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.

About the Role

The Challenge

Do you know what it takes to evolve "some dbt models" into a fully governed, well-tagged data platform that every function in the company trusts? Are you ready to take IaC from 60% to 100% coverage while building multi-region resiliency for sensitive financial data?

The Team and Scope

As our Data and Platform Engineering Manager, you'll lead a team of six engineers — three data and three platform — reporting directly to the CTO. You'll steward the full lifecycle from raw ingestion to governed data assets, while growing Caribou's GCP infrastructure toward complete automation and cost efficiency. This role sits at the heart of Caribou's engineering leadership team — a meaningful seat at the table as we scale toward our next stage.

Our Stack and Philosophy

We're on GCP (GKE, Cloud Storage, CloudSQL, BigQuery, Cloud Composer) and use dbt and Pulumi. We care more about your engineering fundamentals and curiosity than your specific history with these tools. If you're experienced in AWS, Snowflake, or Terraform and excited to apply those principles to our stack, we want to talk to you.

Your First 90 Days

Your primary mission will be to lead your data engineers, alongside our analytics and data science teams, in maturing our medallion and marts architecture. We aim to deliver material protection against data evolution for the organization by the second half of 2026.

What You Will Do

• Shape the architectural vision for our data platform, ensuring high-quality, self-service data assets across all business functions
• Lead the SDLC for your team, delivering platform and infrastructure improvements consistently and with clear communication
• Strengthen Caribou's GCP infrastructure by growing IaC coverage, optimizing cloud spend, and building platform resiliency
• Collaborate with Analytics and Product to understand platform needs and translate that context into a prioritized roadmap
• Mentor and coach engineers to deepen their expertise across data modeling, platform engineering, and technical leadership
• Help shape Caribou's engineering culture, contributing a perspective that touches every part of the business

Candidates are welcome to work remotely from the states of AZ, CO, DC, IL, MD, TX, and VA with a preference for the Chicago IL, Denver CO, or Phoenix AZ areas.  Caribou does provide the option to work in-office or hybrid from our Denver, CO or Chandler, AZ offices.  We may consider remote candidates in CA, FL, MA, NY, OR, WA, and WI, subject to additional approval. Eligibility by state is subject to change.  

Please let us know if you need accommodations at any point during the interview process.

What We Look For

• Experience leading engineers who build and mature data platforms — from raw ingestion pipelines through governed assets business teams rely on
• Fluency with the modern data stack: dbt, a cloud warehouse (BigQuery or equivalent), orchestration tooling, and sound data modeling patterns
• Cloud infrastructure fluency — IaC tooling (Terraform or equivalent), cost discipline, and the operational rigor a regulated financial environment demands
• Collaborative leaders who favor context over command and control, measuring success by the growth of those around them
• Curious, life-long learners equally at home in a data modeling discussion and a deep infrastructure review, bringing the same rigor to both.

We know strong candidates rarely check every box, and backgrounds in this space take many shapes — analytics engineers who grew into platform ownership, SREs who moved into data, DevOps practitioners who found their way to EM. If this role excites you, we'd love to hear from you.

Interview Roadmap:

• Recruiter phone interview (30 mins)
• At-home coding assessment (60 mins)
• Hiring Manager video interview (60 mins)
• Final interview loop - (4) one-hour video interviews scheduled across 1-4 business days

How we will take care of you

Everyone at Caribou is a valued team member. Our compensation and benefits package includes: 

• Competitive compensation: $175k - $215k
• Eligible for annual performance based bonus
• Equity options
• 401k savings program
• Generous paid time off including: Flexible Time Off (FTO) for all employees and 100% paid parental leave for all parents.
• Robust wellness benefits including company-paid plans for health, dental, vision, mental health, disability and basic life insurance.
• Optional benefits to suit your individual circumstances such as HSAs, FSAs, supplemental life and medical insurance, and pet insurance.
• Up to $1,000 per year for eligible professional development expenses.

ABOUT ROCKET MONEY 🔮

Rocket Money’s mission is to meaningfully improve the financial prosperity of millions of people. Rocket Money offers members a unique understanding of their finances and a suite of valuable services that save them time and money – ultimately giving them a leg up on their financial journey.

ABOUT THE TEAM 🤝

Data Platform Engineers at Rocket Money further our mission by building and maintaining the infrastructure that enables our company to understand our users and products through reliable, scalable data systems. We build the foundational platform that ingests, processes, and serves data—enabling our Data Analytics, Machine Learning, and Software Engineering teams to build data products efficiently. We work closely with engineering teams to ensure data is captured correctly at the source, design resilient pipelines, and create self-service capabilities that empower stakeholders.

The Data Platform team is in an exciting phase of growth and formalization. While we have established tools and processes in place (dbt, BigQuery, Terraform), we're actively building the comprehensive standards, practices, and systems that will scale with Rocket Money as it grows. We're looking for engineers who can take ownership and evolve workflows into reliable, well-documented, production-grade systems. This is an opportunity to shape architectural decisions, establish team practices, and define how Rocket Money works with data for years to come.

We have a strong preference for process-oriented systems thinkers who excel in balancing stakeholder requirements, technical debt, and organizational complexity—and who thrive in environments where they need to create structure from ambiguity. You'll need to be comfortable making principled decisions, taking ownership not just of code but of outcomes, and operating with significant autonomy as we build the platform together.

ABOUT THE ROLE 🤹

In this role, you will:

• Be an end-to-end owner of our data platform infrastructure, ensuring its security, usability, and performance. Work closely with analytics engineers, machine learning engineers, and software engineers to ensure the platform meets their needs.
• Make collaborative decisions about data tooling, pipeline design, and governance, and implement opinionated interfaces that facilitate easy and best-practice-aligned development for other teammates.
• Continuously improve failure rates for data sources by moving alerting “to the left”; i.e. catching and quarantining bugs/failures as close to the source as possible.
• Analyze patterns in how source data is generated, modeled, and consumed. Work with stakeholders to implement the best solution when new data sources are added.
• Take ownership of existing tools and workflows that may be functional but lack formal structure, documentation, or reliability measures. You'll assess what's working, identify gaps, and systematically improve systems to production-grade quality.
• Document everything you build knowing that you're creating the foundation others will build upon. Your runbooks, architecture decision records, and system documentation will become the institutional knowledge of our data platform.
• Proactively communicate with multiple stakeholders on platform capabilities, technical constraints, architectural decisions, project priorities, and platform support.
• Confidently juggle multiple projects and priorities in our fast-paced environment and work with stakeholders and platform teammates to ensure infrastructure changes, migrations, and improvements are delivered on schedule.
• Automate aggressively and deliberately; anything from GitHub Actions to Slack Workflows to minimize repetitive tasks. Effectively judge when the level of effort is appropriate and avoid over-engineering.

ABOUT YOU 🦄

• You have 6+ years of experience working with data infrastructure, data engineering, or platform engineering within a fast-paced environment. You are highly proficient with SQL, Python, and cloud-based Infrastructure-as-Code (e.g. Terraform), and comfortable working with bash/shell scripting.
• You have 4+ years of production experience with modern data stacks including data warehouses (BigQuery, Snowflake, or Redshift), orchestration tools, managed ingestion services, and infrastructure as code (Terraform, Pulumi, or CloudFormation).
• You have 2+ years of experience building and maintaining production data pipelines, whether through ELT tools, custom applications, streaming systems, or event-driven architectures.
• You've successfully "professionalized" data infrastructure before—taking scrappy, working systems and evolving them into reliable, well-documented, production-grade platforms. You can articulate what "production-ready" means in a data context.
• You have a bias toward action and aren't paralyzed by imperfect solutions. You understand when "good enough for now with a plan to improve" beats "perfect but six months late." You ship incrementally and iterate based on feedback.
• You're comfortable being the first person to tackle a problem. You don't need extensive mentorship or detailed tickets—you can take a high-level business need and figure out the technical approach. That said, you know when to ask for help and can articulate what you need.
• You take ownership seriously—not just of writing code, but of outcomes. When you build something, you implement monitoring, write runbooks, create alerts, and ensure it can be maintained by others. You think about the full lifecycle of systems, not just initial delivery.
• You have strong opinions, weakly held. You can make and defend architectural decisions, but you're open to feedback and willing to change course when presented with better information. You can disagree and commit.
• You understand that "building from scratch" doesn't mean rejecting existing tools—it means thoughtfully selecting, configuring, and integrating managed services and open-source solutions to create a cohesive platform. You know when to build and when to buy.
• You have experience making big changes to critical data infrastructure. You’ve successfully re-architected, migrated, or upgraded data tooling that has strict SLA’s, without significantly affecting downstream stakeholders.

Bonus points if:

• You have led a data infrastructure migration or modernization project where you defined the vision, approach, and implementation.
• You have created internal tools, frameworks, or CLIs that improved how teams work with data (not just one-off scripts).
• You have established data platform best practices like CI/CD workflows, testing frameworks, or observability standards where none existed.
• Expertise in cloud platforms and technologies analogous to our stack:
  • Our stack: GCP (BigQuery, Datastream, Cloud Functions, Vertex AI, GCS), dbt, Fivetran, Postgres, Python, Terraform, Looker, Retool
  • Analogous experience: AWS (Redshift, DMS, Lambda, SageMaker, S3) or Azure (Synapse, Data Factory, Functions), Snowflake, Airbyte/Stitch, infrastructure as code tools, BI platforms

WE OFFER 💫

• Health, Dental & Vision Plans
• Life Insurance
• Long/Short Term Disability
• Competitive Pay
• 401k Matching
• Team Member Stock Purchasing Program (TMSPP)
• Learning & Development Opportunities
• Tuition Reimbursement
• Unlimited PTO
• Daily Lunch, Snacks & Coffee (in-office only)
• Commuter benefits (in-office only)

Additional information: Salary range of $160,000 - $200,000 + bonus + benefits. Base pay offered may vary depending on job-related knowledge, skills, and experience.

Rocket Money, Inc. is an Affirmative Action and Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

The mission of Speechify is to make sure that reading is never a barrier to learning.

Over 50 million people use Speechify’s text-to-speech products to turn whatever they’re reading – PDFs, books, Google Docs, news articles, websites – into audio, so they can read faster, read more, and remember more. Speechify’s text-to-speech reading products include its iOS app, Android App, Mac App, Chrome Extension, and Web App. Google recently named Speechify the Chrome Extension of the Year and Apple named Speechify its 2025 Design Award winner for Inclusivity.  

Today, nearly 200 people around the globe work on Speechify in a 100% distributed setting – Speechify has no office. These include frontend and backend engineers, AI research scientists, and others from Amazon, Microsoft, and Google, leading PhD programs like Stanford, high growth startups like Stripe, Vercel, Bolt, and many founders of their own companies.

Overview

We're looking to hire for our Data side of our AI team at Speechify. This role is responsible for all aspects of data collection to support our model training operations. We are able to build high-quality datasets at petabyte-scale and low cost through a tight integration of infrastructure, engineering, and research work. We are looking for a skilled Software Engineer to join us.

What You’ll Do

• Be scrappy to find new sources of audio data and bring it into our ingestion pipeline
• Operate and extend the cloud infrastructure for our ingestion pipeline, currently running on GCP and managed with Terraform.
• Collaborate closely with our Scientists to shift the cost/throughput/quality frontier, delivering richer data at bigger scale and lower cost to power our next-generation models.
• Collaborate with others on the AI Team and Speechify Leadership to craft the AI Team’s dataset roadmap to power Speechify’s next-generation consumer and enterprise products.

An Ideal Candidate Should Have

• BS/MS/PhD in Computer Science or a related field.
• 5+ years of industry experience in software development.
• Proficiency with bash/Python scripting in Linux environments
• Proficiency in Docker and Infrastructure-as-Code concepts and professional experience with at least one major Cloud Provider (we use GCP)
• Experience with web crawlers, large-scale data processing workflows is a plus
• Ability to handle multiple tasks and adapt to changing priorities.
• Strong communication skills, both written and verbal.

What we offer

• A fast-growing environment where you can help shape the company and product.
• An entrepreneurial-minded team that supports risk, intuition, and hustle.
• A hands-off management approach so you can focus and do your best work.
• An opportunity to make a big impact in a transformative industry.
• Competitive salaries, a friendly and laid-back atmosphere, and a commitment to building a great asynchronous culture.
• Opportunity to work on a life-changing product that millions of people use.
• Build products that directly impact and support people with learning differences like dyslexia, ADD, low vision, concussions, autism, and more.
• Work in one of the fastest-growing sectors of tech, the intersection of artificial intelligence and audio.

Compensation: The United States base salary range for this full-time position is $140,000-$200,000 + bonus + equity depending on experience

Think you’re a good fit for this job? 

Tell us more about yourself and why you're interested in the role when you apply.
And don’t forget to include links to your portfolio and LinkedIn.

Not looking but know someone who would make a great fit? 

Refer them! 

Speechify is committed to a diverse and inclusive workplace. 

Speechify does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.

The Role

As a Senior Full Stack Engineer, you will be a key leader across the entire software development lifecycle, from conceptual design to deployment and monitoring. You will be responsible for building robust, scalable backend services using Python and AWS Serverless technologies (Lambda, DynamoDB, SQS, EventBridge), developing event-sourced systems that provide complete state history and auditability.

A critical component of this role is a demonstrated fluency with modern AI/ML development tools (e.g., GitHub Copilot, Claude Code, agentic coding tools, large language models). We are looking for candidates who not only use these tools daily but can articulate a clear, thoughtful perspective on their limitations, security implications, and the strategic opportunities they create for the engineering profession.

Key Responsibilities

• Lead the design and implementation of highly scalable, cost-efficient serverless backend services using Python and AWS (Lambda, API Gateway, DynamoDB, SQS, EventBridge).
• Architect and build event-sourced systems and event-driven pipelines, ensuring reliable message processing, idempotency, and eventual consistency across distributed services.
• Collaborate with product managers and designers to translate requirements into technical designs and shippable code.
• Implement best practices for security, logging, monitoring, and infrastructure-as-code (e.g., Terraform or CloudFormation).
• Perform rigorous code reviews, mentor junior and mid-level engineers, and drive technical excellence across the team.
• Actively research, propose, and integrate new technologies and patterns, with a focus on optimizing development velocity and system performance.
• Clearly articulate a professional point of view on how to effectively, securely, and ethically integrate AI tools into the software development lifecycle.

Qualifications

Required

• 5+ years of professional experience in full-stack software development.
• Expert-level proficiency in Python for backend development.
• Strong working proficiency with Node.js.
• Deep practical experience designing, deploying, and maintaining applications using serverless architectures (preferably AWS Lambda, API Gateway, and managed services).
• Hands-on experience with DynamoDB, including single-table design patterns, GSI/LSI strategies, and performance optimization at scale.
• Solid understanding of event sourcing and event-driven architecture patterns, including experience with message brokers or streaming services (e.g., SQS, SNS, EventBridge, or Kinesis).
• Experience with CI/CD pipelines, automated testing, and Infrastructure-as-Code.
• Demonstrated Fluency in AI-Assisted Development: Candidates must be able to demonstrate practical, hands-on experience using AI-powered coding tools to achieve specific, quantifiable productivity gains, while also clearly understanding and mitigating the associated risks (e.g., code quality, security, IP concerns).

Nice to Have

• Experience with advanced AWS services (e.g., Kinesis, SageMaker, Step Functions).
• Familiarity with TypeScript for robust front-end or Node.js development.
• Proficiency with a modern front-end framework (e.g., React, Vue, or Angular).
• Experience working in a regulated or compliance-focused environment.
• A Bachelor’s or Master’s degree in Computer Science or a related technical field.

AI and the Evolution of Software Engineering & Ops

All candidates must be prepared to discuss and demonstrate the following during the interview process:

• Practical AI Tool Usage: Specific examples of AI tools used in professional or personal projects to enhance productivity, solve complex problems, or accelerate development. If you have a preferred agentic tool setup, please share!
• Understanding of AI Limitations: A clear understanding and articulation of the current technical, ethical, and accuracy limitations of modern AI tools in the engineering context.
• Strategic AI Opportunity: A viewpoint on how AI tools create new opportunities for software engineering, such as shifting focus from boilerplate code to complex problem-solving or architectural design.
• Evolution of the Engineering Role: A well-articulated point of view on how your role has already changed (e.g., new workflows, skills, ways of collaborating) due to the introduction of AI and how you expect the role to evolve in the coming years based on your assessment of coming advancements in AI tools and technologies.

Application Process

To apply, please submit your resume and a cover letter. Your cover letter should be informed by the “AI and Forward-Looking Requirements” section above.

The hiring process includes:

• Initial screening email
• A Technical Assessment Interview
• A Panel Interview

Salary Range $140,000 - $175,000