Artificial Intelligence Engineer

Senior AI Engineer – Multimodal & Autonomous Systems

Location:

Santa Clara, CA

Employment Type:

Full-Time/Contract

Only Locals can apply

About the Role

We are seeking a highly experienced

Senior or Staff AI Engineer

to work on advanced AI systems powering autonomous robotic platforms. You will help develop intelligent models capable of understanding complex environments, making decisions, coordinating with other autonomous systems, and operating reliably in real-world scenarios.

This role is ideal for an engineer who enjoys working at the intersection of

foundation models, agentic AI, reinforcement learning, simulation, and robotics

and wants significant ownership in a fast-moving environment.

What You'll Do

• Develop and improve multimodal AI models for autonomous robotic applications.
• Experiment with model architectures, training approaches, datasets, and evaluation techniques.
• Build scalable pipelines for training and fine-tuning large AI models.
• Develop AI capabilities involving reasoning, memory, planning, communication, and tool usage.
• Design evaluation frameworks, benchmarks, and metrics to measure model performance.
• Create simulation and testing environments for reinforcement learning and synthetic data generation.
• Work with real-world and simulated datasets to improve model capabilities.
• Translate research concepts into reliable, production-ready AI systems.
• Collaborate with robotics, software, and systems engineers to integrate AI into autonomous platforms.
• Participate in testing and field deployments to evaluate system performance under real-world conditions.
• Help shape technical direction, engineering practices, and development processes as the team grows.

Required Qualifications

• 6+ years of professional experience developing and deploying AI/ML systems.
• 2+ years of experience working with

multimodal AI, foundation models, or agentic systems

.

• Strong programming skills in

Python

and hands-on experience with

PyTorch

.

• Experience training or fine-tuning large-scale deep learning models.
• Experience working with distributed training and modern ML infrastructure.
• Strong understanding of model evaluation, experimentation, and data preparation.
• Experience with modern AI training techniques such as

SFT, DPO, or reinforcement-learning-based approaches

.

• Ability to take an AI concept from experimentation through production deployment.
• Bachelor's, Master's, or PhD in Computer Science, Engineering, Mathematics, Physics, or a related technical discipline, or equivalent practical experience.

Preferred Experience

• Robotics or physical AI.
• Reinforcement learning.
• Multi-agent systems.
• Computer vision and multimodal models.
• Simulation environments and synthetic data generation.
• AI planning, reasoning, memory, or tool-use systems.
• Edge AI or real-time inference optimization.
• Experience deploying AI systems in resource-constrained environments.