Senior Software Developer: Models Team (Token Factory)

Cache-aware routing

NUMA-aware deployments

KV-cache offloading

Disaggregated serving architectures

Autoscaling with high-speed model loading over InfiniBand / RoCE

The team maintains and extends forks of leading inference frameworks such as vLLM and TRT-LLM . We have deep expertise in production-scale model serving and regularly support the Solutions Architects team on the most demanding customer PoCs.

To operate efficiently at scale, we invest heavily in tooling and automation.

Performance, quality, and smoke-testing frameworks

Hyperparameter optimization for inference framework configurations

Gibberish detection systems

Automated rollout pipelines for inference framework upgrades

Diagnostics and observability tooling

Traffic replay systems

Automated search for optimal serverless deployment configurations

We collaborate closely with model builders, open-source communities, Nebius Cloud teams, and hardware vendors to continuously improve our serving infrastructure. The team is highly goal-oriented and outcome-driven, with a strong focus on delivering results rather than following rigid processes.

We are currently a team of eight engineers distributed across Europe, with members based in the Netherlands, the United Kingdom, Germany, and Latvia. Our workflows are optimized for remote collaboration. At the same time, we meet in person every one to two months at one of our locations to work together, brainstorm new ideas, and plan upcoming milestones.

While many team members joined without extensive AI/ML experience, we have rapidly developed strong expertise in large-scale model serving and AI infrastructure.

Our work is deeply integrated with the broader cloud and infrastructure ecosystem. We primarily use Go and Python to build and scale backend systems. We collaborate closely with teams working on cloud infrastructure, observability, reliability, fault tolerance, and platform engineering. The challenges we solve sit at the intersection of distributed systems, high-performance computing, and modern AI infrastructure.

Experience serving LLMs in production

Strong Python and/or Go programming skills

Experience designing and operating highly scalable, highly available distributed services

Contributions to vLLM, SGLang, TRT-LLM, or NVIDIA ecosystem open-source projects

Deep understanding of KV cache management, speculative decoding, and quantization

Experience with LLM evaluation frameworks

Hands-on experience with performance benchmarking and optimization

Deep understanding of Kubernetes

Familiarity with distributed serving architectures and autoscaling

Knowledge of InfiniBand, RoCE, or high-performance networking

Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI

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