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AI Engineer - Foundational Vision & Language… at Shifters AI · Yavne
Skills
Job Description
Mid–Senior | Robotics | Applied Research & Deployment
We are hiring a Mid–Senior AI Engineer to join our AI team and work on large language, vision-language, and vision-language-action models for robotic autonomy. This role will focus on improving the robot’s reasoning, planning, and autonomous decision-making capabilities using modern foundational AI models.
You will collaborate closely with the AI, robotics, and embedded software teams to build intelligent robotic capabilities end to end — from model development and evaluation to integration and deployment on real robots with onboard compute.
What You’ll Do
Design, develop, and deploy LLM-, VLM-, and VLA-based systems for robotic reasoning, planning, perception, and action.
Build agentic and multi-agent systems for robotic decision-making and task execution.
Develop human–robot interaction capabilities using language, vision, and multimodal inputs.
Fine-tune, adapt, evaluate, and deploy foundation models for real-world robotic use cases.
Integrate AI models with perception, navigation, control, and ROS-based robotic systems.
Work on model deployment for fully onboard and hybrid edge/cloud compute scenarios.
Build evaluation pipelines for robot autonomy, model behavior, task success, reliability, and safety.
Collaborate with embedded software and robotics engineers to connect high-level AI systems with real-time robot execution.
Translate research ideas into practical, robust systems that operate on physical robots.
Strong background in Python, machine learning, deep learning, and applied AI development.
Hands-on experience working with LLMs, VLMs, multimodal models, or foundation model pipelines.
Experience with model fine-tuning, prompting, evaluation, inference optimization, or deployment.
Familiarity with agent frameworks, tool-using models, planning systems, or similar architectures.
Strong understanding of modern AI model development workflows, including data preparation, training, evaluation, and deployment.
Ability to work across software boundaries, from research code to production-quality robotic systems.
Strong systems thinking and the ability to reason about end-to-end autonomy pipelines.
Experience debugging complex AI systems in real-world or simulated environments.