Machine Learning Engineer – AI Core
Employment Information
Job Description
Machine Learning Engineer leveraging AI and data to build scalable ML solutions for vehicle claims at Solera. Collaborating with an international team to improve workflows and deliver high-quality components.
Responsibilities:
- Leverage AI and Solera’s data assets to develop, deliver, operate, and maintain innovative, production-grade components
- Design, train, and ship computer vision models for vehicle damage detection
- Build scalable data and ML pipelines on GCP for training, evaluation, and inference at scale
- Deploy and operate services on GKE/Cloud Run with Docker and Kubernetes, following CI/CD
- 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
Requirements:
- 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
Benefits:
- Freedom to choose the best tool for the job
- High autonomy and ownership
- Production mindset: reliability, observability, maintainability, and measurable impact
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