WHAT YOU WILL BE DOING
At Our Client´s Motor Company, it is believed freedom of movement drives human progress. With their exciting plans for the future of mobility, they have a wide variety of opportunities for you to accelerate your career potential as you help us define tomorrow´s transportation. Modern vehicles are increasingly software-defined, connected, and intelligent. Delivering a best-in-class ownership and service experience now depends on Our Client´s ability to detect, understand, diagnose, and resolve complex software and electronics issues quickly and accurately. That is why Our Client is investing in an End-to-End Software Diagnostics & Observability initiative focused on transforming how vehicle issues are understood across engineering, diagnostics, and service workflows. We are building state-of-the-art AI-powered Embedded Vehicle Diagnostics capabilities that combine vehicle signals, diagnostics, logs, engineering knowledge, service procedures, and intelligent reasoning to improve case quality, accelerate fault isolation, guide next-best actions, and support scalable human-in-the-loop escalation. This initiative sits at the intersection of embedded systems, cloud services, diagnostics, observability, and AI/ML engineering. Do you want to help define the future of AI-enabled diagnostics for next-generation vehicles?
Our Client´s team is a fast-paced, highly collaborative organization that translates advanced technical strategy into deployable capabilities. If you are passionate about AI/ML, complex systems, embedded software, and solving real-world engineering problems at scale, consider joining our forward-thinking team. As a Systems Engineer - End to End Software Diagnostics & Observability, you will work with a cross-functional team responsible for defining, integrating, and maturing intelligent diagnostic workflows that span embedded vehicle behavior, cloud-based observability, AI reasoning engines, and human support processes. This role is ideal for a highly capable recent graduate or early-career engineer from a top engineering, computer science, or AI/ML program who wants to work on real-world AI systems for software-defined vehicles.
Responsibilities include but are not limited to:
- Help define system-level requirements, interfaces, and workflows for Our Client´s End to End Software Diagnostics & 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.
- Help 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.
- Support design of systems that combine Our Client´s engineering knowledge, diagnostics data, and AI reasoning to isolate likely root causes in embedded vehicle systems.
- 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 Our Client´s-managed cloud environments and workflow systems.
- Help define observability requirements for diagnostic systems, 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.
- Work under the guidance of experienced engineers.
WHAT YOU'LL NEED
Essential:
- Systems Development Life Cycle, Software Systems, Systems Engineering, Product Management, Systems Architecture, Systems Analyst.
- Software Systems - Understand how an application´s components work together, and help troubleshoot issues across the system.
- Systems Analyst - Gather and document user or business requirements, analyze current processes, and translate needs into functional specifications.
- Systems Architecture - Understand or help document how systems and components fit together, including integrations, data flows, and technology choices.
- Systems Development Life Cycle - Participate in the stages of system delivery, from requirements and design through development, testing, deployment, and maintenance.
- Systems Engineering - Help define system requirements and ensure components work together to meet performance, reliability, and operational needs.
- Product Management - Help prioritize features, maintain a product backlog or roadmap, and coordinate with stakeholders and development teams.
- Bachelor´s or Master´s degree in Computer Science, Computer Engineering, Electrical Engineering, Systems Engineering, Artificial Intelligence, Machine Learning, Robotics, Data Science, or 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.
- 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 and requirements authoring with the ability to drive high level abstract conversations with leadership.
- Demonstrated ability through coursework, research, internships, or projects to build or prototype AI/ML enabled systems.
- 4 days per week in office.
Preferred:
- Python, GCP, Java, Artificial Intelligence & Expert Systems.
- Artificial Intelligence & Expert Systems - Understand or contribute to a basic AI or rules-based feature, such as classifying requests or recommending a next step, while recognizing when human review is needed.
- GCP - Use Google Cloud services to deploy or support an application, store data, or monitor a cloud-based system.
- Python - Write or maintain basic Python scripts for automation, data processing, or application functionality.
- Java - Write or maintain Java code for application features, business logic, and error handling.
- 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 evaluation of 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.
- Ability to work well in a collaborative, agile environment with software, embedded, cloud, AI, and product teams.
- Strong curiosity, ownership mindset, and willingness to learn quickly in a technically demanding domain.
- Ability to be detail oriented while understanding broader system and product goals.
