Technical Project Manager
Role Overview
Early-career systems engineering role on an end-to-end software diagnostics and observability initiative for software-defined vehicles.
Work spans embedded vehicle behavior, cloud observability, AI reasoning, and human support processes.
The initiative combines vehicle signals, diagnostics, logs, engineering knowledge, service procedures, and intelligent reasoning to improve issue resolution.
Key Skills for This Role
Full Job Posting
Role overview
Early-career systems engineering role on an end-to-end software diagnostics and observability initiative for software-defined vehicles.
Work spans embedded vehicle behavior, cloud observability, AI reasoning, and human support processes.
The initiative combines vehicle signals, diagnostics, logs, engineering knowledge, service procedures, and intelligent reasoning to improve issue resolution.
Responsibilities
- Define system-level requirements, interfaces, and workflows for the diagnostics and observability initiative.
- Support AI-powered diagnostics for issue detection, case intake, root-cause isolation, and guided repair.
- Connect embedded, cloud, data, and AI/ML domains and translate business, service, and engineering needs into technical requirements.
- Support case intake assistance, knowledge retrieval, diagnostic reasoning, decision support, validation, and orchestration.
- Define evidence collection requirements for diagnostic codes, vehicle data, logs, traces, module state, and procedural outcomes.
- Help combine engineering knowledge, diagnostic data, and AI reasoning to isolate likely root causes.
- Evaluate AI behavior using diagnostic evidence, service data, engineering content, and observability signals.
- Integrate containerized AI solutions into managed cloud environments and workflow systems with internal teams and suppliers.
- Define logs, metrics, traces, dashboards, alerts, and escalation requirements.
- Participate in integration, issue triage, root-cause analysis, testing, and deployment of diagnostic capabilities.
Essential qualifications
- Bachelor’s or master’s degree in a related computer science, engineering, AI/ML, robotics, or data science field.
- 3–6 years of related experience, including internships, research, academic projects, or full-time work.
- Strong AI/ML foundation and practical familiarity with machine learning, LLMs, retrieval, inference, model evaluation, and data pipelines.
- Strong proficiency in Python and familiarity with AI/ML prototyping workflows.
- Familiarity with software engineering fundamentals, APIs, Git-based development, and containerized applications.
- Ability to turn ambiguous problems into structured technical requirements; strong communication and requirements-authoring skills.
- Demonstrated ability to build or prototype AI/ML-enabled systems through coursework, research, internships, or projects.
Preferred qualifications
- GCP, Java, and artificial intelligence or expert systems experience are listed as preferred; Python is also required elsewhere in the posting.
- Preferred experience includes LLMs, semantic retrieval, vector search, ranking, agents, AI assistants, and evaluation for grounding and traceability.
- Preferred tools include PyTorch, TensorFlow, scikit-learn, LangChain, Vertex AI, BigQuery, Docker, GitHub, CI/CD, Dynatrace, or Grafana.
- Embedded software, vehicle diagnostics, distributed systems, platform integration, and cloud-native service experience are advantageous.
- Collaborative agile working, curiosity, ownership, attention to detail, and willingness to learn are valued.
Work arrangement
- The role requires working in the office four days per week.
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