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.
Similar Jobs
Explore other opportunities that match your interests
Visa Sponsorship
Relocation
Remote
Job Type
Contract
Experience Level
Associate
Multiverse Computing
Spain
Data, Analytics & AI Architect
••••••
••••••
••••••
Job Type
••••••
Experience Level
••••••
UST España & Latam
Spain
Visa Sponsorship
Relocation
Remote
Job Type
Full-time
Experience Level
Associate
Axpe Consulting
Spain