Senior / Lead Machine Learning Engineer, Inference - Serbia

Inworld, Serbia

What you need

You don't need all of this. But you need enough to make a case.

Inference Optimization. Deep understanding of modern serving frameworks and techniques like vLLM or TRT-LLM.
Model Acceleration. Hands-on experience with quantization, distillation, caching strategies , continuous batching, paged attention, and speculative decoding.
High-Performance Systems. Proficiency in C++, CUDA, Rust, or highly optimized Python. You know how to profile code and squeeze every ounce of performance out of NVIDIA GPUs.
Distributed Systems & Scaling. Experience with Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, and reliably handling thousands of concurrent connections.
Public work. Non-trivial systems programming projects, open-source contributions to major inference engines, or deep-dive technical write-ups.
Full-cycle ownership. You can take a model from the research team, containerize it, optimize its serving, and ensure it runs reliably in production.
Background. PhD in CS, Physics, Math, or equivalent practical experience building backend or ML systems.

Inworld, Serbia

You don't need all of this. But you need enough to make a case.

Inference Optimization. Deep understanding of modern serving frameworks and techniques like vLLM or TRT-LLM.
Model Acceleration. Hands-on experience with quantization, distillation, caching strategies , continuous batching, paged attention, and speculative decoding.
High-Performance Systems. Proficiency in C++, CUDA, Rust, or highly optimized Python. You know how to profile code and squeeze every ounce of performance out of NVIDIA GPUs.
Distributed Systems & Scaling. Experience with Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, and reliably handling thousands of concurrent connections.
Public work. Non-trivial systems programming projects, open-source contributions to major inference engines, or deep-dive technical write-ups.
Full-cycle ownership. You can take a model from the research team, containerize it, optimize its serving, and ensure it runs reliably in production.
Background. PhD in CS, Physics, Math, or equivalent practical experience building backend or ML systems.