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).
$226,000 - $307,000 a year
Base Salary Range There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.
Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.
