
Total funding
$72M
Headquarters
Amsterdam, Netherlands
Founded
2014

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