AI Engineer

Overview

Work across the full AI engineering stack by training and evaluating models, deploying them in real-world systems, and building the surrounding data, training, evaluation, and production infrastructure. Partner with platform, product, and firmware teams to ship and own work end to end across model, edge, and cloud boundaries.

What you'll do

• Research and develop machine learning models for perception and understanding problems across visual, audio, and other real-world signals.
• Explore how specialized models and larger general-purpose models can work together in production systems.
• Design data, training, and evaluation approaches that hold up under real-world conditions.
• 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.

What you'll need

• Understand deep-learning fundamentals, including architectures, training dynamics, evaluation design, and data quality and annotation.
• Think in systems, including latency and memory budgets, failure modes, distributed pipelines, and on-device constraints.
• Take end-to-end ownership across the model, edge, and cloud boundaries.
• Thrive in fast-paced environments and rapidly iterate from experimentation to production.
• Have hands-on experience using agentic development tools and AI-assisted coding as a core part of how you build and ship.
• Be proficient in Python and PyTorch.
• Have working ability in at least one systems language, such as C++ or Go.

Nice to 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, including SNPE, TensorRT, TFLite, ONNX Runtime, or similar.
• Experience with video understanding, VLM/embedding models, or large-scale retrieval.
• Experience working with large-scale video or telemetry data, including annotation pipelines and observability tooling, such as Snowflake and Redash.
• Experience with distributed training or experimentation frameworks, such as Ray/Anyscale, and building rigorous evaluation harnesses.

Details

• Location: Bangalore, India.
• The applicant must be authorized to receive and access commodities and technologies controlled under U.S. Export Administration Regulations.
• Employees must be authorized to receive access to Motive products and technology.