AI Software Engineer (Vehicle Engineering)

• Build AI systems and applications that address engineering and business problems, working with users to understand requirements and define measurable success criteria.
• Develop evaluations (evals), benchmarks, and representative datasets to measure model and agent performance, compare approaches, and detect regressions.
• Build automated graders and scoring methods, validate their agreement with expert judgments, and investigate misleading results, data leakage, and other evaluation failures.
• Create reproducible environments for training and evaluating agents, including task definitions, tool interfaces, constraints, and feedback signals.
• Integrate models and agents with engineering tools, internal services, and company data, capturing execution traces and results to support debugging and evaluation.
• Establish baselines, run experiments, and analyze failures to determine whether changes to data, models, tools, or workflows produce meaningful improvements.
• Develop data preparation, training, and evaluation workflows for problems with limited real-world data, including targeted data collection, labeling, augmentation, and synthetic data where appropriate.
• Use Docker and Kubernetes to build and operate reliable workloads with appropriate isolation, access controls, resource management, logging, and reproducibility.
• Collaborate with engineering, infrastructure, security, HR, and legal partners to implement requirements for data handling, traceable outputs, auditability, and human review.
• Maintain deployed systems through testing, monitoring, ongoing evaluation, and operational debugging, improving quality, reliability, latency, and cost based on evidence and user feedback.
• Bachelor’s degree in computer science, data science, engineering, math, or physics; OR 4+ years of professional experience building and training AI/ML systems in lieu of a degree
• +1 years of Python programming development and debugging software on Linux.
• +1 years of experience with ML framework such as PyTorch, or practical experience developing agents and model-based applications.
• +1 years of experience building or evaluating AI/ML systems through professional work, internships, research, or substantial independent projects.
• Understanding of evaluation fundamentals, including appropriate metrics, baselines, separation of training and evaluation data, error analysis, and reproducible experimentation.
• Experience with Docker and familiarity with Kubernetes.
• Familiarity with version control, software testing, and collaborative development practices.
• Ability to explain technical decisions, work with people across disciplines, and learn unfamiliar tools and problem domains.
• Designing task-specific evals, evaluation datasets, automated graders, or frameworks for comparing model and agent performance.
• Building reinforcement learning environments, agent tools, reward functions, or verifiers.
• Working with model fine-tuning, reinforcement learning, or learning from human feedback.
• Developing effective ML systems with limited data, including data curation, augmentation, and synthetic data generation.
• Deploying and troubleshooting Kubernetes workloads and distributed job execution.
• Building systems for experiment tracking and versioning datasets, models, environments, and evaluations.

None flagged.