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 (R4830)
In this role, the Applications Engineer will work closely with customers to understand their requirements, provide technical expertise and customer support during deployment, and ensure successful integration of Hivemind software. Responsibilities include deploying with customers on site globally with approximately 50% travel to support software integration and development activities, becoming an expert user of the Hivemind enterprise software stack and its autonomy modules, providing technical support and training on Hivemind, developing AI and Autonomy applications using the Shield AI enterprise software development kit, assisting the sales team in pre-sales activities such as demos and conferences, assisting in post-sales deployment and integration of Shield AI software products, developing and maintaining technical documentation and training materials, helping customers debug software and API integration issues, collaborating with the product engineering team to address customer feedback and improve products, and acting as a technical leader across engagements by elevating team performance, driving execution across cross-functional teams, and ensuring successful delivery in complex environments.
Senior Engineer, Applications (R4829)
Deploy with customers on site globally (approximately 50% travel) to support software integration and development activities. Become an expert user of the Hivemind enterprise software stack and its various autonomy modules. Provide technical support and training to customers on the use of Hivemind. Develop AI & 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.
AI Deployment Engineer
The AI Deployment Engineer will deeply embed with the most strategic platform customers, serving as their technical thought partner in ideating and building novel applications on the OpenAI API. They will proactively guide customers on maximizing business impact from their applications and accelerating their time to value, experiment and prototype solutions with and for customers, and forge and manage relationships with customers’ leadership and stakeholders to ensure successful deployment and scale of applications. The role includes contributing to open-source developer and enterprise resources, scaling the AI Deployment Engineering function by sharing knowledge, codifying best practices, and publishing notebooks to internal and external repositories, and validating, synthesizing, and delivering high-signal feedback to the Product and Research teams. The engineer will use their expertise in programming with Python and Javascript.
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.
Operational Safety Lead - Defense
The role involves designing and building training, inference, and evaluation infrastructure to support the autonomy stack development, orchestrating massive GPU clusters to process petabytes of multimodal sensor data. It includes optimizing multimodal data ingestion and preprocessing pipelines such as LiDAR, camera, radar, and map priors to support perception and planning model development. The position requires working across cloud environments to support high-throughput distributed training and collaborating closely with the AI research team and autonomy teams. The engineer will work across the entire AI lifecycle including dataset generation, training frameworks, compute, evaluation, and deployment, and interact with external and internal users to collect feedback and contribute to team culture.
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