AI Jobs in United States

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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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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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Contributors may design original computational biology problems simulating real biology research workflows, create problems requiring Python programming (using Numpy, SciPy, BioPython) that are computationally intensive and cannot be solved manually within reasonable timeframes, develop problems requiring non-trivial reasoning chains in bioinformatics, systems biology, and molecular modeling, base problems on real research challenges or practical applications from biology practice, verify solutions using Python with standard computational biology libraries, and document problem statements clearly with verified correct answers.

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Contributors design original computational biology problems simulating real biology research workflows, create Python programming problems using libraries such as Numpy, SciPy, and BioPython, ensure problems are computationally intensive and require extended resolution times, develop problems needing non-trivial reasoning in bioinformatics, systems biology, and molecular modeling, base problems on real research challenges or biology practice applications, verify solutions with Python using standard computational biology libraries, and document problem statements clearly along with verified correct answers.

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Contributors design original computational biology problems that simulate real biology research workflows; create problems requiring Python programming to solve using libraries such as Numpy, SciPy, and BioPython; ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes; develop problems requiring non-trivial reasoning chains in bioinformatics, systems biology, and molecular modeling; base problems on real research challenges or practical applications from biology practice; verify solutions using Python with standard computational biology libraries; and document problem statements clearly with verified correct answers.