Machine Learning Engineer - AI Core

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.

How we work

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.

Nice to have

• Vertex AI pipelines.
• GPU optimization and cost/performance tuning for training/inference.
• Experience in insurance, automotive, or related computer vision domains.