Machine Learning Engineer

This is a remote position.

We are looking for a skilled ML Engineer with strong experience in computer vision and production machine learning systems.

You will work directly with the founders and help expand our AI capabilities across image analysis, structured data modeling, and LLM-powered workflows. Our ML infrastructure is already mature and running in production, so this role is focused on improving, scaling, and evolving existing systems while introducing new approaches and models.

This is a high-ownership role ideal for engineers who enjoy moving fast, solving difficult technical challenges, and taking ML systems from experimentation to production deployment.

Key Responsibilities

• Develop and improve computer vision models for vehicle crash analysis
• Build and maintain traditional ML models for structured and sensor data
• Work with LLM APIs such as OpenAI, Anthropic, and Gemini for structured outputs and evaluation workflows
• Deploy, package, and serve ML models in production environments
• Monitor model quality and performance metrics including regression evaluation (MAE, R²)
• Collaborate closely with the full-stack engineering team on ML integration
• Work with both image-based and structured datasets
• Ensure ML systems are reliable, scalable, and production-ready
• Participate in the full ML lifecycle from experimentation to deployment and monitoring

Requirements

Machine Learning & Data Science

• Strong Python skills with the scientific stack:
  • pandas
  • numpy
  • scipy
  • scikit-learn
• Hands-on experience with gradient boosting frameworks:
  • XGBoost
  • LightGBM
  • CatBoost
  • AutoGluon
• Experience taking ML models from research/notebooks into production systems
• Experience with model packaging, inference serving, and monitoring

Computer Vision & Deep Learning

• Strong experience with PyTorch
• Familiarity with CUDA and GPU-based training/inference
• Experience with computer vision and image analysis systems
• Familiarity with segmentation or foundation vision models such as SAM is a plus

LLM & AI API Integration

• Experience working with LLM APIs including:
  • OpenAI
  • Anthropic
  • Gemini
• Prompt engineering and structured outputs
• LLM evaluation, monitoring, and reliability workflows

Infrastructure & Systems

• Experience working with image and structured sensor datasets
• Familiarity with workflow orchestration tools such as Temporal
• Understanding of service-oriented architecture
• Experience with cloud infrastructure and containers:
  • GCP
  • Docker
  • Object Storage