You will architect and implement model conversion and compilation pipelines to deploy machine learning models onto edge hardware. Additionally, you will maintain the model release registry and perform rigorous latency and quality benchmarking to ensure safety-critical performance.
Design, build, and maintain ETL/ELT pipelines while structuring data models to support reliable reporting and dashboard performance. Partner with business stakeholders to translate reporting needs into actionable data models and migrate existing data delivery to the Databricks platform.
The Test Safety Engineer will develop and maintain operational safety concepts, processes, and risk assessments for autonomous heavy trucks. They will collaborate with cross-functional teams to monitor safety metrics, investigate incidents, and ensure compliance with safety standards during testing operations.
You will be responsible for building and maintaining robust CI/CD pipelines and managing production infrastructure to support autonomous vehicle software development. Additionally, you will troubleshoot production incidents, participate in on-call rotations, and create comprehensive documentation for engineering tools.
The role involves designing, developing, and improving machine learning models for 3D perception systems within an autonomous driving stack. You will own the end-to-end model development process, from data preparation and training to integration and performance optimization.
The Senior Product Cybersecurity Architect will define and implement security controls across the autonomous vehicle platform throughout the product lifecycle. They will collaborate with cross-functional teams to integrate security into design, conduct threat modeling, and ensure compliance with safety and security standards.
Design and deploy offline 3D reconstruction, lane line detection, and mapping modules to generate high-quality annotations for autonomous vehicle sensor data. Lead technical projects, mentor team members, and define data governance standards to improve perception and mapping model performance.
Design and deploy data pipelines and quality control systems to support autonomous vehicle perception model training. Provide technical leadership and statistical reporting to ensure high-quality data delivery for downstream ML teams.
The role involves building and maintaining production-grade data pipelines to process petabytes of autonomous vehicle log data. You will collaborate with ML engineers to deploy tagging models and manage AWS infrastructure to support large-scale data processing.
The role involves acting as an embedded point of contact between the simulation platform team and autonomy teams to drive the adoption of simulation tools. You will implement end-to-end data flows, onboard autonomy models for large-scale replay and evaluation, and debug issues across the full stack.
You will lead, grow, and set the technical direction for the Offline Perception team, overseeing the development of auto-labeling pipelines and training data. You will also manage the team's roadmap, represent strategy to senior leadership, and collaborate with cross-functional stakeholders to ensure high-quality delivery.
You will design, build, and maintain scalable data infrastructure and pipelines to process high-bandwidth sensor logs from autonomous vehicles. Additionally, you will develop tools for dataset curation, labeling, and visualization to support perception, planning, and simulation engineering teams.
The Senior SOTIF Engineer will lead safety assessments and risk analyses to ensure autonomous trucking systems meet rigorous safety and reliability standards. They will collaborate with cross-functional teams, including AI-ML and software developers, to define acceptance criteria and implement design improvements.
You will lead the Scene Generation team in developing neural rendering and generative models for autonomous vehicle simulation. This involves setting the technical roadmap, mentoring engineers, and ensuring the delivery of production-quality software for sensor simulation.
The Safety Data Analyst will develop and implement safety performance indicators to assess risk across on-road and simulation-based datasets. They will also partner cross-functionally to ensure data-driven safety decisions and maintain rigorous documentation for regulatory readiness.
Manage and optimize cloud costs across multi-cloud environments using FinOps best practices and Lean-Agile methodologies. Design cost models, implement tagging strategies, and build dashboards for financial and technical stakeholders.
Develop and implement BI solutions, dashboards, and data models to monitor business performance and efficiency. Partner with stakeholders to translate business goals into actionable insights and automate reporting systems.
Design, develop, and deploy production machine learning models for 3D perception and Bird's Eye View capabilities. Collaborate cross-functionally to integrate these models into the autonomous truck software stack and mentor junior engineers.
Design, develop, and maintain Hardware-in-the-Loop (HiL) test benches to validate autonomous driving software and system-level functionality. Execute test strategies and automated scripts to ensure safety and performance across the autonomy stack.
Develop machine learning workflows and AI development tooling to solve complex business problems for automated trucks. Lead projects and collaborate with stakeholders to implement state-of-the-art technology into production.
Lead the technical development of motion planning, prediction, and decision-making systems for autonomous trucks. Drive architectural strategy and engineering excellence while collaborating across autonomy, controls, and safety teams to deploy scalable planning capabilities.
Develop and maintain high-fidelity vehicle dynamics models and simulation platforms to validate autonomous driving software for trucks. Collaborate with autonomy engineering teams to implement requirements and ensure model fidelity through automated regression frameworks.
Design and implement cloud-based pipelines to convert multi-sensor data into VLM/VLA training datasets. Develop VLM-assisted auto-labeling systems and curate long-tail scenarios to improve autonomous driving model performance.
The role involves employing statistically rigorous analyses to quantify risk and evaluate the safety of autonomous driving systems. You will develop production-ready analysis workflows and communicate defensible findings to technical leaders and executives.
Lead complex data science projects to improve manufacturing processes and develop machine learning workflows from analysis to embedded deployment. Define data ingestion and governance standards while providing technical leadership and mentoring to team members.
Develop and maintain automated test frameworks and Python-based tooling for component-level validation using Hardware-in-the-Loop (HIL) systems. Collaborate with cross-functional teams to debug hardware/software issues and improve the reliability of autonomous driving software.
Architect and optimize distributed data pipelines to extract safety-critical driving events from massive multi-sensor logs. Develop ML-assisted algorithms for scenario classification and manage the integration of tagged events into an observations database.
This role provides strategic leadership to implement, monitor, and qualify the testing and release workflow for scalable Level 4 autonomous truck software, ensuring safe and successful product delivery across engineering divisions. The engineer will define strategic approaches for verification and validation lifecycles, covering multiple test levels and environments like HIL and simulation, while reporting critical gaps to leadership.
