Research Engineer (Agentic Models)

United States Digital Space LLC; Berlin, München; Vor Ort; EUR 90.000 - 120.000; Vollzeit

Zusammenfassung

United States Digital Space LLC in Berlin is seeking a Research Engineer to design and optimize SFT and RL post-training pipelines for multi-step coding agents integrated into developer IDEs.

You will work with PyTorch-based stacks, large-scale distributed training on GPU clusters, and evaluation loops to shape models that power our agentic workflows. The role emphasizes end-to-end ownership and collaboration with product and infra teams.

Qualifikationen

• Extensive hands-on experience training LLMs (pre-training, fine-tuning, or post-training) in a research or production setting.
• Strong practical knowledge of PyTorch and large-scale ML tooling.
• Experience with distributed training on GPU clusters and data pipelines.

Aufgaben

• Design, implement, and maintain SFT and RL post-training pipelines for multi-step coding agents.
• Train and adapt LLMs for agent workflows including planning and tool use.
• Build evaluation environments to measure agent behavior and iterate on training data and rewards.
• Collaborate with research, product, and infra teams to ship features.

Kenntnisse

Python

PyTorch

LLM training

Distributed training

Data pipelines

Tools

Megatron

NeMo

verl

Jobbeschreibung

At the company, code is our passion. Ever since we started, back in 2000, we’ve been striving to make the strongest, most effective developer tools on earth. Today, AI-powered assistance and agents are becoming a core part of how developers work in our IDEs.

We’re building multi-step coding agents that can understand large codebases, plan changes, call tools, and iterate with the user. As a Research Engineer in the Agentic Models team, you’ll be responsible for the models, training loops, and evaluation pipelines that power these agents.

You’ll work at the intersection of SFT and RL-style post-training, and product-driven evaluation, using our distributed GPU and MapReduce clusters to ship models into the company products.

As part of our team, you will:

• 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 the company IDEs.
• Build and develop evaluation and simulation environments where coding agents can act, be measured, and compared on realistic developer tasks.
• Design evaluation frameworks and metrics for agent behavior, analyze traces and logs, and close the loop from evaluation back into training, data, and reward design.
• Analyze training and evaluation results to propose and implement improvements to model architectures, training recipes, and datasets.
• Work with large-scale infrastructure, including distributed training on GPU clusters and large MapReduce-style data processing for pre-training and fine-tuning datasets.
• Collaborate closely with research, product, and infrastructure teams to turn high-level product visions into concrete models, experiments, and shipped features.

We’ll be happy to bring you on board if you have:

• Extensive hands-on experience training LLMs (pre-training, fine-tuning, or post-training) in a research or production setting.
• Deep expertise in modern deep learning frameworks such as PyTorch, and specialized LLM training stacks (e.g. Megatron, NeMo, verl, or similar).
• Strong theoretical and practical understanding of LLM fundamentals: architectures, tokenization, data pipelines, batching, mixed precision, distributed training, and debugging unstable runs.
• The ability to own projects end to end, starting from a high-level problem or product pain point and overseeing it through the design, experimentation, implementation, and iteration phases.
• A product-aware mindset – you care about how developers actually use agents and can translate product needs and failure modes into modeling and evaluation work.
• At least 3 years of Python experience writing clean, maintainable code in modern ML codebases.

Our ideal candidate would have experience with:

• ML orchestrators and workflow tools such as Kubeflow, Dagster, Airflow, ZenML, and/or job schedulers like Kubernetes or SLURM.
• Large-scale data and training pipelines, e.g. MapReduce-style clusters, multi-node GPU training, or workloads on the order of 1M+ CPU/GPU hours.
• Designing and maintaining evaluation pipelines for LLMs or agents, including metrics, dashboards, experiment tracking, and automated regression checks.
• AI agent development, such as tool-using agents, planners, or multi-step coding workflows, and familiarity with agentic frameworks or patterns.
• Experiment tracking and observability using tools like Weights & Biases, MLflow, Langfuse, or similar.
• Inference optimization and serving optimized models in production.

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