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About the Team The Fulfillment Planning team builds the intelligence that powers DoorDash’s logistics network. We optimize how deliveries are planned and executed across the full delivery lifecycle, improving customer…
Job Description *** This role is hybrid in our Chicago headquarters and requires the ability to travel to manufacturing sites across North America *** Here at Kraft Heinz, we grow our people to grow our business,…
Staff Software Engineer, Machine Learning Platform via Greenhouse Location Toronto Employment Type Full Time Location Type Onsite Department 8212 ML Foundations Compensation Not disclosed About the role Who we are…
Senior/Staff ML Engineer at Waymo building ultra-realistic 3D/4D world models and generative systems for autonomous vehicle simulation using advanced ML techniques like diffusion models and VLMs.
Lead the design and development of machine learning systems to evaluate and improve Waymo's autonomous driving technology, focusing on reinforcement learning, generative models, and large-scale simulation workflows.
Builds and scales machine learning infrastructure for autonomous vehicle perception systems, focusing on data flywheels, model training, and performance evaluation using large-scale real-world driving data.
Leads development of deep learning and generative AI models to evaluate and improve Waymo’s autonomous driving systems, using large-scale data and ML frameworks like TensorFlow and PyTorch.
Build ultra-realistic autonomous-vehicle simulations using large foundation models to train and evaluate Waymo’s self-driving AI in London, collaborating with teams in Mountain View and Oxford.
Leads the design and implementation of evaluation systems for large vision and language models used in autonomous driving, building benchmarks to assess model quality, safety, and realism.
Leads the design and scaling of AI/ML infrastructure for billion-parameter foundation models used in ultra-realistic autonomous-driving simulations, collaborating with research teams to improve simulation fidelity.
Leads a team developing machine learning models to evaluate and improve autonomous vehicle behavior using deep learning, Gen AI, and large-scale simulation systems.
Designs and optimizes large vision-language and language models for autonomous driving, focusing on hardware-aligned architectures and on-device performance in a safety-critical environment.
Develops and deploys machine learning models to evaluate autonomous vehicle behavior in simulation and real-world settings using Python/C++ and deep learning.
Builds and evaluates machine learning models for autonomous vehicle simulations, focusing on realism metrics and integrating foundation models into Waymo's evaluation systems.
Lead the design and deployment of auto-labeling systems and computer vision models to scale data pipelines for autonomous driving, using C++, Python, PyTorch, and TensorFlow.
Build and deploy multimodal LLMs and world models for 3D perception in autonomous vehicles using camera, LiDAR, and radar data.
Develops AI foundation models for autonomous driving, integrating large-scale systems with production platforms and adapting models to new sensors/platforms while collaborating across Alphabet teams.
Staff Tech Lead ML Engineer on Waymo's Perception team, designing multi-sensor model architectures for autonomous vehicle scene understanding and optimizing models for onboard compute using Python, C++, and frameworks like PyTorch/JAX/TensorFlow.
Waymo is hiring a Sr Staff Tech Lead / ML Engineer for its Perception team to set the technical roadmap for next-generation multi-modal perception of the Waymo Driver - architecting scalable sensor-fusion models, optimizing them for onboard hardware, leading cross-functional initiatives, and mentoring engineers. Core stack: Python, C++, and modern ML frameworks like PyTorch, JAX, and TensorFlow.
As an Applied AI/ML Scientist tech lead at Faire, you'll own strategy and execution for a two-sided marketplace, developing personalized recommendation, LTV prediction, and causal inference models using machine learning, deep learning, and LLMs, while mentoring scientists and solving marketplace growth/quality challenges.
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