招聘

Senior DL Software Engineer, Model Optimization and Edge Deployment - Autonomous Vehicles
US, CA, Santa Clara
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On-site
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Full-time
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Today
NVIDIA is at the forefront of the AI revolution, specifically in the constantly evolving field of Embodied AI. We are seeking a high-caliber Deep Learning Engineer to bridge the gap between cutting-edge multimodal architectures and real-time robotic execution for autonomous vehicles. In this role, you will design and implement SOTA algorithms to make LLM/VLM fast, lean, and reliable enough to power an end-to-end driving stack. You won’t just be "running" models; you will be re-architecting them for the edge, ensuring that models capable of complex scene reasoning can operate within the strict latency and safety constraints of an AV compute platform.
What You’ll Be Doing:
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Develop SOTA model optimization techniques, such as speculative decoding with block diffusion, KV cache streaming, and Prefill–Decode separation, etc. to boost E2E model performance for production deployments.
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Implement advanced compression techniques including Quantization (FP4/FP8), pruning, and knowledge distillation to minimize model footprints without compromising safety-critical accuracy.
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Design high-performance optimization strategies for inference, including automated model sharding (tensor/sequence parallelism) and the development of efficient attention kernels optimized for KV-caching.
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Conduct deep, layer-by-layer model profiling to identify compute and memory bottlenecks, driving targeted optimizations for real-time execution.
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Leverage the Py Torch ecosystem to extract standardized model graph representations and automate deployment pipelines for TensorRT conversion.
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Scale DL model performance across diverse NVIDIA edge architectures, maximizing the throughput of specialized accelerators on the road.
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Architect the software interface to seamlessly integrate and interact with large-scale models within a high-performance C++ production environment.
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Partner with research, TensorRT, and Cosmos teams to translate breakthrough innovations into shipping product solutions.
What We Need to See:
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PhD with 4+ years, MS with 6+ years, or BS (or equivalent experience) with 8+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field.
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Expert-level proficiency in Py Torch, JAX, or similar machine learning frameworks.
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Sophisticated proficiency with modern LLM/VLM inference stacks, such as vLLM, TensorRT-LLM and SGLang.
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A proven track record of training, deploying, or optimizing large-scale DL models in production environments.
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Deep familiarity with NVIDIA’s deep learning SDKs, specifically TensorRT and CUDA.
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Strong understanding of GPU architecture, the compilation stack, and the ability to debug end-to-end performance across the hardware/software boundary.
Ways to Stand Out from the crowd:
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Deep experience with LLM, VLM, and VLA model optimization, specifically tailored for real-time robotic control, embodied AI, and autonomous decision-making.
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Proven track record of implementing low-bit inference
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Prior experience writing custom high-performance kernels using CUDA, Triton, or CUTLASS to accelerate non-standard neural network layers and specialized attention mechanisms.
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Active contributions to open-source inference and optimization libraries such as vLLM, SGLang and TensorRT-LLM.
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Thorough understanding of the unique constraints of real-time robotics, including safety-critical determinism, hardware-in-the-loop (HIL) testing, and ultra-low latency requirements.
At NVIDIA, we’re dedicated to making self-driving vehicles a reality and believe this technology can save millions of lives. Join a team of innovative thinkers at one of the world’s most respected technology companies. If you’re motivated, curious, and ready to make a difference, we’d love to meet you! We believe that building self-driving vehicles will be a defining contribution of our generation (e.g. traffic accidents are responsible for ~1.25 million deaths per year world-wide). We have the funding and scale, but we need your help on our team. NVIDIA is widely considered to be one of the technology world’s most desirable employers with some of the most forward-thinking people in the world working here. If you're entrepreneurial and autonomous, we want to hear from you!
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until April 25, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
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关于NVIDIA

NVIDIA
PublicA computing platform company operating at the intersection of graphics, HPC, and AI.
10,001+
员工数
Santa Clara
总部位置
$4.57T
企业估值
评价
4.4
10条评价
工作生活平衡
2.8
薪酬
4.2
企业文化
4.3
职业发展
4.1
管理层
3.8
78%
推荐给朋友
优点
Cutting-edge technology and innovation
Excellent compensation and benefits
Great team culture and collaboration
缺点
Work-life balance challenges
High pressure and stress
Long hours required
薪资范围
67个数据点
Junior/L3
Mid/L4
Senior/L5
Junior/L3 · Analyst
7份报告
$170,275
年薪总额
基本工资
$130,981
股票
-
奖金
-
$155,480
$234,166
面试经验
5次面试
难度
3.0
/ 5
面试流程
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Onsite/Virtual Interviews
5
Team Matching
6
Offer
常见问题
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Past Experience
新闻动态
Negotiating NVIDIA's Offer
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Team-dependent experience; sink-or-swim culture that rewards high performers but can be overwhelming. No politics, flat structure, but demanding workload with some teams requiring evening/weekend work.
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Technical bar is high with 4-6 rounds. Process takes 4-8 weeks. Expect C++ questions, LeetCode medium, and system design. Difficulty rated 3.16/5.
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