AI Platform Evaluation Software Engineer - Autonomous Vehicles

Description
Role Overview
As a member of the core AV software reliability team , you will be responsible for ensuring safe, stable, and scalable Autonomous Vehicle (AV) software releases by turning failures into actionable engineering insights at scale.
This is a software-first, platform-focused role. You will work primarily across the AV platform software stack (frameworks, runtime, services, orchestration, data pipelines) and its interaction with vehicle hardware and compute -not cloud infrastructure or hardware design.
The mission of this role is to:

• Improve learning velocity from failures

• Reduce reliability escapes

• Increase confidence in production readiness

You will do this through intelligent triage, deep software debugging, and AI-assisted failure analysis across simulation, CI, HIL, SIL, and on-road environments , ensuring that failures are:

• Correctly detected and interpreted

• Consistently categorized and de-duplicated

• Rapidly mapped to the right owners and solution space

You will collaborate closely with AV software engineers, ML engineers, systems engineers, test platform owners, and release/safety stakeholders to ensure reliability signals directly influence engineering priorities and release decisions .
If you are passionate about software reliability, failure analysis, and building AI-driven systems that help organizations learn faster from complex ML-based AV software, this role is for you.
Key Responsibilities

• Own the AV software reliability triage framework for the on-vehicle / AV platform stack, defining how failures from simulation, CI, HIL/SIL, and on-road validation are detected, grouped, and escalated into actionable tickets and insights.

• Perform deep debugging and root-cause analysis across:

• AV platform and framework code

• Perception / planning / control software integrations

• ML pipelines and model rollouts

• Vehicle compute and hardware interfaces (sensors, ECUs, networks)
  Connecting failure symptoms (logs, time-series, traces) to clear solution paths and corrective actions.

• Design and evolve automated triage mechanisms and reliability taxonomies that:

• Improve regression detection and signal-to-noise ratio

• Identify flaky tests and intermittent platform issues

• Enable consistent failure classification across teams and releases

• Build and govern reliability data pipelines for AV software:

• Ingest logs, metrics, traces, and test results across CI, simulation, and vehicle runs

• Compute stability trends, recurrence patterns, and systemic risks

• Provide dashboards and views that support day-to-day triage and release readiness reviews

• Apply AI / ML to reliability triage , for example:

• Clustering and de-duplicating failures across large-scale test runs

• Learning-based suggestions for ownership, component mapping, and likely root causes

• LLM-powered summaries of complex failure scenarios for engineers and leadership

• Translate reliability findings into decision-grade communication :

• Influence prioritization of bugs vs. technical debt vs. feature work

• Provide input to go/no-go decisions and safety/release governance

• Create clear narratives and visuals for engineering, safety, and leadership audiences

Required Qualifications

• Strong proficiency in Python for automation, log analysis, data processing, and reliability tooling.

• Proficiency in SQL for querying reliability and test data, building views, and supporting dashboards.

• Proven experience with CI/CD systems (e.g., GitHub Actions, Jenkins, GitLab CI or equivalent) used to run automated tests for complex software systems.

• Hands-on experience implementing ETL/ELT pipelines for reliability, quality, or system health monitoring (e.g., ingesting logs/metrics from test runs, building reliability datasets).

• Solid understanding of software reliability engineering concepts , including:

• Regression tracking

• Flakiness detection and management

• Failure classification and de-duplication

• Release criteria and quality gates for software

• Strong analytical and cross-stack debugging skills in large-scale distributed or real-time software systems , ideally with C++ and/or Python-based services .

• Experience integrating simulation, HIL/SIL, or system-level AV/robotics test signals into automated analysis workflows.

• Track record of effective cross-functional collaboration with software engineering, QA, test platform, and systems teams .

• Ability to operate autonomously in high-ambiguity, safety-critical environments, driving clarity and decisions from noisy data.

• Excellent written and verbal communication skills for presenting data-driven reliability insights to engineers and technical leadership.

• Bachelor's, Master's, or PhD in Computer Science, Electrical/Computer Engineering, Robotics, or a related field - or equivalent software-focused experience .

Preferred Qualifications

• Experience with reliability governance in ML-based or AV systems , including:

• Model rollout policies

• Guardrails / kill-switches

• Shadow or A/B validation strategies

• Familiarity with reliability methodologies (e.g., FMEA, reliability growth analysis, MTBF trends) applied to software and integrated AV platforms.

• Knowledge of AV / ADAS software architectures , including:

• Perception / localization / planning / control pipelines

• On-vehicle platform software, middleware, and simulation-to-road validation loops

• Experience building reliability or analytics pipelines in cloud environments (AWS, GCP, Azure) to support AV software validation at scale.

• Familiarity with observability and visualization tools (e.g., Grafana, Superset, Power BI, or similar) for reliability dashboards and on-call / triage workflows.

• Experience using Jira, GitHub Projects , or similar tools to design reliability triage workflows, routing, SLAs, and dashboards.

• Experience applying AI / ML or LLMs to:

• Log analysis and anomaly detection

• Failure clustering and root-cause suggestion

• Automated summarization of complex test outcomes

Compensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of New York, Colorado, California, or Washington. • The salary range for this role: is $123,200 to $189,100. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position. • Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance. • Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more
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