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Manager, Applied Science (Telematics & OCR)
Motive
Lead a team to develop and deploy large-scale AI/ML models for fleet safety and operations.
2d ago
$138,000 - $200,000
Expert & Leadership (13+ years), Experienced (8-12 years)
Full Time
San Francisco, CA
Hybrid
Company Size
4,629 Employees
Service Specialisms
Fleet Management
GPS Tracking
Driver Safety
Compliance
Maintenance
Spend Management
Sector Specialisms
Renewables
Energy
Utilities
Marine
Decommissioning
Oil & Gas
Infrastructure
Power
Role
What you would be doing
event detection
a/b testing
deep learning
model deployment
model optimization
team management
- Work with vision, telematics and sensor data (dashcam, GPS, IMU, accelerometers) to improve event detection models (e.g., collision detection, risky driving behavior)
- 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
- Develop, train, and optimize deep learning models for safety, compliance, and fleet operations, including LLMs, transformer models, and multimodal AI.
- Collaborate with engineering teams to deploy models into production, ensuring robustness, interpretability, and real-time performance.
- Fine-tune and distill large models (LLMs, Vision Transformers, etc.) to optimize latency and deployment efficiency on edge devices and cloud infrastructure.
- Design and implement ML pipelines for large-scale data processing, including feature engineering, model training, and real-time inference.
- Manage and scale a team of applied scientists and engineers
What you bring
python
sql
llms
deep learning
cloud
degree
- Previous experience running a technical team
- Ability to translate business problems into scientific solutions and communicate technical findings to stakeholders.
- Knowledge of transformer models, LLMs, and multimodal AI.
- Strong experience in SQL and handling large-scale datasets.
- Experience with ML model deployment on cloud platforms (AWS, GCP).
- Experience working with hardware, robotics, telematics, geospatial data, or sensor fusion.
- Bachelor’s or Master’s degree in a quantitative field (CS, AI, Math, Statistics, or related).
- Understanding of probability, statistics, and optimization techniques.
- 4+ years of experience in deep learning, machine learning, or applied AI.
- Proficiency in Python (TensorFlow, PyTorch, NumPy, Pandas).
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Training + DevelopmentInformation not given or found
Interview process
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Company
Overview
Founded in 2013
Year Established
The company began as KeepTruckin in 2013 with a focus on digitizing driver hours and logs for truckers.
Rebranded 2022
New Identity Year
The company rebranded to Motive in 2022 to better reflect its expanded mission of powering the physical economy.
$150M Raised in 2022
Series F Funding
The company secured $150 million in Series F funding in 2022, contributing to its valuation.
120,000+ Businesses
Global Reach
The platform supports over 120,000 businesses worldwide, streamlining operations, safety, and finance teams.
- 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.
- Own It
- Less but Better
- Build Trust
- Unlock Potential
- Make it happen
- Make it simple
- Make it great
- Make it different
- Make it together
5M+ Electric Miles
Electric Vehicle Impact
Electrification initiatives have displaced over 5 million electric miles, significantly reducing environmental impact.
13% Fuel Savings
Fuel Efficiency Enhancements
Implementing advanced fleet management has achieved up to 13% reduction in fuel consumption.
20% Idling Cut
AI-Driven Fuel Insights
AI technology has successfully reduced unnecessary idling, saving fleets up to 10% in fuel costs.
15M+ COâ‚‚ Reduced
Carbon Emissions Impact
Efforts have resulted in the displacement of 15 million pounds of COâ‚‚ and reduced particulate matter effectively.
- Carbon Neutrality target achieved for Scope 1 & 2 emissions
- Upgraded fuel and carbon emissions reporting with low‑carbon integration
- Net‑zero target: achieving carbon neutrality on operational emissions (Scope 1 & 2)
4% increase
Women in Leadership
Year-over-year growth in women occupying leadership roles, reflecting progress toward gender equality in executive positions.
- Implementing industry-leading employee dialogue processes for an inclusive workplace.
- Published diversity metrics in annual Impact Reports.
- Commitment to building a diverse, inclusive team driving innovation.
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