Systems Engineer, End-to-End Software Diagnostics and Observability (AI/ML)

Job Description

Systems Engineer, End-to-End Software Diagnostics and Observability (AI/ML)

This role sits at the intersection of embedded systems, cloud services, diagnostics, observability, and AI/ML engineering, a great fit for a highly capable recent graduate or early-career engineer from a top engineering, computer science, or AI/ML program who wants real hands-on experience with AI systems for software-defined vehicles.

Responsibilities

Work with a cross-functional team defining, integrating, and maturing intelligent diagnostic workflows spanning embedded vehicle behavior, cloud-based observability, AI reasoning engines, and human support processes

Help define system-level requirements, interfaces, and workflows for the diagnostics and observability initiative

Support development of AI-powered embedded vehicle diagnostics capabilities that improve issue detection, case intake quality, root-cause isolation, and guided repair

Work across embedded, cloud, data, and AI/ML domains to connect vehicle diagnostics with intelligent reasoning and observability workflows

Translate business, service, and engineering needs into technical requirements for diagnostic systems, AI engines, APIs, workflow orchestration, and support tooling

Support AI/ML driven capabilities such as case intake assistance, knowledge retrieval, diagnostic reasoning, decision support, validation, and orchestration

Define and refine requirements for diagnostic evidence collection, including DTCs, PIDs, Freeze Frame data, logs, event traces, module state, and procedural outcomes

Participate in evaluation and validation of AI system behavior using diagnostic evidence, service data, engineering content, and observability signals

Work with internal teams and suppliers to integrate containerized AI solutions into cloud environments and workflow systems

Help define observability requirements including logs, metrics, traces, dashboards, alerts, and escalation workflows

Participate in system integration, issue triage, root-cause analysis, and cross-functional technical problem solving

Support rapid iteration, testing, and deployment of AI-enabled diagnostic capabilities into engineering and non-production environments

Communicate technical tradeoffs, risks, and recommendations clearly to engineering teams, product teams, and leadership

Required qualifications

Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Systems Engineering, Artificial Intelligence, Machine Learning, Robotics, Data Science, or a related field

3-6 years of experience in AI/ML engineering, embedded software, systems engineering, cloud engineering, or related areas, through internships, research, academic projects, or full-time work

Strong academic foundation in AI/ML engineering with practical familiarity in machine learning, LLMs, retrieval workflows, inference systems, model evaluation, and data pipelines

Strong proficiency in Python

Familiarity with AI/ML prototyping and engineering workflows, including training, inference, prompt-based systems, retrieval-augmented workflows, embeddings, ranking, or reasoning pipelines

Familiarity with software engineering fundamentals, APIs, Git-based development, and containerized application workflows

Genuine interest in embedded systems, vehicle diagnostics, software-defined vehicles, and intelligent support workflows

Ability to translate ambiguous problem statements into structured technical requirements and system behavior

Strong written and verbal communication skills, with requirements authoring ability and comfort driving high-level, abstract conversations with leadership

Demonstrated ability, through coursework, research, internships, or projects, to build or prototype AI/ML enabled systems

Nice to have

Education from a highly regarded engineering, computer science, or AI/ML program with strong evidence of technical rigor

Hands-on experience with LLMs, semantic retrieval, vector search, ranking systems, agent-based workflows, or decision-support systems

Experience with PyTorch, TensorFlow, scikit-learn, LangChain, Vertex AI, BigQuery, or similar AI/ML and cloud tools

Experience building chatbots, copilots, AI assistants, search systems, or reasoning systems

Familiarity with evaluating AI systems for grounding, confidence, traceability, explainability, and policy compliance

Familiarity with embedded software systems, electronic control modules, diagnostics, or connected vehicle technologies

Exposure to DTCs, PIDs, Freeze Frame data, logs, vehicle network data, or diagnostic workflows

Familiarity with GCP, Docker, GitHub, CI/CD, and observability tools such as Dynatrace or Grafana

Experience through internships, research, or projects involving distributed systems, platform integration, or cloud-native services

Comfort working in a collaborative, agile environment alongside software, embedded, cloud, AI, and product teams

Strong curiosity, an ownership mindset, and a willingness to learn quickly in a technically demanding domain

Ability to stay detail oriented while keeping sight of broader system and product goals

This employer uses both human and technology-assisted tools to support candidate screening and assessment. Final hiring decisions are made by people.