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.
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.
Staff Engineer, Applications (R4828)
Work closely with customers to understand their requirements, provide technical expertise and customer support during deployment, and ensure successful integration of Hivemind software. Deploy with customers on site globally, supporting software integration and development activities, including frequent international travel. Become an expert user of the Hivemind enterprise software stack and its autonomy modules. Provide technical support and training to customers on the use of Hivemind. Develop AI and Autonomy applications using the Shield AI enterprise software development kit. Assist the sales team with pre-sales activities such as demos, conferences, and immersions, and support post-sales deployment and integration of Shield AI enterprise software products. Develop and maintain technical documentation and training materials. Help customers debug software and API integration issues. Collaborate with the product engineering team to address customer feedback and improve products. Act as a technical leader by elevating team performance, driving execution across cross-functional teams, and ensuring successful delivery in complex environments.
Senior Engineer, Applications (R4792)
Deploy with customers on site globally to support software integration and development activities, with approximately 50% travel. Become an expert user of the Hivemind enterprise software stack and its various autonomy modules. Provide technical support and training to customers on use of Hivemind. Develop AI and Autonomy applications using the Shield AI enterprise software development kit. Assist the sales team in pre-sales activities such as demos, conferences, and immersions. Assist in post-sales deployment and integration of Shield AI enterprise software products. Develop and maintain technical documentation and training materials. Help customers debug software and API integration issues. Collaborate with the product engineering team to address customer feedback and improve products.
Engineer II, Applications (R4789)
Applications Engineers are responsible for deploying Shield AI's Hivemind software in real-world environments, working closely with customers to understand their requirements, providing technical expertise and customer support during deployment, and ensuring successful integration of Hivemind. They collaborate internally with engineering teams to develop and test new autonomy capabilities. The role involves frequent travel, often international, to work alongside customers on-site. Responsibilities include supporting software integration and development activities on-site with customers, becoming an expert user of Hivemind enterprise software and its autonomy modules, providing technical support and training to customers, developing AI and Autonomy applications using the Shield AI software development kit, assisting sales teams in pre-sales activities, helping in post-sales deployment and integration of the software products, developing and maintaining technical documentation and training materials, debugging software/API integration issues with customers, and collaborating with the product engineering team to address customer feedback and improve products.
RISC-V AI / HPC & Agentic Software Engineer
Lead and contribute to cross-functional efforts solving complex physical design challenges across IPs, projects, and advanced technology nodes. Develop and enhance RTL-to-GDS methodologies, including floorplanning, synthesis, placement and routing (P&R), static timing analysis (STA), signoff, and assembly. Architect and deploy AI/ML-driven solutions in production flows to improve engineering efficiency, turnaround time, and quality of results (QoR). Optimize EDA tools and custom CAD flows using data-driven and machine learning-based techniques, collaborating closely with verification, extraction, timing, Design for Test (DFT), and EDA vendors.
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.
Safety Engineer
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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