Applied Scientist II

About the Role

We are looking for an Applied Scientist to help develop and deploy cutting-edge machine learning and deep learning models that power Motive's safety and fleet management solutions. You will work on LLMs, forecasting, and multimodal deep learning models, driving innovation in areas like collision detection, driver safety scoring, spend management, and fleet optimization. This role sits at the intersection of science and engineering, requiring you to push the boundaries of AI while ensuring models are robust, scalable, and production-ready.

As a key contributor to our Applied Science team, you'll work with massive datasets (petabyte-scale), including geospatial, telematics, and sensor data, to build models that enhance decision-making across thousands of fleets. You'll collaborate with engineers, product managers, and domain experts to develop novel ML algorithms, optimize inference performance, and deploy models in real-world applications that impact millions of drivers and businesses.

Responsibilities

• 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)
• 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.
• Conduct A/B testing and causal inference studies to evaluate the impact of AI-driven decisions.
• Stay up to date with the latest research in deep learning, generative AI, and optimization methods, bringing innovations into production.

Basic Qualifications

• Bachelor's or Master's degree in a quantitative field (CS, AI, Math, Statistics, or related).
• 4+ years of experience in deep learning, machine learning, or applied AI.
• Proficiency in Python (TensorFlow, PyTorch, NumPy, Pandas).
• Strong experience in SQL and handling large-scale datasets.
• Knowledge of transformer models, LLMs, and multimodal AI.
• Experience with ML model deployment on cloud platforms (AWS, GCP, Azure).
• Understanding of probability, statistics, and optimization techniques.
• Ability to translate business problems into scientific solutions and communicate technical findings to stakeholders.

Preferred Qualifications (Nice-to-Have)

• PhD in ML, AI, or related field.
• Experience working with telematics, geospatial data, or sensor fusion.
• Experience with PySpark, Ray, or distributed computing.
• Expertise in ML Ops (Docker, Kubernetes, TensorRT, ONNX).
• Research experience in reinforcement learning, generative models, or self-supervised learning.