Robotics Infrastructure Engineer – CI/CD for Autonomy

ArcBest | Vaux

Boston (MA)

On-site

USD 120,000 - 150,000

Full time

46 hours ago

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Job summary

ArcBest is seeking a Robotics Software Engineer III - Infrastructure to design and enable scalable infrastructure for the full robotics software lifecycle, across source, build, test, simulate, deployment, and monitoring. You will standardize CI/CD pipelines and artifact versioning, supporting ML model deployment and data lineage for autonomy systems.

The role emphasizes reducing friction for autonomy engineers, enabling reproducible builds, and aligning with safety and compliance requirements.

Qualifications

Bachelor's Degree in Computer Science, Robotics, Electrical Engineering, Mechanical Engineering, or related field, required
Master's Degree or equivalent industry experience in robotics, autonomous systems, or distributed systems, preferred
4 years of experience in software infrastructure, platform engineering, or DevOps position(s), preferred
Hands-on experience with Linux-based systems and containerized environments, required
Experience working with CI/CD systems, build tooling, and artifact management solutions, required
Familiar with Bitbucket and Jira automation, preferred
Practical experience with MLOps, including dataset and model versioning and deployment pipelines, preferred
Familiar with robotics middleware such as ROS or ROS2, preferred
Experience supporting complex, distributed, real-time, or hardware-integrated systems, required
Proven ability to design and maintain systems used by multiple teams and products, preferred

Responsibilities

Design and enable shared infrastructure supporting the end-to-end robotics software lifecycle: Source, Build, Test, Simulate, Deploy, Monitor.
Standardize build systems and CI/CD pipelines across robotics software, embedded systems, and simulation environments.
Define and manage artifact versioning strategies for software binaries, firmware, configuration, calibration data, maps, and vehicle-specific parameters.
Establish platform standards, reference architectures, and best practices used across multiple product teams.
Enable large-scale Software-in-the-Loop (SIL), Hardware-in-the-Loop (HIL), and regression simulation workflows.
Integrate simulation results into CI pipelines with clear pass/fail criteria and reporting.
Support scenario-based testing, coverage tracking, and regression analysis.
Provide infrastructure and tooling to improve correlation between simulation results and real-world vehicle behavior in collaboration with simulation and vehicle integration teams.
Design and evolve shared infrastructure supporting the lifecycle of machine learning models used in autonomy systems.
Enable dataset versioning, lineage tracking, training reproducibility, and model promotion workflows.
Define standards for model packaging, deployment to vehicles, runtime monitoring, and rollback mechanisms.
Partner with perception and autonomy teams to enable safe and performant model deployment, while domain teams retain ownership of model design and performance outcomes.
Build and maintain reliable pipelines for deploying software and models to development vehicles, test fleets, and production systems.
Enable vehicle-level observability through logging, metrics, and distributed tracing.
Support rapid diagnosis of field issues and feedback loops into development, testing, and simulation environments.
Enable traceability for safety-critical systems, including the ability to identify what software, model, and configuration ran on a specific vehicle at a given time.