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вчера
ml engineer for coding agents
ориентир по рынку
вакансия
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206 055 ₽
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описание
JetBrains develops tools for software developers, including AI-powered assistance and agents integrated into its IDEs. The company is building multi-step coding agents that understand large codebases, plan changes, use tools, and iterate with users.
задачи
- Design, implement, and maintain SFT and RL post-training pipelines for multi-step coding agents
- Train and adapt LLMs for agent workflows, including planning, tool use, and multi-step interactions inside JetBrains IDEs
- Build evaluation and simulation environments where coding agents can act and be measured and compared on realistic developer tasks
- Design evaluation frameworks and metrics for agent behavior, analyze traces and logs, and use evaluation results to improve training, data, and reward design
- Analyze training and evaluation results and implement improvements to model architectures, training recipes, and datasets
- Work with large-scale infrastructure, including distributed training on GPU clusters and MapReduce-style data processing for pre-training and fine-tuning datasets
- Collaborate with research, product, and infrastructure teams to turn product visions into models, experiments, and shipped features
требования
- Extensive hands-on experience training LLMs in a research or production setting
- Deep expertise in PyTorch and specialized LLM training stacks such as Megatron, NeMo, or verl
- Strong theoretical and practical understanding of LLM architectures, tokenization, data pipelines, batching, mixed precision, distributed training, and debugging unstable runs
- Ability to own projects end to end, from a high-level problem or product pain point through design, experimentation, implementation, and iteration
- Product-aware mindset, with the ability to translate developer needs and agent failure modes into modeling and evaluation work
- At least 3 years of Python experience writing clean, maintainable code in modern ML codebases
- Будет плюсом: experience with ML orchestrators and workflow tools such as Kubeflow, Dagster, Airflow, or ZenML, and job schedulers such as Kubernetes or SLURM; large-scale data and training pipelines, including MapReduce-style clusters, multi-node GPU training, or workloads of 1M+ CPU/GPU hours; designing and maintaining LLM or agent evaluation pipelines; AI agent development and agentic frameworks or patterns; experiment tracking and observability tools such as Weights & Biases, MLflow, or Langfuse; inference optimization and production model serving
условия
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навыки
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