Demo

Senior Machine Learning Engineer for Autonomous Systems

Paly Ventures
San Francisco, CA Full Time
POSTED ON 3/3/2025
AVAILABLE BEFORE 5/28/2025

Company Description

Join our innovative venture-backed startup as we revolutionize autonomous driving with AI-Defined Vehicles—taking a significant leap beyond traditional software-defined vehicles. Our goal is to create the most intelligent autonomous vehicle that anticipates your needs before you even recognize them.

We are actively seeking a Senior Machine Learning Engineer to develop and enhance cutting-edge solutions in autonomy. If you are driven by a passion for advancing machine learning, LLM / LVMs, and autonomous technologies, we want to hear from you!

Role Description

  • Design, fine-tune, and optimize deep learning systems tailored for autonomy, perception, and decision-making.
  • Research and implement multi-modal AI systems, integrating vision, language, and reinforcement learning methodologies.
  • Refine self-supervised and semi-supervised learning techniques for training models on extensive driving datasets.
  • Collaborate with software, simulation, and cloud engineering teams to seamlessly deploy ML models into advanced autonomy systems.
  • Create and maintain scalable data pipelines for processing extensive sensor fusion data (LiDAR, radar, cameras).
  • Optimize model inference for real-time applications on both embedded and cloud platforms.
  • Conduct rigorous model evaluations, performance tuning, and failure analysis to enhance robustness and generalization capabilities.

Qualifications

  • Minimum of 3 years of industry experience in machine learning, deep learning, or AI engineering post-graduation.
  • Strong expertise in LLM / LVM model architectures, training methodologies, and fine-tuning processes.
  • Solid background in autonomous systems, reinforcement learning, or robotics.
  • Hands-on experience with computer vision tasks, including object detection, segmentation, and sensor fusion.
  • Proficiency in Python along with TensorFlow, PyTorch, and other deep learning frameworks.
  • Experience using AWS services (S3, EC2, SageMaker, Lambda, etc.) for machine learning training and deployment.
  • Understanding of data engineering practices essential for large-scale ML pipelines.
  • Strong algorithmic and problem-solving skills with a focus on optimizing models for various environments.
  • Experience working with autonomous driving technology stacks.
  • Familiarity with distributed training, federated learning, and on-device AI optimization strategies.
  • Exposure to self-supervised learning, generative AI, and multi-modal architectures.
  • Knowledge of simulation environments to validate AI models.
  • Familiarity with automotive systems and functional safety requirements is a plus.
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