Research Intern, Inference (Fall 2026)
As an AI Infrastructure Engineer at Together, the responsibilities include participating in on-call rotation to respond to production incidents, building and running infrastructure using Ansible, Terraform, and Kubernetes to support scaling to a large number of concurrent users, building monitoring systems to ensure high-quality service, designing and implementing operational processes such as deployments and upgrades, debugging production issues across all services and stack levels, identifying improvements for product architecture in terms of reliability, performance, and availability, and planning the growth of Together AI's infrastructure.
Member of Technical Staff (Machine Learning Engineer)
Translate cutting-edge research into production-ready machine learning systems. Design, build, and deploy end-to-end ML models and pipelines. Develop and optimize models for image and video processing. Own the full ML lifecycle including experimentation, training/fine-tuning, evaluation, and deployment. Rapidly prototype using open-source models and adapt them for product needs. Conduct experiments, analyze results, and iterate to improve performance. Collaborate with researchers and cross-functional teams (product, engineering, design) to deliver ML solutions at scale. Participate with advancements in machine learning and apply them to continuously improve products.
Warehouse Supervisor (Temporary)
Utilize proprietary software to provide accurate input and labels for healthcare and administration projects, ensuring high-quality data for AI model training. Deliver curated, high-quality data for scenarios involving patient care coordination, medical billing, administrative workflows, and healthcare operations. Collaborate with technical staff to support the training of new AI tasks and contribute to the development of innovative technologies. Assist in designing and improving efficient annotation tools tailored for healthcare and administration data. Select and analyze complex problems in healthcare and administration fields aligned with your expertise to enhance AI model performance. Interpret, analyze, and execute tasks based on evolving instructions, maintaining precision and adaptability.
Legal Advisor (US Bar Admitted) - Freelance AI Trainer
Contributors may generate prompts that challenge AI; evaluate AI-generated solutions for correctness, assumptions, and logic; improve AI reasoning to align with first principles and accepted standards; and apply structured scoring criteria to assess multi-step problem solving.
Go-to-Market - Monterrey, Mexico
Own the end-to-end technical onboarding experience for new enterprise customers, from kickoff through successful integration. Build and maintain integration scripts, tooling, and documentation to accelerate customer time-to-value. Serve as the primary technical point of contact during the onboarding phase, translating customer requirements into actionable engineering solutions. Diagnose integration issues across customer environments (APIs, data pipelines, cloud infrastructure) and drive them to resolution. Collaborate closely with product and engineering to surface customer feedback and influence the roadmap. Develop reusable onboarding playbooks and internal tooling to scale the deployment process. Travel to customer sites occasionally for critical onboarding milestones or technical workshops.
Manager of Commercial Partnerships, Robotics
As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, supporting end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and resilient cloud infrastructure for international government partners. You will take full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies, oversee the end-to-end health of the platform ensuring seamless integration between AI core and all full-stack components from APIs to UI, and build automated systems to monitor model performance and data drift across geographically dispersed environments. Additionally, you will manage the technical lifecycle within diverse regulatory frameworks, lead production issue response in mission-critical environments, translate technical performance metrics into clear insights for senior government officials, and collaborate with Engineering and ML teams to influence future technical architecture and decisions.
Mid/Senior AI Cinematic Video Editor (Full Remote - Mexico)
Conceptualise scripts based on current production needs centred around existing AI characters. Create narrative-driven, longform video content including stylized and explicit NSFW visuals focused on storytelling, atmosphere, and visual coherence. Manage end-to-end AI video production workflows from ideation and prompting to generation, editing, and post-production. Work extensively with ComfyUI pipelines to build, customize, and optimize node-based workflows for image and video generation. Use tools such as Stable Diffusion (AUTOMATIC1111), ComfyUI, Runway, and other AI video platforms to produce high-quality visual sequences. Maintain consistent character appearance, style, and scene continuity across narratives using advanced techniques. Integrate motion graphic design and colour correction to deliver cohesive final outputs. Experiment rapidly with new AI models, tools, and techniques, incorporating them into workflows and sharing skills with the team. Align with Content Lead’s creative direction while maintaining autonomy in execution and technical decisions. Continuously refine workflows for efficiency, scalability, and output quality.
Software Engineer (Brazil)
Design, develop, test, deploy, maintain, and improve scalable, secure, and high-performance backend systems with a focus on high availability, low latency, and cost-effectiveness. Act as the subject matter expert in infrastructure when designing new products and introducing new technology to existing products. Collaborate closely with engineering and research teams to integrate infrastructure components with product features to optimize system performance and user experience. Design event-driven architectures and develop APIs and microservices for real-time processing and analytics. Ensure system reliability, performance, and scalability through monitoring, logging, and error handling. Stay current with emerging trends, technologies, and methodologies to enhance infrastructure capabilities. Participate in code reviews, contribute to open-source projects, and mentor junior engineers.
AI Pilot Assistant (Freelance)
As an AI Agent Assistant, collaborate with large language models (LLMs) that handle repetitive tasks, bringing nuance, judgment, and creativity to deliver high-quality results. Work alongside AI to shape and complete outputs to ensure they are accurate, reliable, and ready for real-world application. Tasks may include fact-checking scientific claims, curating datasets, conducting market research, and refining sales leads. Deliver well-reasoned, accurate, and clearly written outputs backed by credible sources. Conduct thorough web research to verify information and collect supporting evidence. Collaborate with LLMs and internal tools using them as copilots to complete complex tasks. Design and refine prompts to guide AI toward higher-quality results. Apply best practices for working with LLMs, understanding their strengths and limitations. Adapt quickly between diverse annotation, research, and analysis tasks while following detailed guidelines.
Senior ML Operations (MLOps) Engineer
The Senior ML Operations (MLOps) Engineer at Eight Sleep is responsible for introducing and implementing cutting-edge ML technologies, owning the design and operation of robust ML infrastructure including scalable data, model, and deployment pipelines to ensure reliable model delivery to production. They collaborate cross-functionally with R&D, firmware, data, and backend teams to ensure reliable and scalable ML inference on Pods. They optimize ML systems for cost, scalability, and performance across training and inference, and develop tooling, microservices, and frameworks to streamline data processing, experimentation, and deployment. The role requires effective communication in a remote work environment.
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