AI is rewriting the rules of software development. Our engineers use AI agents to write, refactor, and ship code at a pace that was impossible two years ago. But that velocity is only real if the infrastructure behind it can keep up. We're looking for an Acceleration-focused Engineer who doesn't just support AI-augmented development, but pushes its boundaries alongside the rest of the team.

This is a highly hands-on role where you'll be an AI-native engineer yourself, using agents and generative tools to build the OS images, CI/CD systems, developer workflows, and internal platform capabilities that help engineers develop, test, ship, and debug software across robot, device, and cloud surfaces. You'll own the bring-up and continuous delivery of full OS images to application and autonomy teams, and you'll design the technical foundations that improve engineering velocity, reduce toil, and increase software quality, making pragmatic architecture decisions based on the organization's stage, scaling needs, and safety/security requirements. Your work determines how quickly the team can go from code change to validated result on real hardware.

A core part of this role is AI enablement: embedding AI-native tooling and agentic automation directly into engineering workflows so that build, test, triage, and deployment become faster, more reliable, and require less manual effort. We're looking for someone who enjoys building robust internal platforms, modern robotics OS stacks, and AI-assisted workflows that other engineers rely on.

Key job responsibilities

As a Builder Acceleration Engineer you'll own the systems that keep the development feedback loop fast and reliable as AI-driven development dramatically increases code throughput. You'll build and maintain CI/CD pipelines, automated test environments, on-target deployment workflows, and developer-facing tooling across a complex embedded platform.

- Design and implement CI/CD pipelines supporting parallel build, test, and deployment across multiple compute targets (Linux application processors, real-time microcontrollers, safety-critical processors)

- Build automated test infrastructure that scales with high commit volume: test orchestration, parallelization, flaky test detection, and feedback loops that give signal fast

- Embed AI-native tooling and agentic automation into engineering workflows: automated failure triage, intelligent test selection, AI-driven code review checks, root-cause analysis for pipeline failures, and self-serve workflows that reduce manual intervention

- Develop on-target deployment automation for target hardware so engineers get hardware-validated feedback without manual flash cycles

- Own image build, release management, and OS image composition including versioning, reproducible builds, commit-to-image traceability, and working knowledge of the platform stack (BSP, kernel configuration, device trees, root filesystem layers)

- Define and track engineering effectiveness metrics (build times, queue times, test failure rates, flaky test trends, deployment lead time) and build developer-facing dashboards that surface actionable insights

- Maintain OTA update infrastructure and data collection pipelines, bringing software to test robot fleets and telemetry back to cloud storage for analysis

- Design and maintain containerized build and development environments that minimize friction in the inner development loop

- Ensure software supply chain integrity through reproducible builds, image signing, artifact provenance tracking, and access controls across the build and release pipeline

A day in the life

You check the CI dashboard and spot a spike in overnight failures from concurrent agent-driven commits. You quarantine a flaky HIL test and push a fix. At standup, you demo the new parallel test runner that cuts pipeline time from 45 minutes to 20. Then you spend a few hours paired with a platform EE and work on the automated flash-and-validate sequence for the new custom hardware boards. You wrap the day working with another SDE to trace why their node passes in the containerized environment but fails on-target, finding a timing difference in the test harness.

BASIC QUALIFICATIONS

- 3+ years of non-internship professional software development experience

- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience

- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field

- - 3+ years of software engineering experience with a focus on CI/CD infrastructure, developer tools, or build systems

- - Proficiency in Python and at least one other infrastructure automation language

- - Experience designing and maintaining CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI, or similar) with a focus on speed and reliability at scale

- - Experience with Linux-based OS development and deployment, including building OS images, BSP/kernel work, init/services configuration, and OTA update systems

- - Familiarity with AI-assisted development workflows and the infrastructure challenges of high-volume automated code generation

- - Experience with containerization and build environment management (Docker, Kubernetes, or similar)

- - Experience with build systems (CMake, Bazel, Make, or similar) and cross-compilation for embedded or multi-architecture targets

PREFERRED QUALIFICATIONS

- - Experience building on-target test infrastructure that deploys and validates software on physical hardware (development boards, HIL rigs, or device farms)

- - Experience with OTA update frameworks or software deployment systems for embedded or IoT devices (SWUpdate, RAUC, Mender, or similar)

- - Experience building developer-facing dashboards or observability tooling (Grafana, custom web UIs, or similar)

- - Experience with cloud infrastructure (AWS) for data pipelines, telemetry collection, or fleet management

- - Background in robotics, consumer electronics, autonomous vehicles, or other complex embedded product development

- - Experience with heterogeneous compute platforms, and ARM-based embedded hardware

- - Experience with simulation or emulation environments for embedded software validation

- - Strong communication skills with ability to explain infrastructure decisions and trade-offs to engineers across disciplines

- - Experience building AI-assisted developer tools or integrating LLMs into engineering workflows (automated triage, test generation, agentic automation, or similar)