Design, build, and optimize containerized inference execution for the latest 3D VLMs from NVIDIA, turning research work into production-grade, highly optimized software (NIMs, NVIDIA Inference Microservices)
Develop benchmarks to validate the models accuracy and performance (latency, throughput, scalability)
Release and maintain the models and their pipelines throughout their lifecycle (bug fixes, security patches)
Contribute VLM-related features to Open-Source projects like vLLM
Collaborate closely with Research and Product teams and influence our common roadmaps

Requirements

Master's of Science in Computer Science + 3 years, or Electrical Engineering, Bachelor of Science (or equivalent experience) + 5 years of experience.
History of building, validating and releasing production-grade AI distributed systems, backend services, microservices, and cloud technologies.
Deep technical expertise in distributed applications using Docker, Kubernetes, endpoints and their APIs (REST, gRPC), Helm.
Hands-on experience with modern inference platforms (vLLM, SGLang, Torch, TRT, TRT-LLM).
Proficiency with Python and C++.
Excellent software engineering fundamentals (source control, CI/CD, testing/validation, packaging, containerization).
Excellent written, visual, and verbal communication.
Curiosity and drive to learn new technologies and partner across teams and functions.
Track record contributing to open-source or production-grade software.
Experience with ML model engineering: training, fine-tuning, distillation, quantization.
Experience with low-level optimization of ML models (CUDA kernels).
Strong fundamentals in 3D graphics, 3D computer vision or neural reconstruction (NERFs, Gaussian Splats).

Benefits

equity
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Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonC++DockerKubernetesRESTgRPCHelmvLLMSGLangTorch

Soft Skills

communicationcuriositycollaborationdrive to learn

Certifications

Master's of Science in Computer ScienceBachelor of Science in Electrical Engineering