Python AI Jobs

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As the first US based Engineer, the role entails acting as the technical bridge between the product vision and customer reality. Responsibilities include designing, architecting, and shipping full-stack features that solve customer compliance challenges, owning the technical relationship with key customers by implementing solutions, gathering requirements, and translating feedback into product improvements. The engineer will build scalable services and APIs for the LLM compliance platform, make high-impact technical decisions quickly while being accountable to engineering standards and customers, challenge assumptions about what and how to build, and shape the product roadmap and engineering practices as the company scales from Series A to market leadership. The work combines coding, customer engagement, and steering product direction with high autonomy and collaboration with the founding team.

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The role involves joining Mistral's Solutions team with the mission to take the company's AI models and make them indispensable for enterprise clients. Responsibilities include owning a portfolio of enterprise clients and acting as their primary point of contact and co-builder, identifying high-impact use cases, structuring deployments, and removing obstacles. The role requires bridging the gap between the AI models' capabilities and client needs, addressing issues ranging from prompting to executive-level challenges. Additionally, the person feeds back signals from the field to product and research teams. Depending on background, one may focus either on deployment strategy (including business cases, C-level adoption, executive workshops, adoption roadmaps, and ROI proposals) or on applied AI engineering (including integration architecture, fine-tuning, production deployment, prototypes, deployment pipelines, client code, and open-source contributions).

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The Software Engineer on the Workspace & Observability Team at Anyscale is responsible for building user-facing application features for the Anyscale AI platform, focusing on the backend to implement the core business logic of these features. Responsibilities include interacting with users to understand their requirements, designing and implementing features, maintaining and improving these features over time, and working on observability tools that help users monitor and debug AI applications running on distributed clusters. Specific projects may involve developing the Ray Dashboard observability tool, library-specific observability tools like the Ray Train and Ray Serve dashboards, a unified log viewer for querying logs across a Ray cluster, and anomaly detection features to automatically identify and suggest fixes for performance bottlenecks or bugs. The role also involves collaborating with distributed systems and machine learning experts, communicating work through talks, tutorials, and blog posts, and contributing to building and shaping the company.

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Translate business requirements into requirements for AI/ML models; prepare data to train and evaluate AI/ML/DL models; build AI/ML/DL models by applying state-of-the-art algorithms, especially transformers; leverage existing algorithms from academic or industrial research when applicable; test, evaluate, and benchmark the AI/ML/DL models, and publish the models, data sets, and evaluations; deploy models in production by containerizing the models; work with customers and internal employees to refine the quality of the models; establish continuous learning pipelines for models with online learning or transfer learning; build and deploy containerized applications on cloud or on-premise environments.

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Design original computational statistics problems that simulate real mathematical 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 areas like number theory, combinatorics, graph theory, and numerical analysis; base problems on real research challenges or practical applications from mathematical practice; verify solutions using Python with standard mathematical libraries; document problem statements clearly and provide verified correct answers.

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Contributors design original computational statistics problems that simulate real mathematical research workflows, creating problems requiring Python programming to solve using libraries like Numpy, SciPy, and Sympy. They ensure problems are computationally intensive and require non-trivial reasoning chains in areas such as number theory, combinatorics, graph theory, and numerical analysis. The problems are based on real research challenges or practical mathematical applications. Contributors verify solutions using Python with standard mathematical libraries and document problem statements clearly with verified correct answers.

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Contributors design original computational statistics problems that simulate real mathematical research workflows and create problems requiring Python programming to solve using libraries such as Numpy, SciPy, and Sympy. They ensure problems are computationally intensive and not solvable manually within reasonable timeframes, develop problems requiring non-trivial reasoning chains in areas like number theory, combinatorics, graph theory, and numerical analysis, base problems on real research challenges or practical mathematical applications, verify solutions using Python with standard mathematical libraries, and document problem statements clearly providing verified correct answers.

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Contributors design original computational statistics problems simulating real mathematical research workflows, create problems requiring Python programming to solve using libraries like 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 number theory, combinatorics, graph theory, and numerical analysis, base problems on real research challenges or practical applications, verify solutions using Python with standard mathematical libraries, and document problem statements clearly alongside providing verified correct answers.

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Contributors may design original computational statistics problems that simulate real mathematical 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 in areas like number theory, combinatorics, graph theory, and numerical analysis; base problems on real research challenges or practical applications; verify solutions using Python with standard mathematical libraries; and document problem statements clearly while providing verified correct answers.

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Contributors design original computational statistics problems that simulate real mathematical 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 that require non-trivial reasoning chains in areas including number theory, combinatorics, graph theory, and numerical analysis, base problems on real research challenges or practical applications from mathematical practice, verify solutions using Python with standard mathematical libraries, and document problem statements clearly with verified correct answers.