Python AI Jobs

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Lead and contribute to cross-functional efforts solving complex physical design challenges across IPs, projects, and advanced technology nodes. Develop and enhance RTL-to-GDS methodologies, including floorplanning, synthesis, placement and routing (P&R), static timing analysis (STA), signoff, and assembly. Architect and deploy AI/ML-driven solutions in production flows to improve engineering efficiency, turnaround time, and quality of results (QoR). Optimize EDA tools and custom CAD flows using data-driven and ML-based techniques, working closely with verification, extraction, timing, design for test (DFT), and EDA vendors.

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Lead and contribute to cross-functional efforts solving complex physical design challenges across IPs, projects, and advanced technology nodes. Develop and enhance RTL-to-GDS methodologies, including floorplanning, synthesis, place and route, static timing analysis, signoff, and assembly. Architect and deploy AI/ML-driven solutions in production flows to improve engineering efficiency, turnaround time, and quality of results. Optimize EDA tools and custom CAD flows using data-driven and machine learning-based techniques in close collaboration with verification, extraction, timing, design for test, and EDA vendors.

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Lead and contribute to cross-functional efforts solving complex physical design challenges across IPs, projects, and advanced technology nodes. Develop and enhance RTL-to-GDS methodologies, including floorplanning, synthesis, P&R, STA, signoff, and assembly. Architect and deploy AI/ML-driven solutions in production flows to improve engineering efficiency, turnaround time, and QoR. Optimize EDA tools and custom CAD flows using data-driven and ML-based techniques, in close collaboration with verification, extraction, timing, DFT, and EDA vendors.

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Build and improve production RAG and LLM-based systems. Design and maintain data pipelines that integrate enterprise data sources at scale. Develop full-stack product features end-to-end, from Python backend through React/TypeScript frontend. Work on optimization and scheduling problems with real operational constraints. Build evaluation frameworks that measure whether AI systems are actually improving.

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Applications Engineers are responsible for deploying Shield AI's Hivemind software in real-world environments, working closely with customers to understand their requirements, providing technical expertise and customer support during deployment, and ensuring successful integration of Hivemind. They collaborate internally with engineering teams to develop and test new autonomy capabilities. The role involves frequent travel, often international, to work alongside customers on-site. Responsibilities include supporting software integration and development activities on-site with customers, becoming an expert user of Hivemind enterprise software and its autonomy modules, providing technical support and training to customers, developing AI and Autonomy applications using the Shield AI software development kit, assisting sales teams in pre-sales activities, helping in post-sales deployment and integration of the software products, developing and maintaining technical documentation and training materials, debugging software/API integration issues with customers, and collaborating with the product engineering team to address customer feedback and improve products.

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The role involves building and operating the next generation of data infrastructure at Mistral AI, being a core contributor to the design and scaling of massive compute fleets and storage systems for high performance and scalability. Responsibilities include architecting and maintaining multi-cluster orchestration layers for optimizing workload placement across diverse hardware and regions, designing future-proof storage systems anticipating exabyte scale growth, contributing to the internal training platform development to support model training and fine-tuning across Kubernetes and SLURM environments, implementing and managing metadata and lineage systems to provide visibility and traceability of data and model pipelines, and managing cloud-native deployments using modern workflows to ensure scalability and operational excellence. The role also includes full lifecycle ownership, from migrating away from legacy orchestrators to implementing production-grade pipelines and participating in on-call rotations for critical training jobs.

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The role involves working on custom memory systems that grow and scale as users use the platform, developing a custom cutting-edge agent, managing bare metal infrastructure and scalability with concurrency and high reliability, optimizing cost and output with multiple models, and evaluating large language model (LLM) performance across a wide domain of tasks.

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Build the unified company data graph by integrating systems across execution (GitHub, Linear), communication (Slack, email, Zoom, calendars), model performance (W&B, eval dashboards), and operations (Rippling, Vanta, Ramp, Runway). Design and ship agents that surface performance signals, resource allocation suggestions, bottleneck detection, and opportunity visibility to leadership. Start with observability by creating a real-time map of work, ownership, and impact across the company. Progress from visibility to recommendations to partial automation, following the progressive autonomy principle and ensuring never to automate a decision that is not yet understood. Own the entire stack including data pipelines, APIs, agent orchestration, evals, and the interfaces leadership uses to interact with the system. Take responsibility for end-to-end development including designing data architecture, writing integrations, deploying infrastructure, and iterating on the agent layer with high autonomy and direct access to leadership for decision making.

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Design original computational physics problems that simulate real physics research workflows; create problems requiring Python programming to solve using libraries such as Numpy, SciPy, and Sympy; ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes; develop problems requiring non-trivial reasoning chains in mechanics, electromagnetism, thermodynamics, and quantum mechanics; base problems on real research challenges or practical applications from physics practice; verify solutions using Python with standard physics simulation libraries; document problem statements clearly and provide verified correct answers.

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Contributors may design original computational physics problems that simulate real physics research workflows, create problems requiring Python programming to solve using libraries such as Numpy, SciPy, and Sympy, and ensure these problems are computationally intensive and cannot be solved manually within reasonable timeframes. They develop problems involving non-trivial reasoning chains in mechanics, electromagnetism, thermodynamics, and quantum mechanics, based on real research challenges or practical physics applications. Contributors verify solutions using Python with standard physics simulation libraries and document problem statements clearly, providing verified correct answers.