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Staff Software Engineer at Waymo designing and leading evaluation frameworks for autonomous driving simulation, blending C++/Python systems with AI-driven virtual environments to validate realism.
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo…
Leads a team developing machine learning models to evaluate and improve autonomous vehicle behavior using deep learning, Gen AI, and large-scale simulation systems.
Senior ML Engineer builds and deploys computer vision and vision-language models to generate high-fidelity labels for autonomous driving, using deep learning, generative AI, and reinforcement learning at scale.
Develops and improves machine learning models to generate high-fidelity simulation data for autonomous driving, focusing on realistic agent behavior and sensor accuracy.
Develops and maintains petabyte-scale data systems and ML pipelines for autonomous driving foundation models using frameworks like JAX and Flume/Beam.
A senior machine learning engineer at Waymo optimizes generative models and simulations for autonomous driving, focusing on performance, efficiency, and scalability using advanced ML techniques and hardware accelerators.
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 operate petabyte-scale ML pipelines and infrastructure to train, benchmark, and deploy foundation models for Waymo’s autonomous driving systems using frameworks like JAX and Flume.
Build and deploy multimodal LLMs and world models for 3D perception in autonomous vehicles using camera, LiDAR, and radar data.
Develops and deploys machine-learning recipes for autonomous-driving agents, working with large-scale data, evaluation pipelines, and foundation models to improve safety and realism.
Develops and optimizes firmware, drivers, and compute architectures for Waymo’s high-performance autonomous vehicle hardware, focusing on AI workloads and custom silicon.
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.
Principal ML Engineer at HubSpot builds AI systems that extract context from CRM data to power customer-facing features using deep learning, retrieval, and NLP.
This role develops AI/ML models for satellite intelligence processing using Python, PyTorch, and TensorFlow. The engineer will work onsite in Beavercreek, OH on OPIR data exploitation for defense applications.
Remote full-stack developer at Sauti who owns end-to-end feature delivery — design, coding, documentation, and deployment — while managing the company's GitHub org and CI/CD pipelines. Works across React/Angular/Vue front ends and Node.js/Python/Java/.NET back ends, with cloud (AWS/Azure/GCP) deployments and hands-on client engagement.
Trainee machine learning engineer at QBrainX in Coimbatore, India, working with data scientists and senior ML engineers to build, train, and deploy models that solve real-world problems. Core stack: Python with scikit-learn, pandas, NumPy, TensorFlow or PyTorch, plus Git.
Syngenta's R&D Digital data science team is hiring a Machine Learning Engineer to build and deploy production-grade computer vision and ML solutions that turn drone, satellite, and sensor imagery into digital traits for seed breeding. Day-to-day work spans the full ML lifecycle — model development, cloud data pipelines, MLOps, and monitoring — using Python, PyTorch/TensorFlow, Docker, and AWS/GCP/
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