S
AI Summary
Design, train, and deploy AI and machine learning models for vehicle claims and ownership. Develop scalable data and ML pipelines on GCP. Collaborate with a distributed team to drive clarity and improve ML/engineering workflows.
Key Highlights
Design and deploy AI and machine learning models
Develop scalable data and ML pipelines on GCP
Collaborate with a distributed team
Technical Skills Required
Benefits & Perks
Production mindset: reliability, observability, maintainability, and measurable impact
Freedom to choose the best tool for the job; high autonomy and ownership
Job Description
Machine Learning Engineer – AI Core
Mission
Leverage AI and Solera’s data assets to develop, deliver, operate, and maintain innovative, production-grade components that make vehicle claims and ownership simpler, faster, and more more efficient for customers and users.
What You Will Do
- Design, train, and ship computer vision models for vehicle damage detection (classification, detection, segmentation), as well as tree-based models and LLM-powered components.
- Build scalable data and ML pipelines on GCP (BigQuery, Dataflow, Vertex AI) for training, evaluation, and inference at scale across hundreds of millions of images and claims.
- Deploy and operate services on GKE/Cloud Run with Docker and Kubernetes, following CI/CD with robust build systems and testing.
- Expose models via FastAPI; build internal tools and demos with Streamlit; instrument monitoring and alerting with Grafana.
- Own the end-to-end lifecycle: problem framing, data curation, experimentation, model/productization, performance/cost optimization, and post-deployment monitoring.
- Contribute to a high-quality monorepo: code reviews, standards, documentation, testing, and reproducibility.
- Collaborate in an internationally distributed team, driving clarity, sharing best practices, and improving ML/engineering workflows.
Monorepo with strong build, CI/CD, and code quality practices.
Freedom to choose the best tool for the job; high autonomy and ownership.
Production mindset: reliability, observability, maintainability, and measurable impact.
Tech stack
Python; TensorFlow, PyTorch
GCP: BigQuery, Dataflow, Vertex AI, GKE, Cloud Run, Cloud Deploy
Docker, Kubernetes
FastAPI, Streamlit
Grafana
What You Bring
- Strong Python and software engineering fundamentals (testing, code quality, CI/CD, performance).
- Proven experience training and deploying CV models (classification, detection, segmentation) with TensorFlow/PyTorch.
- Proficiency with large-scale datasets and distributed processing on GCP (BigQuery, Dataflow) or similar.
- Production MLOps experience on Kubernetes/containers.
- Ability to design clean APIs and services (FastAPI) and build usable internal tools (Streamlit).
- Experience with tree-based models.
- Experience with integrating LLM APIs into production workflows.
- Structured problem solving, critical thinking, and a driven, ownership-oriented mindset.
- Effective communication and collaboration in a distributed, cross-functional environment.
- Vertex AI pipelines.
- GPU optimization and cost/performance tuning for training/inference.
- Experience in insurance, automotive, or related computer vision domains.
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