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Senior full stack developer who designs, builds, and maintains scalable web applications using Python and ReactJS, deploys and manages them on AWS, and integrates machine learning models in collaboration with AI specialists. The role also covers code reviews, testing, troubleshooting, and documentation.
Backend developer on TransPerfect's AI team in Barcelona, building a Python pipeline that converts complex, unstructured PDFs into pixel-perfect editable .docx files. Day to day: benchmarking commercial and open-source OCR/document-AI tools (ABBYY, AWS Textract, LayoutLM-style models), and integrating LLMs like GPT/Claude for layout correction.
Future Processing, a Polish IT services company, is hiring a Senior Machine Learning Engineer to build and advise on ML/AI solutions for client projects, including NLP/LLM work, cloud AI services, and presales involvement. Core stack is Python with ML libraries like TensorFlow, PyTorch, and scikit-learn; B2B contract at 115–185 PLN/h.
Pipe Technologies is hiring a Senior Data Scientist (remote anywhere in the U.S.) to build and deploy ML and statistical models forecasting customer cash flows and credit risk, run A/B tests to improve product and underwriting, and advance models with deep learning techniques like transformers. Core stack includes PyTorch/TensorFlow, Spark, and AWS/GCP.
ML Systems Engineer at Final, an HFT/trading-algorithms firm in Ramat Hasharon, building and optimizing proprietary deep learning systems for live trading. Day-to-day involves running DL models on large GPU clusters and adapting them for production serving, using PyTorch, Python/C/C++, and custom CUDA/Triton kernels.
Develops and optimizes large-scale vision-language models for autonomous vehicle perception, training pipelines on multimodal sensor data to improve real-world driving decisions.
Develops and deploys machine learning models for autonomous vehicle prediction and planning, using deep learning frameworks to improve navigation safety and efficiency.
Develops ultra-realistic simulation environments for autonomous driving using machine learning, focusing on generative AI and foundation models to enhance the Waymo Driver's performance.
Designs and optimizes large-scale machine learning pipelines for vision-language models to improve autonomous vehicle perception, using Python and deep learning frameworks.
Senior engineer building and scaling AI/ML infrastructure for ultra-realistic autonomous-driving simulations using multi-billion-parameter foundation models and distributed training.
Build and optimize large-scale ML evaluation platforms for autonomous driving models, focusing on performance, scalability, and efficiency using frameworks like TensorFlow, JAX, and XLA.
Senior engineer building AI/ML infrastructure for autonomous-driving simulations, scaling multi-billion-parameter foundation models on large distributed systems.
Senior Software Engineer at Waymo optimizing distributed ML training pipelines for autonomous driving models using TensorFlow, JAX, and Grain.
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.
Develops and maintains Angular-based internal tools for Waymo’s autonomous vehicle fleet monitoring, using TypeScript and full-stack development practices.
Senior engineer building ML infrastructure for Waymo's ride-hailing marketplace—scaling the economic engine and automating ML model training, deployment, and inference for real-time pricing, matching, and routing decisions. Core tech includes Java/C++, Python, TensorFlow/PyTorch, and large-scale backend systems.
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.
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.
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