Lucid Motors is seeking a Senior SQA and Test Infrastructure Development Engineer to help build the next generation of test, automation, and validation systems that power our in‑vehicle compute platforms, embedded controllers, and advanced driver‑assistance technologies. You will play a key role in ensuring that Lucid’s software and hardware meet the highest standards of safety, reliability, and performance—supporting everything from firmware development to large‑scale automotive validation.

This role sits at the intersection of DevOps, embedded systems, and automotive test engineering, enabling fast, safe, and scalable development for the world’s most advanced electric vehicles.

What you’ll be doing

• Architect and maintain DevOps pipelines and test automation infrastructure optimized for automotive and embedded development workflows, including cross‑compilation, ECU flashing, and hardware‑in‑the‑loop (HIL) validation
• Build internal tools and services that streamline embedded software testing, firmware deployment, and continuous integration for safety‑critical systems
• Collaborate with Embedded Software, Vehicle Integration, and Test Engineering teams to design scalable test environments for Lucid’s in‑vehicle compute modules and real‑time embedded platforms
• Implement containerized and cloud‑native systems using Docker and Kubernetes to orchestrate distributed test execution across lab and cloud resources
• Integrate automotive‑specific workflows into CI/CD systems, including static analysis, MISRA compliance, traceability, and automated regression testing
• Develop monitoring, logging, and diagnostic tools to ensure high availability and performance of embedded test infrastructure
• Support and extend test frameworks used for ECU validation, vehicle‑level testing, and manufacturing test

What we need to see

• BS/MS in Computer Science, Electrical Engineering, Computer Engineering, or related field
• 5+ years of experience in DevOps, embedded test infrastructure, or automation tooling
• Strong proficiency in Python and experience with frameworks such as Flask or Django
• Experience with embedded Linux, cross‑compilation, and deploying applications to embedded targets
• Familiarity with automotive communication protocols such as CAN bus, or Automotive Ethernet
• Experience with HIL/SIL systems, embedded test benches, or automotive validation environments such as vector Tools
• Proficiency with version control systems such as GIT or GitLab
• Strong Linux knowledge—deployment, debugging, system monitoring, and automation
• Familiarity with bash scripting and Linux‑based automation workflows

Ways to stand out from the crowd

• Background in deploying or maintaining embedded test automation systems, including HIL rigs or ECU validation setups
• Experience with real‑time operating systems (RTOS) or embedded firmware development
• Exposure to manufacturing test environments for automotive ECUs, embedded boards, or in‑vehicle compute modules
• Familiarity with cloud platforms (AWS, Azure, GCP) and infrastructure‑as‑code tools such as Terraform or Ansible
• Passion for building robust CI/CD pipelines and developer‑facing infrastructure for embedded systems

Bonus Qualifications – Agentic AI Tools & Intelligent Automation

Lucid is increasingly leveraging agentic AI systems to accelerate development, automate complex workflows, and enhance test intelligence. Experience with any of the following is a strong plus:

• Building or integrating AI‑driven DevOps agents for automated debugging, test generation, or pipeline optimization
• Experience with LLM‑powered tools for code analysis, anomaly detection, or automated documentation
• Familiarity with AI‑assisted test case generation, including reinforcement‑learning‑based or model‑based testing
• Experience using or developing autonomous agents that interact with CI/CD systems, embedded hardware, or cloud infrastructure
• Knowledge of AI‑driven observability platforms for predictive monitoring, log intelligence, or automated root‑cause analysis
• Understanding of agent orchestration frameworks such as LangChain, LangGraph, or similar tools
• Experience integrating AI agents with automotive workflows such as ECU flashing, HIL orchestration, or embedded diagnostics

How you’ll demonstrate Ownership

We are seeking an AI Platform Engineer to join the Cognite Atlas AI Product team in Phoenix, AZ, to engineer, build, and operate the production-grade, multi-cloud platform that enables our internal and partner teams to build, deploy, and manage industrial AI agents. You will be responsible for creating the core services, frameworks, and infrastructure for our "agent builder workbench" and agent runtime, focusing on scalability, reliability, cost-efficiency, and security. Your work will directly impact industrial efficiency and sustainability, which is critical to our mission of powering a high-tech, sustainable, and profitable industrial future.

The Impact you bring to Cognite

• Design, build, and maintain the core Python SDKs and services for the Atlas AI platform. Create clean abstractions that empower Solution Engineers to easily define and test agents and workflows.
• Build the core agentic runtime, ensuring it is scalable, meets its SLOs, and can reliably manage the state, orchestration, and execution of industrial agents.
• Develop a robust, governed, and secure framework for AI agent tool-use. Engineer the platform components that allow solution engineers to safely add new tools (e.g., API calls, database queries) and that manage the secure execution, monitoring, and access control for those tools.
• Manage the LLM serving layer, including deploying and optimizing models for low-latency/high-throughput inference. Build and maintain model routing logic to select the most appropriate model (e.g., performance vs. cost) for a given task.
• Implement evaluation and observability for all AI services. Create standardized frameworks for systematically evaluating the performance, accuracy, cost, and safety of LLMs and agentic workflows. Drive the implementation of robust, automated testing strategies for LLM-based systems.
• Own the full development lifecycle for services in a production SaaS environment. This includes establishing automated code coverage goals, rigorous code reviews, defining SLOs, participating in on-call rotations, and ensuring a fast and effective incident response process.
• Work closely with the Lead Architect to translate the technical vision into implemented, production-grade services. Act as a key partner for the Solution Engineers (your internal customers) to understand their needs and abstract common patterns into reusable, robust platform components.
• Stay up to date on the latest developments in the field, and mentor junior developers.

Required Qualifications

• Bachelor's or Master’s degree in Computer Science or a related field, or equivalent practical experience.
• 8+ years of professional experience in backend software engineering, platform engineering, or MLOps, with a proven track record of architecting and operating complex systems at scale.
• 2+ years of hands-on experience building applications or platforms on top of AI/ML models or LLMs.
• Expert-level proficiency in Python and a strong background in software architecture, robust API design, and building maintainable, well-documented SDKs for other developers.
• Hands-on experience with Kubernetes (K8s) and building services on managed PaaS in a multi-cloud environment (AWS, Azure, GCP). Strong understanding of Infrastructure as Code (e.g., Terraform).
• Proven experience building and operating production-grade SaaS software. Understanding of the full development life cycle, including CI/CD, monitoring, telemetry, and on-call incident response.
• Practical experience with LLM orchestration frameworks (Bedrock, Vertex, Semantic Kernel, LangChain).
• Strong verbal and written communication skills, with the ability to articulate complex technical designs and decisions clearly.

Preferred Experience

• Hands-on experience deploying and managing LLMs in production using high-performance serving frameworks.
• Experience with MLOps/LLMOps tools for tracing, monitoring, and evaluating LLM applications (LangSmith, Arize, Phoenix, or equivalent).
• Experience with RAG Infrastructure, embedding generation pipelines, vector database integrations, and high-performance vector similarity search APIs.

A snapshot of our many perks and benefits as a Cogniter

 

 

 

Learn more about us

 

 

 

• Impact 2025
• Cognite's Industrial AI: Moonshot
• We’re globally recognized domain experts with an international presence that spans Phoenix, Houston, Oslo Tokyo, Bengaluru, and Abu Dhabi.

 

 

Equal Opportunity

Cognite is committed to creating a diverse and inclusive environment at work and is proud to be an equal opportunity employer. All qualified applicants will receive the same level of consideration for employment.