AI Jobs in Mexico

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The AI Safety Engineer is responsible for designing and building scalable backend infrastructure for content moderation, abuse detection, and agents guardrails by deploying AI/ML models into production systems. They will architect robust APIs, data pipelines, and service architectures to support real-time and batch moderation workflows. The role includes implementing comprehensive monitoring, alerting, and observability systems, establishing SLIs, SLOs, and performance benchmarks. The engineer will collaborate with ML engineers to translate research models into production-ready systems and integrate them across the product suite. Additionally, they will drive technical decisions and contribute to the vision for the safety roadmap to build next-generation platform guardrails for scale and precision.

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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 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.

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Lead and develop a high-performing engineering team to build core monetization and ads systems at OpenAI. Define and execute the technical strategy and roadmap for next-generation monetization infrastructure while collaborating closely with product, design, and research partners.