Manager, Applied Science (Safety)

Description

• Fine-tune and distill large models (LLMs, VLMs) to optimize performance and minimize latency on edge devices and cloud infrastructure
• Stay up to date with the latest research in deep learning, generative AI, and optimization methods and bringing these innovations into production
• Develop, train, and optimize AI models for safety, compliance, and fleet operations, including classical ML models, LLMs, and multimodal systems
• Design and implement ML pipelines for petabyte-scale data processing, including feature engineering, model training, and real-time inference
• Manage and scale a team of applied scientists and engineers
• Conduct A/B testing and causal inference studies to evaluate the impact of AI-driven decisions
• Collaborate with engineering teams to deploy models into production, ensuring robustness, interpretability, and real-time performance
• Work with vision, telematics, and sensor data (e.g., dashcam, GPS, IMU, accelerometer) to improve event detection models (e.g., collision detection, risky driving behavior)

Requirements

• Strong experience in SQL and handling large-scale datasets
• 5+ years of experience in deep learning, machine learning, or applied AI
• Previous experience running a technical team
• Ability to translate business problems into scientific solutions and communicate technical findings to stakeholders
• Proficiency in Python (TensorFlow/PyTorch, Pandas, PySpark)
• Understanding of probability, statistics, and optimization techniques
• Experience working with hardware, robotics, telematics, geospatial data, or sensor fusion.
• Knowledge of transformer models, LLMs, and multimodal AI
• Experience with ML model deployment on cloud platforms (AWS, GCP)
• Masters or Doctoral degree in a quantitative field (CS, AI, Math, Statistics, or related)

Benefits

Training + Development

Interview process

Visa Sponsorship

Security clearance