Python AI Jobs

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As a Research Engineer in OpenAI's Monetization Group, you will design and deploy advanced machine learning models to solve real-world problems, bringing research from concept to implementation and creating AI-driven applications with direct impact. You will collaborate closely with researchers, software engineers, and product managers to understand complex business challenges and deliver AI-powered solutions. Your work includes implementing scalable data pipelines, optimizing models for performance and accuracy to ensure they are production-ready, and monitoring and maintaining deployed models to ensure they continue delivering value. You will stay ahead of developments in machine learning and AI by engaging with the latest research, participate in code reviews, share knowledge, and lead by example to maintain high-quality engineering practices.

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Partner with internal stakeholders to understand their daily workflows to surface and document those that can be automated with AI. Build autonomous and semi-autonomous agent workflows that interact with browsers, codebases, and APIs to complete complex content operations. Learn and contribute to prompt engineering strategies, with guidance on building agentic workflows, memory systems, and multi-step reasoning. Build and operate MCP Servers that can be called by AI Agents. Design and build proprietary agent libraries, prompting strategies and benchmarking frameworks. Build and operationalize AI solutions with retrieval techniques (e.g., RAG, vector DBs) for context-aware applications. Prototype, test, and optimize AI-powered applications, including retrieval-augmented generation, workflow automation, and agentic experiences. Collaborate with cross-functional teams to align AI features with user needs and business goals, gaining exposure to product and platform thinking. Stay current on advancements in AI technologies, frameworks, and best practices, and evangelize AI capabilities internally and externally. Provide technical support, documentation, and training to facilitate adoption and effective use of AI solutions. Participate in technical discussions, architecture reviews, and sprint planning. Contribute to knowledge sharing and technical documentation.

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Lead a cross-functional team including engineers, designers, data scientists, and researchers to develop generative AI-enabled solutions for external riders and internal operations. Drive discovery into unmet needs, shape product vision, define priorities to achieve customer and business objectives, establish success metrics, and explore technical feasibility. Work closely with leadership across Product & Experience, Software, and Vehicle Engineering to implement AI solutions for the ride-hail service. Design AI-generated capabilities to enhance consumer experience, utilize data and market insights to guide product strategies, integrate user research into product requirements, oversee planning and management of tools and product scalability, collaborate with engineers and designers, coordinate cross-functional teams to meet milestones, lead the creation and launch of generative AI products, and develop and analyze performance metrics to gauge product success.

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The Senior AI Product Manager at OpusClip is responsible for bridging the gap between complex AI research and seamless user experiences by transforming raw model capabilities and complex workflows into polished products that serve millions of creators. They act as the final filter for aesthetic quality, ensuring every feature meets high standards for rhythm, composition, and visual harmony. They lead rapid prototyping by building functional proofs-of-concept, working directly with APIs and codebase to validate hypotheses before full-scale engineering. Additionally, they identify latent creator needs and competitive gaps to prioritize bold and high-impact product bets over incremental iterations, architecting the future of digital storytelling through multimodal AI.

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The Senior AI Product Manager at OpusClip is responsible for bridging the gap between complex AI research and seamless user experiences by transforming raw model capabilities and complex workflows into polished products. They act as the final filter for aesthetic quality, ensuring every feature meets high standards of rhythm, composition, and visual harmony. They lead rapid prototyping by building functional proofs-of-concept, working directly with APIs and codebase to validate hypotheses before full-scale engineering. They also identify latent creator needs and competitive gaps early on and prioritize bold, high-impact product decisions over incremental changes, architecting the future of digital storytelling and video creation.

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Design model policies that govern safe model behavior in an objective and defensible way, determining how models should respond in risky or unsafe scenarios and defining what unsafe means while balancing safety with beneficial model capabilities. Develop taxonomies that guide data collection campaigns, model behavior, and monitoring strategies, balancing maximizing utility with preventing catastrophic risk. Lead prioritization efforts for safety across the company related to new model launches, addressing technical and business trade-offs. Develop broad subject matter expertise while maintaining agility across various topics. Collaborate across many internal teams, requiring high organizational acumen and confident decision making.

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As a Data Engineer, you will build and evolve the data backbone of an AI-first product including document intelligence, time-series IoT data, and agentic AI systems. You will design, implement, and operate data systems across the full lifecycle from raw ingestion to AI-driven outputs used by customers. You will work directly with customers and internal stakeholders to understand problems and translate them into technical solutions, iterating quickly. Responsibilities include building pipelines that support document processing, sensor data, and ML workflows, contributing to feature engineering and model experimentation when needed, and owning systems in production. You will make architectural decisions, improve system reliability over time, and help define best practices as the team and product scale.

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Lead and grow the Clinician Science team by recruiting, developing, and managing clinician scientists with diverse specialty expertise, while fostering a culture of clinical rigor, intellectual curiosity, and cross-functional collaboration. Ensure clinical excellence across product development by partnering with product teams to embed clinical expertise into feature discovery, design, and validation, translating clinical workflows and best practices into actionable product requirements. Define and uphold clinical quality standards by establishing frameworks for what "clinically meaningful" means across specialties, note types, and workflows, and guide the team in evaluating note quality, prompt iterations, and model outputs through LFD sessions and systematic assessments. Build scalable clinical evaluation systems by partnering with ML Science to develop LLM judges, annotation frameworks, and evaluation pipelines that scale clinical expertise and ensure evaluation methodologies reflect real-world clinical judgment. Serve as a translator between clinical reality and ML/engineering capabilities to help both engineers and clinicians understand respective nuances and constraints. Close the feedback loop with Commercial by partnering with Clinical Success and Solutions Consulting to surface real-world usage insights, customer feedback, and quality issues, ensuring product development stays grounded in how clinicians actually use Abridge. Represent the clinical perspective in Builder strategy by participating in roadmap planning, feature prioritization, and cross-pod alignment discussions, acting as the voice of clinical credibility in product decisions.

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As an AI Solution Architect at Mistral AI, the responsibilities include driving the adoption and deployment of Mistral's AI solutions by working closely with customers from strategic vision to production implementation. This involves leading executive-level workshops to identify business challenges and opportunities, co-creating AI adoption roadmaps with customers, and collaborating with Account Executives to develop business cases and align solutions with customer objectives. The role requires architecting end-to-end AI solutions that integrate Mistral's models and platform into customer workflows and infrastructure, partnering with the Applied AI team to design, prototype, and deploy solutions, and overseeing pilot projects and proofs-of-value to demonstrate technological potential. The architect serves as a trusted advisor guiding customers' AI strategies, monitoring KPIs related to business outcomes, and identifying expansion opportunities. Additionally, the role acts as a liaison between customers and internal teams, develops reusable assets and best practices for consistent delivery, and involves travel to foster client relationships and support on-site deployment.

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The AI Solution Architect is responsible for driving the adoption and deployment of Mistral’s AI solutions by working closely with customers from strategic vision to production implementation. This includes leading executive-level workshops to identify business challenges and opportunities, co-creating AI adoption roadmaps, collaborating with Account Executives on business cases, architecting end-to-end AI solutions integrating Mistral's models and platform into customer workflows and technical infrastructure, partnering with the Applied AI team to design, prototype, and deploy AI solutions in production, executing pilot projects and proofs-of-value, serving as a trusted advisor to customers to guide their AI strategy and maximize investment value, monitoring KPIs tied to business outcomes and communicating progress to executive sponsors, proactively identifying expansion opportunities within accounts, acting as a bridge between customers and Mistral’s internal teams to influence product and research roadmaps, developing reusable assets, best practices, and playbooks to scale go-to-market efforts, and traveling approximately 30-60% to foster client relationships and support on-site deployment.