Staff Data Scientist - Behavior Evaluation

The Program Management Office (PMO) at Zoox is responsible for driving cross-functional program planning and execution at the highest level. We partner with leaders from across the company in vehicle development, system design, software engineering, manufacturing, fleet operations, and business functions to develop and deliver major corporate milestones on the path to launch, and beyond. Our program managers contribute to the end to end success of technical, integrated product milestones involving both of our vehicle platforms - the Toyota Highlander test fleet and Zoox’s purpose-built robotaxi. In this role, you will: Own the fleet validation operational strategy: Lead the end-to-end strategy and execution of mileage accumulation for both the physical test fleet and simulation assets, to enable Zoox’s geofence expansion across all cities. Partner closely with upstream teams (e.g. Autonomy, Systems Design & Mission Assurance, Places & Build) and downstream teams (e.g. Data Science, Fleet Operations) to ensure critical inputs are available, and results are delivered; you will ultimately "own the outcome" of test fleet data collection. This is a high leverage, high impact role. Usher in a simulation-first methodology : Act as the cross-functional catalyst for scaling simulated mileage accumulation. Drive alignment between Autonomy, System Design & Mission Assurance, and Simulation teams to ensure clear requirements are set, and simulation platforms are resourced to meet exponentially growing data needs. Identify and resolve critical bottlenecks : Develop rigorous tracking mechanisms that model the impact of upstream dependencies (e.g., map content deliverables and safety requirements). You’ll think critically and work cross-functionally to squash inefficiencies in upstream and downstream pipelines and systems. Partner collaboratively wi

The Prediction & Behavior ML team is responsible for developing machine learning (ML) algorithms that learn and predict behaviors from data, applying them both on-vehicle to influence driving behavior and off-vehicle to provide ML capabilities to simulation and validation. Given the tight integration of behavior forecasting and motion planning, our team collaborates closely with the Planner team to advance overall vehicle behavior. We also work closely with our Perception, Simulation, and Systems Engineering teams to accelerate our ability to validate our driving performance. As a Learned Trajectory Machine Learning Engineer you will be responsible for developing deep learned models that produce trajectories for our vehicles to drive. Given the tight integration of behavior prediction and motion planning, you will closely collaborate with the Planner and Perception teams in the advancement of our overall vehicle behavior. In this role, you will: You will develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for our autonomous vehicle. You will also work on techniques to estimate the quality of those driving plans along the dimensions of safety, progress, comfort etc. You will leverage our large-scale machine learning infrastructure to discover new solutions and push the boundaries of the field You will develop metrics and tools to analyze errors and understand improvements of our systems You will collaborate with engineers on Perception, Planning, and Simulation to solve the overall Autonomous Driving problem in complex urban environments Qualifications BS, MS, or PhD degree in computer science or related field Experience with training and deploying transformer-based model architectures and reinforcement learning Experience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelines Fluency in Pyt

The Zoox 3D Sensor Simulation team’s work is at the core of creating in-the-loop simulation and high fidelity synthetic data for training and testing autonomous vehicle AI. We are looking for a machine learning professional with experience applying ML to sensor data, such as LiDAR, radar, and cameras. This role lies at the intersection of machine learning and 3D simulation at Zoox, ranging from learned sensor models to neural 3D reconstruction. By improving the realism of simulated sensors (lidar, radar, and cameras), you’ll enable the Zoox robotics autonomy stack to learn, improve, and enhance the mobility of humans. In this role, you will: Design, train, and validate ML models that improve the realism of simulated lidar, radar, and camera data in autonomous vehicle (AV) simulation pipelines. Collaborate with Synthetic Data and HIL Sim teams to integrate 3DGS and NeRF-based 3D reconstruction with simulation of all sensor modalities. Build and curate training datasets from real and simulated sensor data, including GenAI-based data augmentation. Partner with Sim V&V and Perception engineers to define sensor realism metrics and close the sim-to-real gaps with the greatest impact on autonomy. Mentor engineers and interns, and establish best practices for training and deploying ML on Zoox sensor data. Qualifications: 2+ years of prior experience as a Technical Lead 5+ years of experience developing ML models in Python with PyTorch or TensorFlow <

Zoox is looking for an experienced software engineer to work on large-scale simulation pipelines used to validate the behavior of the Zoox self-driving vehicle. These are data and GPU intensive workloads built on Ray.io and Kubernetes. Given the massive scale and criticality of these pipelines, ensuring their reliability and efficiency has a significant impact on the company's ability to safely and quickly iterate on autonomy development. We are a small, scrappy team within the larger Autonomy organization. Although this role primarily involves off-vehicle pipelines, you will work closely with engineers developing the on-vehicle algorithms and models in our autonomy stack. We stay close to the end users - autonomy engineers - and think about the end to end use case for these validation pipelines. This is a hands-on role with a high degree of independence and ownership. You will be expected to contribute towards the framework’s architecture, reliability, efficiency, and grow its capabilities to support new use cases. You should have a track record of keeping production systems running with high availability. Experience with robotics or autonomous systems is not required but an understanding of the robotic data lifecycle is preferred. In this role, you will: Improve the cost efficiency, reliability, and performance of our validation and simulation pipelines Create production-grade APIs, SDKs, and tools to enable a varied set of validations of autonomous behaviors Improve the ML training pipelines supporting the autonomous behavior org Qualifications Bachelor’s degree in Computer Science or related field and 8+ years of industry experience Experience optimizing large-scale distributed systems for cost and efficiency Experience with AWS or similar providers Proficiency with Python and familiarity with C++ Bonus Qu

Zoox is looking for an experienced Staff Software Engineer to build, scale, and operate our custom High-Performance Computing infrastructure. As Zoox scales its autonomous vehicle development, our HPC platform must keep pace with rapidly growing compute, storage, and scheduling demands across the company. You will modernize our HPC platform—built on industry-leading technologies like Ray.io, SLURM, and Kubernetes—with a focus on reliability, scalability, and world-class developer velocity. These HPC services form the backbone of development workflows across all Zoox software teams, from data engineering to training our AI models in Perception, Planner, Prediction, to Simulation, and more. You will have a direct impact on the productivity and effectiveness of every engineering team at Zoox. The position comes with a high degree of independence and the opportunity to define Zoox's HPC platform strategy, both technically and organizationally. You will work closely with stakeholders in Autonomy and Software teams to understand their workload requirements and translate them into robust, scalable infrastructure. In this role, you will: Design and implement core services and abstractions for distributed compute infrastructure supporting hundreds of thousands of concurrent jobs Work with customer teams and other infrastructure teams to build a multiyear software engineering roadmap for the HPC platform Lead multi-quarter, cross team initiatives that drive org-wide improvements Create production-grade APIs, SDKs, and tools that make it easy for engineers across Zoox to run large-scale distributed workloads Design and improve job scheduling algorithms and auto-scaling policies to maximize reliability and resource availability Design multi-region orchestration strategies that optimize for data locality, reliability, and performance Identify and resolve syste

Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation. About the Role Join our Enterprise Platform team and build the infrastructure foundations that enable the world's largest organizations to run Replit within their security and compliance boundaries. As a Software Engineer on this team, you'll design and implement the deployment flexibility, networking capabilities, authorization systems, and data controls that enterprises require, from single-tenant architectures and private connectivity to custom policy enforcement and customer-managed encryption. You'll work at the intersection of cloud infrastructure and enterprise requirements, partnering with Platform Engineering, Security, and Sales to ship capabilities that unlock adoption at demanding organizations. What You'll Do Build enterprise deployment infrastructure: Design and implement single-tenant and dedicated deployment options, enabling customers to run Replit with the isolation guarantees their security posture requires. Implement private networking capabilities: Build VPC peering, private connectivity, and static IP configurations that allow enterprises to integrate Replit into their existing network architectures. Design authorization services: Build the authorization infrastructure that enforces custom enterprise policies; enabling fine-grained access controls, custom permission models, and policy enforcement that integrates with customers' existing identity and governance systems. Ship data protection features: Implement bring-your-own-key (BYOK) encryption, customer-managed keys, and data residency controls that give enterprises ownership over their most sensitive data. Develop infrastructure automation: Write Terraform modules and automation that enable reliable, repeatable enterprise deployments across reg