Your key responsibilities will include:

• Leading the definition, design, and integration of new emissions concepts, including hardware, controls, and packaging across the engine, transmission, generator, and aftertreatment domains.
• Overseeing the development and demonstration of system capabilities to deliver emissions targets alongside other vehicle attributes.
• Managing workstreams and project deliverables within the Global Technical Development System (GTDS) framework to ensure research goals are met on schedule.
• Directing sensitivity studies and trade-offs between competing attributes such as fuel economy, performance, and driveability using Design of Experiments (DOE) methods and hybrid operating strategies.
• Ensuring the development of high-quality systems engineering documentation, including Boundary Diagrams, P-Diagrams, and Design Failure Mode and Effects Analysis (DFMEA).
• Supervising development testing across various platforms, including engine, powertrain, and hub dynamometers, as well as vehicle and virtual (analytical) simulations.
• Leading the team in the hands-on commissioning of prototype systems on dynamometers and in development vehicles, including troubleshooting complex mechanical, electrical, and CAN communication issues.

To succeed in this role, you'll need a Bachelor's degree in Mechanical Engineering, Electrical Engineering, or a related Systems Engineering field, along with 10+ years of experience in propulsion systems, emissions aftertreatment, or powertrain calibration. A Master's degree or PhD in Engineering with a focus on Emissions, Controls, or Propulsion is also desirable.

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Your key responsibilities will include:

• Developing test strategy and use cases/scenarios for L3 and L4 autonomous driving products
• Defining test strategy and execution plan on vehicle platform
• Formulating test cases and defining critical performance metrics to ensure compliance with functional and safety requirements
• Collaborating with different stakeholders to define KPIs and evaluate product progress by closing the loop with test data/results
• Establishing strong multi-functional relationships with collaborators across engineering, safety, and product teams
• Scaling and innovating the product ODDs by designing campaigns and roadmap
• Leading L3/L4 vehicle integration and retrofitting, managing end-to-end processes to advance testing and validation schedules
• Spearheading data triage and root cause analysis, driving issue resolution and generating comprehensive test reports

We are looking for a highly skilled and experienced Senior Software Test Engineer who has a strong background in software testing, particularly in the area of autonomous driving. You should have a deep understanding of functional safety (ISO 26262), Safety of the Intended Functionality (SOTIF / ISO 21448), and V-model validation methodologies.

In addition to your technical expertise, you should have strong leadership and interpersonal skills, with the ability to drive alignment across large organizations. You should also be able to provide technical mentorship to junior engineers and lead by example in establishing rigorous testing and validation standards.

If you are a motivated and detail-oriented individual who is passionate about autonomous technology, we encourage you to apply for this exciting opportunity.

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Responsibilities:

Model Development & Training: Train, fine-tune, and evaluate image generation models (diffusion, GAN, transformer-based). Implement and adapt techniques from research papers into working production systems. Design and run experiments to improve image quality, diversity, and controllability. Curate, clean, and manage large-scale image-text training datasets.

Evaluation, Hillclimbing & Quality Systems: Build and maintain evaluation frameworks for correctness, safety, grounding, and UX quality. Run hillclimbing loops across prompts, models, and tool-use strategies to continuously improve assistant performance. Analyze failure modes, design mitigations, and drive systematic improvements across the stack.

LLM Tooling & Internal Infrastructure: Develop internal tools for prompt experimentation, model comparison telemetry and debugging automated eval pipelines. Create reusable frameworks that accelerate the entire AI org’s ability to ship high-quality assistant features.

Applied ML & Product Integration: Integrate LLMs with product surfaces, APIs, and backend systems. Build lightweight ML components (ranking, classification, summarization, personalization) that enhance assistant intelligence. Collaborate with PM, design, and research to turn ambiguous ideas into polished user experiences.

High-Velocity Teamwork: Operate with startup-founder energy: bias for action, rapid iteration, and comfort with ambiguity. Work closely with researchers, engineers, and product leaders in a fast-moving AI team where ideas ship quickly and impact is immediate. Contribute to a culture of experimentation, clarity, and high-quality execution. Build prompt architectures, system instructions, and orchestration logic that ensure reliability, grounding, and personality consistency. Prototype new capabilities rapidly and iterate based on user signals and evaluation data.

Production & Infrastructure: Optimise models for inference latency, throughput, and cost (quantization, distillation, batching). Build and maintain serving pipelines for real-time and batch image generation. Develop APIs and SDKs that expose image generation capabilities to downstream teams/products. Monitor model performance in production; debug quality regressions.

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