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).
Skills Required
- Deep experience in system and performance optimization on CPU/GPU systems for low latency or high throughput
- Expertise with real-time systems and constraints (processing latency, memory utilization, memory bandwidth)
- Expertise in model quantization (PTQ, QAT) and mixed-precision inference (INT8, FP8, FP4, BF16/FP16)
- Experience compressing, accelerating, and deploying complex models (LLMs, VLMs, foundation models) for power- and thermal-constrained vehicle SoCs
- Proficiency developing and optimizing custom ML ops and TensorRT Plugins with efficient CUDA kernel implementations
- Production-level C++ (C++14/17/20) programming skills
- Production-level Python programming skills
- Experience developing concurrent, memory-safe, real-time inference code for edge devices
- Architect and implement model conversion and compilation pipelines using TensorRT for edge deployment
- Prior experience in high-performance robotics applications (AV/drones/robots)
- Familiarity with autonomous driving perception algorithms (temporal 3D detection, BEV, 3D occupancy) and multi-modal sensor processing
- Experience with end-to-end autonomous driving paradigms and edge deployment technologies (e.g., TensorRT-LLM)
Zoox Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Zoox and has not been reviewed or approved by Zoox.
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Healthcare Strength — Healthcare is extensive, with broad medical and vision options, company‑paid disability coverage, and multiple mental‑health resources. Feedback suggests coverage breadth and auxiliary programs support a wide range of needs.
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Parental & Family Support — Family supports include paid parental leave, additional pregnancy disability time, fertility coverage, and adoption/surrogacy assistance. Backup care and family‑oriented programs further reinforce support across life stages.
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Wellbeing & Lifestyle Benefits — Day‑to‑day perks are robust, including free daily meals, fitness subsidies, commuter support, and on‑site amenities. Feedback suggests these lifestyle benefits enhance convenience and workplace experience, especially for office‑based roles.
Zoox Insights
What We Do
Zoox is an autonomous mobility company that was founded to provide a safer, cleaner, and more enjoyable future on the road. To achieve that goal, the company has spent the past 10 years creating a purpose-built robotaxi that gives the world a better way to ride.
Why Work With Us
At Zoox, we are working to solve one of the greatest technological challenges of our generation. From the beginning, we have been focused on our goal of reimagining transportation from the ground up. We are a mission-driven community of innovators working together to create a safer, cleaner, and more enjoyable future on the road.
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