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Build, train, and deploy ML models for finance using Python, Spark, and cloud tools; collaborate with data scientists to scale AI solutions on cloud platforms.
Build and deploy scalable ML models for finance using Python, Spark, and cloud tools like Databricks and Azure ML, collaborating with data scientists to turn business needs into production systems.
Build and refine large language models and AI agents for crypto trading, compliance, and Web3 infrastructure at Binance.
Build and optimize post-training systems for large language models, including data pipelines, reward models, and reinforcement learning to improve reasoning and instruction-following.
Designs and scales AI platform services for agent orchestration, retrieval, and model serving at Gusto, leading architecture, productionizing AI techniques, and mentoring engineers.
Principal engineer optimizing AI/ML compilers and runtime for AMD GPUs, focusing on MLIR/LLVM transformations and ONNX operators to accelerate training and inference workloads.
Build high-fidelity RL environments and training gyms for AI agents, partnering with top labs to improve agentic models through reinforcement learning and verifiable rewards.
Build and optimize data pipelines to process large financial datasets, then engineer ML features and models that power AI-driven banking solutions using Python, Spark, and NoSQL databases.
Build and optimize distributed AI training and inference pipelines for gaming using PyTorch, DeepSpeed, and Kubernetes to support large language models and reinforcement learning workloads.
Build and deploy AI models for contact-rich robot manipulation tasks like precision assembly, integrating vision, force sensing, and reinforcement learning into Intrinsic’s robot control stack.
Build and scale AI infrastructure for industrial automation, bridging research and production with MLOps and distributed systems.
Build and evaluate AI agent architectures that work reliably across different models, providers, and environments, focusing on portability, interoperability, and optimization.
Senior ML Engineer to research and build AI models for physical systems like robots, combining reinforcement learning, multimodal models, and real-world robotics in a collaborative research environment.
Build and optimize tiny, ultra-efficient AI models for on-device computer vision and multimodal LLMs, shipping features like People Detection and Video Search to millions of cameras.
Research and develop state-of-the-art AI models for embedded devices, focusing on computer vision and multimodal LLMs to enable features like people detection and video search.
Build and deploy large-scale AI/ML systems on Google Cloud, designing models and infrastructure for speech, reinforcement learning, or other ML domains to power Google’s products.
Design and deploy AI algorithms for robotics autonomy, integrating perception, planning, and control systems in Python/C++ and ROS/ROS2 for real-world deployments in construction, security, and mining.
Design and deploy AI algorithms for robot perception, planning, and control in unstructured environments using Python/C++ and ROS/ROS2.
Build and deploy ML models and LLM-based apps for beauty ecommerce, including recommendation engines and NLP, using PyTorch, FastAPI, and GCP VertexAI.
Build and deploy AI features like LLMs and predictive models, writing clean Python code and collaborating with cross-functional teams in a fast-paced startup.
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