The Machine Learning Engineer will develop, deliver, and operate AI-driven components for vehicle claims using computer vision and scalable ML pipelines on GCP, deploying services and ensuring performance optimization.
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.
#LI-MG1
Top Skills
BigQuery
Cloud Run
Dataflow
Docker
Fastapi
GCP
Gke
Grafana
Kubernetes
Python
PyTorch
Streamlit
TensorFlow
Vertex Ai
Similar Jobs
Healthtech • Internet of Things • Hospitality • Telehealth
The role requires a skilled Machine Learning Engineer to create complex models and AI solutions while collaborating with teams to enhance products and services.
Top Skills:
AIMachine Learning
Information Technology • Security • Software • Cybersecurity • App development • Data Privacy
The Director of Data & Analytics will lead a multidisciplinary team, unify the analytics strategy, and oversee data governance and platform evolution to enhance decision-making and business impact at MacPaw.
Top Skills:
AIBi ToolsCRMData AnalyticsData EngineeringData WarehousesMartechMlModern Data Stacks
Cloud • Security • Software • Cybersecurity • Automation
As an SRE, you'll automate environments, debug production issues, contribute to CI/CD workflows, and enhance observability while collaborating across teams.
Top Skills:
AIAnsibleDevsecopsElkGitlabGoGrafanaKubernetesPrometheusRubyTerraform
What you need to know about the Boston Tech Scene
Boston is a powerhouse for technology innovation thanks to world-class research universities like MIT and Harvard and a robust pipeline of venture capital investment. Host to the first telephone call and one of the first general-purpose computers ever put into use, Boston is now a hub for biotechnology, robotics and artificial intelligence — though it’s also home to several B2B software giants. So it’s no surprise that the city consistently ranks among the greatest startup ecosystems in the world.
Key Facts About Boston Tech
- Number of Tech Workers: 269,000; 9.4% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Thermo Fisher Scientific, Toast, Klaviyo, HubSpot, DraftKings
- Key Industries: Artificial intelligence, biotechnology, robotics, software, aerospace
- Funding Landscape: $15.7 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Summit Partners, Volition Capital, Bain Capital Ventures, MassVentures, Highland Capital Partners
- Research Centers and Universities: MIT, Harvard University, Boston College, Tufts University, Boston University, Northeastern University, Smithsonian Astrophysical Observatory, National Bureau of Economic Research, Broad Institute, Lowell Center for Space Science & Technology, National Emerging Infectious Diseases Laboratories
