Python Inference Engineer
TLDR
Build the inference layer, integrate vLLM and TensorRT-LLM, and bring language and multimodal models into production.
What You’ll Do
- 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.
What We’re Looking For
- 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: you think about how systems are used, not just how they work.
- Genuine interest in inference engineering. You do not need direct experience with inference engines, but you should be motivated to learn.
- Good communication and collaboration skills.
Nice to Have
- Experience with vLLM, SGLang, NVIDIA Dynamo, TensorRT-LLM, or a similar inference framework.
- 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 ML, inference, or infrastructure projects.
Benefits
At Gcore, we want you to do your best work and enjoy the journey. Our benefits are designed to support your growth, well-being, and life beyond work:
- Competitive compensation
- Flexible working hours and hybrid or remote options, depending on your role
- Competitive compensation
- Work from anywhere in the world for up to 45 days per year
- Private medical insurance for you and your family*
- Extra paid vacation and sick leave days*
- Support for life’s important moments and celebrations
- Language courses to help you connect and grow
- Modern, welcoming offices with snacks, drinks, and entertainment*
- Team sports and social activities*
*Benefits may vary depending on your location.
Equal Opportunity Employer
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.
Benefits
Flexible Work Hours
Flexible working hours and hybrid or remote options, depending on your role
Free Meals & Snacks
Team sports and social activities*
Health Insurance
Private medical insurance for you and your family*
Learning Budget
Language courses to help you connect and grow
Support for life’s important moments and celebrations
Paid Time Off
Extra paid vacation and sick leave days*
Remote-Friendly
Work from anywhere in the world for up to 45 days per year
Gcore builds a comprehensive infrastructure and software suite that powers the digital experiences of AI, cloud, network, and security services. Aimed at global enterprises, its solutions enhance everything from real-time communications to secure web applications. With a focus on reliability and performance, Gcore stands out as a go-to partner for businesses navigating the demands of the digital landscape.
- Founded
- Founded 2014
- Employees
- 201-500 employees
- Industry
- information technology and services
- Funding stage
- Series A
- Total raised
- $60M raised