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Motive empowers physical operations teams—from Fortune 500 enterprises to small businesses across transportation, logistics, construction, energy, field service, manufacturing, and more—with a unified platform for managing drivers, vehicles, equipment, and fleet spend. The platform combines safety, operations, and finance capabilities with industry-leading AI to provide visibility, control, and automation.
Motive's AI team builds the models and systems that process camera feeds, sensor data, and millions of hours of fleet data to understand what's happening on the road and in the field. The work spans model development, on-device deployment, and cloud infrastructure that powers products for drivers and safety teams.
As an AI Engineer, you will own work end-to-end across the full stack: training and evaluating models, deploying them in real-world conditions, and building the systems around them. You'll partner closely with platform, product, and firmware teams to ship production systems. Your days might involve improving a model, building the pipeline that feeds it, or troubleshooting production issues.
Key responsibilities:
- Research and develop machine learning models for perception and understanding across visual, audio, and other real-world signals
- Explore how specialized models and larger general-purpose models work together in production
- Design data, training, and evaluation approaches that hold up under real-world conditions, not just in notebooks
- Study model behavior, robustness, and failure modes across data, deployment, and operating conditions
- Integrate and validate new capabilities in real-time or resource-constrained systems
- Work with firmware, platform, and product teams to turn research into working systems
Requirements:
- Deep understanding of deep-learning fundamentals: architectures, training dynamics, evaluation design, data quality and annotation
- Systems thinking: latency and memory budgets, failure modes, distributed pipelines, on-device constraints
- End-to-end ownership across model, edge, and cloud boundaries; comfort with ambiguity
- Ability to thrive in fast-paced environments and rapidly iterate from experimentation to production
- Hands-on experience with agentic development tools and AI-assisted coding as a core part of your workflow
- Proficiency in Python and PyTorch; working ability in at least one systems language (C++, Go, or similar)
Strong candidates may also have:
- Experience building LLM or agent-based applications including tool use, retrieval over domain data, and evaluating non-deterministic systems
- Experience with on-device or embedded ML (SNPE, TensorRT, TFLite, ONNX Runtime, or similar)
- Experience with video understanding, VLM/embedding models, or large-scale retrieval
- Experience with large-scale video or telemetry data, including annotation pipelines and observability tooling (Snowflake, Redash)
- Experience with distributed training or experimentation frameworks (Ray/Anyscale) and building rigorous evaluation harnesses
