עדיין מחפשים עבודה במנועי חיפוש? הגיע הזמן להשתדרג!
במקום לעבור לבד על אלפי מודעות, Jobify מנתחת את קורות החיים שלך ומציגה לך רק משרות שבאמת מתאימות לך.
מעל 80,000 משרות • 4,000 חדשות ביום
חינם. בלי פרסומות. בלי אותיות קטנות.
מלאה
25,000-40,000 ₪
We are seeking a hands-on Applied AI Scientist to join our core R&D team and drive the development of next-generation AI systems for autonomous driving. This role sits at the intersection of applied research and deployment - you will go from reading papers to shipping production systems. You will work directly on our multi-layered autonomy architecture, with a primary focus on real-time predictive models for driving decisions.
A deep technical role for someone who thrives on turning cutting-edge research into real, working systems under hard constraints.
Responsibilities:
Own the research-to-deployment cycle for predictive driving models - from literature review and prototyping through to production integration
Design, implement, and iterate on real-time predictive models, including vision-language models, motion prediction models, and inverse reinforcement learning approaches (e.g., imitation learning, reward recovery)
Collaborate on higher-level reasoning systems, contributing to vision-language-action models that handle complex edge cases and long-horizon planning
Bridge cloud-scale training with edge deployment - work on model compression, quantization, speculative decoding, and efficient inference for embedded automotive platforms
Evaluate and integrate state-of-the-art techniques from the broader AI research community into our autonomy stack
Collaborate closely with internal R&D teams to unblock technical challenges, accelerate delivery, and raise the overall technical bar.
A deep technical role for someone who thrives on turning cutting-edge research into real, working systems under hard constraints.
Responsibilities:
Own the research-to-deployment cycle for predictive driving models - from literature review and prototyping through to production integration
Design, implement, and iterate on real-time predictive models, including vision-language models, motion prediction models, and inverse reinforcement learning approaches (e.g., imitation learning, reward recovery)
Collaborate on higher-level reasoning systems, contributing to vision-language-action models that handle complex edge cases and long-horizon planning
Bridge cloud-scale training with edge deployment - work on model compression, quantization, speculative decoding, and efficient inference for embedded automotive platforms
Evaluate and integrate state-of-the-art techniques from the broader AI research community into our autonomy stack
Collaborate closely with internal R&D teams to unblock technical challenges, accelerate delivery, and raise the overall technical bar.
Requirements:
Ph.D. in Computer Science, Electrical Engineering, Machine Learning, Robotics, or a related field
Strong publication or deployment track record in one or more of: deep learning, computer vision, reinforcement learning, imitation learning, vision-language models, or motion prediction
Demonstrated ability to go from paper to working implementation - not just theory, but shipped systems
Strong coding skills in Python; experience with C++ is a plus
Familiarity with modern ML infrastructure: PyTorch, distributed training, model optimization
Solid mathematical foundations in probability, optimization, and statistics
Attributes:
Experience with CUDA or low-level GPU optimization
Hands-on work with model quantization, distillation, or efficient inference on edge devices
Background in real-time, safety-critical, or embodied AI systems (robotics, autonomous vehicles, drones, etc.)
Experience with small language models (SLMs) or on-device deployment of foundation models
Familiarity with driving datasets, simulation environments, or sensor fusion pipelines.
Ph.D. in Computer Science, Electrical Engineering, Machine Learning, Robotics, or a related field
Strong publication or deployment track record in one or more of: deep learning, computer vision, reinforcement learning, imitation learning, vision-language models, or motion prediction
Demonstrated ability to go from paper to working implementation - not just theory, but shipped systems
Strong coding skills in Python; experience with C++ is a plus
Familiarity with modern ML infrastructure: PyTorch, distributed training, model optimization
Solid mathematical foundations in probability, optimization, and statistics
Attributes:
Experience with CUDA or low-level GPU optimization
Hands-on work with model quantization, distillation, or efficient inference on edge devices
Background in real-time, safety-critical, or embodied AI systems (robotics, autonomous vehicles, drones, etc.)
Experience with small language models (SLMs) or on-device deployment of foundation models
Familiarity with driving datasets, simulation environments, or sensor fusion pipelines.
This position is open to all candidates.
במקום לעבור לבד על אלפי מודעות, Jobify מנתחת את קורות החיים שלך ומציגה לך רק משרות שבאמת מתאימות לך.
מעל 80,000 משרות • 4,000 חדשות ביום
חינם. בלי פרסומות. בלי אותיות קטנות.
פתח תקווה
