Python Inference Engineer

Gcore is a global provider of infrastructure and software solutions for AI, cloud, network, and security, operating over 210 edge locations, 50 cloud regions, and thousands of GPUs. The company collaborates with technology partners such as Intel, NVIDIA, Dell, and Equinix. The role is part of the Edge Cloud&AI Team, within a global team of over 550 professionals.

• 5+ years of experience writing reliable, well-tested production code.
• Strong Python skills and experience designing production systems.
• Hands-on experience with PyTorch and deploying machine learning models.
• Experience with Linux, Docker, and Kubernetes.
• Experience in at least one relevant area: distributed systems, GPU computing, ML runtimes, model optimization, or cluster scheduling.
• Ability to debug complex problems across software, infrastructure, and hardware.
• Strong sense of developer experience focused on usability.
• Genuine interest in inference engineering; motivation to learn even without direct inference engine experience.
• Good communication and collaboration skills.

Nice to have:

• Experience with vLLM, SGLang, NVIDIA Dynamo, TensorRT-LLM, or similar inference frameworks.
• Experience running GPU workloads in production.
• Knowledge of inference techniques such as quantization, continuous batching, speculative decoding, prefix caching, chunked prefill, or LoRA serving.
• Experience with CUDA, Triton, TensorRT, or other GPU programming tools.
• Experience profiling and improving model latency, throughput, memory use, or GPU utilization.
• Experience with distributed inference, multi-GPU systems, scheduling, or autoscaling.
• Contributions to open-source machine learning, inference, or infrastructure projects.

• Build and improve the inference layer of the Gcore Inference platform.
• Integrate and operate inference frameworks such as vLLM, SGLang, NVIDIA Dynamo, and TensorRT-LLM.
• Bring new language and multimodal models into production.
• Improve inference latency, throughput, memory use, GPU utilization, and cost efficiency.
• Debug performance and reliability issues across model code, inference frameworks, GPU execution, networking, and Kubernetes.
• Work with platform, infrastructure, product, and customer-facing teams to turn inference improvements into reliable product features.
• Contribute improvements to open-source inference projects when appropriate.

We provide equal opportunity to all applicants without regard to race, color, religion, sex, sexual orientation, age, gender identity, gender expression, national origin, disability, or any other legally protected characteristics.