Senior/Staff Software Engineer - Machine Learning & System Optimization
Foster City, CA
- Pay
USD 254,000–350,000/year — pay source
Salary 254,000 – 350,000 USD per year About Zoox
Read the full posting- Work setting
Hybrid — work setting source
Work setup in the employer’s posting: hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type Full-time
Employment type in the employer’s posting: Full-time
From the employer’s posting
What you’ll work on
Full postingAllocate CPU, GPU and interconnect resources across on-robot perception models
Share and fuse models across the perception stack to improve compute utilization
Quantize, prune and fine-tune large multi-modal, LLM and VLM models for the vehicle
Build TensorRT model conversion and compilation pipelines for edge deployment
Write low-latency, memory-safe C++ and CUDA code for real-time inference
From the employer’s posting
Allocate and distribute system resources (CPU/GPU/interconnect) to various models and inference engines running on the robot.
Spearhead cross-cutting initiatives that allow for better compute utilization through sharing/fusing models and better scheduling strategies.
Optimize large-scale models (Multi-Modal Sensor Fusion models, LLMs, VLMs) using advanced quantization (PTQ, QAT), pruning, mixed-precision inference frameworks, and parameter-efficient fine-tuning (LoRA, QLoRA).
Architect and implement model conversion and compilation pipelines using TensorRT for edge deployment.
Write production-level, low-latency, and memory-safe C++ and CUDA code for real-time inference on vehicle systems.
What you’ll bring
All qualificationsCore experience
- Deep experience in system and performance optimization in CPU/GPU systems designed for low latency or high throughput.
- Prior experience in high-performance robotics applications such as AV/drones/robots.
- Deep expertise in working with real-time systems & required constraints such as processing latency, memory utilization, and memory bandwidth pressure.
- Familiarity with SOTA autonomous driving perception algorithms (temporal 3D object detection, BEV, 3D Occupancy Networks) and multi-modal sensor processing (Vision, LiDAR, Radar).
- Deep expertise in model quantization (PTQ, QAT) and mixed-precision inference frameworks (INT8, FP8, FP4, BF16/FP16).
- Experience with end-to-end autonomous driving paradigms (VLM/VLA models, Foundation models) and edge deployment technologies (e.g., TensorRT-LLM).
Qualification wording
Deep experience in system and performance optimization in CPU/GPU systems designed for low latency or high throughput.
Prior experience in high-performance robotics applications such as AV/drones/robots.
Deep expertise in working with real-time systems & required constraints such as processing latency, memory utilization, and memory bandwidth pressure.
Familiarity with SOTA autonomous driving perception algorithms (temporal 3D object detection, BEV, 3D Occupancy Networks) and multi-modal sensor processing (Vision, LiDAR, Radar).
Deep expertise in model quantization (PTQ, QAT) and mixed-precision inference frameworks (INT8, FP8, FP4, BF16/FP16).
Experience with end-to-end autonomous driving paradigms (VLM/VLA models, Foundation models) and edge deployment technologies (e.g., TensorRT-LLM).
Tools in this posting
- C++
- Python
Source — Tool mentions in context
- Architect and implement model conversion and compilation pipelines using TensorRT for edge deployment. - Write production-level, low-latency, and memory-safe C++ and CUDA code for real-time inference on vehicle systems. Qualifications:
- Proficiency in low-level programming for AI accelerators, specifically developing and optimizing custom ML OPs and TensorRT Plugins with efficient CUDA kernel implementations. - Production-level C++ (14/17/20) and Python programming skills, with experience developing concurrent, memory-safe, real-time inference code for edge devices. Bonus Qualifications:
About Amazon
Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market.
In the employer’s words · Read in context
Posting record
Report an errorOpen 4 monthsWe first saw this posting 121 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jul 1, 2026 · 121 days ago
- Recorded sightings
- 174
- Last seen by us
- Oct 30, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
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Job description
The Perception team is pioneering the development of a multi-modality foundation model to drive the next generation of autonomous system intelligence.
As a Machine Learning and System Optimization Engineer, you will orchestrate and allocate overall system capacity to various core perception models running on-bot, as well as drive large initiatives that allow for more efficient inference by sharing various parts of the perception stack with one another.
You will focus on bringing highly efficient, production-ready large-scale models to our on-vehicle stack. We are looking for experts with hands-on experience compressing, accelerating, and deploying complex models, including LLMs, VLMs, or foundation models, for power- and thermal-constrained vehicle SoCs.
In addition, you will optimize ML models, write custom CUDA kernels, and build highly concurrent inference code to ensure real-time, deterministic execution on edge devices.
In this role, you will:
-
Allocate and distribute system resources (CPU/GPU/interconnect) to various models and inference engines running on the robot.
-
Spearhead cross-cutting initiatives that allow for better compute utilization through sharing/fusing models and better scheduling strategies.
-
Optimize large-scale models (Multi-Modal Sensor Fusion models, LLMs, VLMs) using advanced quantization (PTQ, QAT), pruning, mixed-precision inference frameworks, and parameter-efficient fine-tuning (LoRA, QLoRA).
-
Architect and implement model conversion and compilation pipelines using TensorRT for edge deployment.
-
Write production-level, low-latency, and memory-safe C++ and CUDA code for real-time inference on vehicle systems.
Qualifications:
-
Deep experience in system and performance optimization in CPU/GPU systems designed for low latency or high throughput.
-
Deep expertise in working with real-time systems & required constraints such as processing latency, memory utilization, and memory bandwidth pressure.
-
Deep expertise in model quantization (PTQ, QAT) and mixed-precision inference frameworks (INT8, FP8, FP4, BF16/FP16).
-
Proficiency in low-level programming for AI accelerators, specifically developing and optimizing custom ML OPs and TensorRT Plugins with efficient CUDA kernel implementations.
-
Production-level C++ (14/17/20) and Python programming skills, with experience developing concurrent, memory-safe, real-time inference code for edge devices.
Bonus Qualifications:
-
Prior experience in high-performance robotics applications such as AV/drones/robots.
-
Familiarity with SOTA autonomous driving perception algorithms (temporal 3D object detection, BEV, 3D Occupancy Networks) and multi-modal sensor processing (Vision, LiDAR, Radar).
-
Experience with end-to-end autonomous driving paradigms (VLM/VLA models, Foundation models) and edge deployment technologies (e.g., TensorRT-LLM).
Salary
254,000 – 350,000 USD per year
Employment type
Full-time
Source details
Source notes
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- Pay
Salary 254,000 – 350,000 USD per year About Zoox
- Location & working pattern
Foster City, CA
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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