AI Jobs in Mexico

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Translate research into product by working with client-side researchers on post-training, evaluations, safety, and alignment to build the necessary primitives, data, and tooling. Partner closely with core customers and frontier research labs to tackle technical challenges related to model improvement, performance, and deployment. Shape and propose model improvement work by translating customer and research objectives into technically rigorous proposals and execution plans. Lead the end-to-end lifecycle of projects including discovery, writing PRDs and technical specs, prioritizing trade-offs, running experiments, shipping solutions, and scaling successful pilots. Lead high-stakes engagements with senior stakeholders, define success metrics, identify risks, and drive programs to measurable outcomes. Collaborate across teams including research, platform, operations, security, and finance to deliver production-grade results. Design and implement robust evaluation frameworks, ensure data quality and feedback loops, and share learnings to elevate technical execution across accounts.

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The role involves translating AI research into product solutions by working with client-side researchers on post-training, evaluations, safety, and alignment, building the necessary primitives, data, and tooling. The engineer partners closely with leading AI teams and frontier research labs to solve complex technical problems related to model improvement, performance, and deployment, shaping and proposing technically rigorous model improvement work. Responsibilities include leading the end-to-end lifecycle from discovery to scalable pilots, conducting technical working sessions with senior stakeholders, defining success metrics, managing risks, and driving programs to measurable outcomes. The role requires collaboration with research, platform, operations, security, and finance teams to deliver reliable, production-grade solutions. Additionally, the engineer designs and establishes robust evaluation frameworks, closes feedback loops on data quality, and shares best practices across accounts.

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The AI / ML Solutions Engineer at Anyscale is responsible for designing, implementing, and scaling machine learning and AI workloads using Ray and Anyscale directly with customers. This includes implementing production AI / ML workloads such as distributed model training, scalable inference and serving, and data preprocessing and feature pipelines. The role involves working hands-on with customer codebases to refactor or adapt existing workloads to Ray. The engineer advises customers on ML system architecture including application design for distributed execution, resource management and scaling strategies, and reliability, fault tolerance, and performance tuning. They guide customers through architectural and operational changes needed to adopt Ray and Anyscale effectively. Additionally, the engineer partners with customer MLE and MLOps teams to integrate Ray into existing platforms and workflows, supports CI/CD, monitoring, retraining, and operational best practices, and helps customers transition from experimentation to production-grade ML systems. They also enable customer teams through working sessions, design reviews, training delivery, and hands-on guidance, contribute feedback to product, engineering, and education teams, and help develop reference architectures, examples, and best practices based on real customer use cases.

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The AI Outcomes Manager will partner with executive sponsors and end users to identify high-impact use cases and turn them into measurable business outcomes on Glean. They will lead strategic reviews and advise customers on their AI roadmap to ensure maximum value from Glean's platform. The role involves translating business needs into clear problem statements, success metrics, and practical AI solutions while collaborating with Product and R&D to shape priorities. They will conduct discovery workshops, scope pilots, and guide rollouts to drive broad and deep adoption of the Glean platform. Additionally, they will design and build AI agents with and for customers, including rethinking and redesigning underlying business processes to maximize impact and usability. The manager will proactively identify expansion opportunities and drive engagement across teams and functions.

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The role involves training, evaluating, and monitoring new and improved LLMs and other algorithmic models. The engineer will test and deploy content moderation models in production and iterate based on real-world performance metrics and feedback loops. They are expected to develop medium to long-term vision for content understanding-related R&D, collaborating with management, product, policy & operations, and engineering teams. The position requires taking ownership of results delivered to customers, advocating for changes in approach where needed, and leading cross-functional execution.

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Invent and prototype new model architectures that optimize inference speed, including on edge devices; build and maintain evaluation suites for multimodal performance across a range of public and internal tasks; collaborate with the data and infrastructure teams to build scalable pipelines for ingesting and preprocessing large audio datasets; work with the infrastructure team to optimize model training across large-scale GPU clusters; contribute to publications, internal research documents, and thought leadership within the team and the broader ML community; collaborate with the applied research and business teams on client-specific use cases.

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Use proprietary software applications to provide input and labels on defined projects. Support and ensure the delivery of high-quality curated data. Contribute to the training of new tasks by working closely with the technical staff to develop and implement cutting-edge initiatives and technologies. Interact with technical staff to improve the design of efficient annotation tools. Choose problems from economics fields that align with expertise, focusing on macroeconomics, microeconomics, and behavioral economics. Regularly interpret, analyze, and execute tasks based on given instructions. Provide services including labeling and annotating data in text, voice, and video formats to support AI model training, sometimes involving recording audio or video sessions.

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Help users discover and master the Dataiku platform through user training, office hours, demos, and ongoing consultative support. Analyse and investigate various kinds of data and machine learning applications across industries and use cases. Provide strategic input to the customer and account teams that help make customers successful. Scope and co-develop production-level data science projects with customers. Mentor and help educate data scientists and other customer team members to aid in career development and growth.

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As Product Marketing Manager, you drive go-to-market strategies for OpenAI’s developer platform, focusing on adoption, strategic launch planning, and growth of APIs among developers and businesses. You collaborate closely with Product, Engineering, and GTM teams to develop product positioning, messaging, marketing plans, post-launch campaigns, and customer/market insights.

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You will design and build backend and infrastructure systems for OpenAI’s monetization and ads stack, emphasizing reliability, privacy, security, and large-scale performance. You’ll develop APIs and platforms, drive 0→1 infrastructure projects, and collaborate cross-functionally with Product, Research, and Design teams.