Manager, Applied Science (Safety)

Overview

Lead applied science team building large-scale ML models for safety and fleet management

Key Responsibilities

• model development
• ml pipelines
• event detection
• research innovation
• a/b testing
• model deployment

Tasks

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

Requirements

• python
• tensorflow
• phd
• aws
• sql
• deep learning

What You Bring

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

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About Motive

-Launched with a simple goal: digitize driver hours and logs for truckers. -Evolved into an integrated platform combining IoT devices and AI-powered apps for managing vehicles, safety, compliance, assets, maintenance, and expenses. -Typical projects include rolling out fleet-wide dashcam systems with real-time safety alerts and automating compliance workflows for construction or oil-and-gas fleets. -Specializes in fleet management for sectors like trucking, logistics, construction, field service, agriculture, transit, and delivery. -A standout achievement: its Motive Card achieved a $1 billion annualized spend run rate, showcasing strong traction in spend management.

Sector Specialisms