Motive
Who we are
Motive empowers the people who run physical operations with tools to make their work safer, more productive, and more profitable. For the first time ever, safety, operations and finance teams can manage their drivers, vehicles, equipment, and fleet related spend in a single system. Combined with industry leading AI, the Motive platform gives you complete visibility and control, and significantly reduces manual workloads by automating and simplifying tasks.
Motive serves nearly 100,000 customers – from Fortune 500 enterprises to small businesses – across a wide range of industries, including transportation and logistics, construction, energy, field service, manufacturing, agriculture, food and beverage, retail, and the public sector.
Position Overview We are looking for a Manager, Applied Science 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 manager on the Applied Science team, you’ll mentor a team and also directly 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.
What You’ll Do
Manage and scale a team of applied scientists and engineers
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
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)
Fine‑tune and distill large models (LLMs, VLMs) to optimize performance and minimize latency 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 and bring these innovations into production
What We’re Looking For
Masters or Doctoral degree in a quantitative field (CS, AI, Math, Statistics, or related)
Previous experience running a technical team
5+ years of experience in deep learning, machine learning, or applied AI
Experience working with hardware, robotics, telematics, geospatial data, or sensor fusion.
Proficiency in Python (TensorFlow/PyTorch, Pandas, PySpark)
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)
Understanding of probability, statistics, and optimization techniques
Ability to translate business problems into scientific solutions and communicate technical findings to stakeholders
Pay Transparency Your compensation may be based on several factors, including education, work experience, and certifications. For certain roles, total compensation may include restricted stock units. Motive offers benefits including health, pharmacy, optical and dental care benefits, paid time off, sick time off, short term and long term disability coverage, life insurance as well as 401k contribution (all benefits are subject to eligibility requirements). The compensation range for this position is:
United States
$200,000 - $235,000 USD
EEO Statement Creating a diverse and inclusive workplace is one of Motive's core values. We are an equal opportunity employer and welcome people of different backgrounds, experiences, abilities and perspectives.
#J-18808-Ljbffr
Motive serves nearly 100,000 customers – from Fortune 500 enterprises to small businesses – across a wide range of industries, including transportation and logistics, construction, energy, field service, manufacturing, agriculture, food and beverage, retail, and the public sector.
Position Overview We are looking for a Manager, Applied Science 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 manager on the Applied Science team, you’ll mentor a team and also directly 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.
What You’ll Do
Manage and scale a team of applied scientists and engineers
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
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)
Fine‑tune and distill large models (LLMs, VLMs) to optimize performance and minimize latency 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 and bring these innovations into production
What We’re Looking For
Masters or Doctoral degree in a quantitative field (CS, AI, Math, Statistics, or related)
Previous experience running a technical team
5+ years of experience in deep learning, machine learning, or applied AI
Experience working with hardware, robotics, telematics, geospatial data, or sensor fusion.
Proficiency in Python (TensorFlow/PyTorch, Pandas, PySpark)
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)
Understanding of probability, statistics, and optimization techniques
Ability to translate business problems into scientific solutions and communicate technical findings to stakeholders
Pay Transparency Your compensation may be based on several factors, including education, work experience, and certifications. For certain roles, total compensation may include restricted stock units. Motive offers benefits including health, pharmacy, optical and dental care benefits, paid time off, sick time off, short term and long term disability coverage, life insurance as well as 401k contribution (all benefits are subject to eligibility requirements). The compensation range for this position is:
United States
$200,000 - $235,000 USD
EEO Statement Creating a diverse and inclusive workplace is one of Motive's core values. We are an equal opportunity employer and welcome people of different backgrounds, experiences, abilities and perspectives.
#J-18808-Ljbffr