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.
Deployment Lead
As an Applied Research Engineer at Labelbox, you will create frameworks and tools to construct, train, benchmark, and evaluate autonomous agent capabilities. You will design agent-focused data programs using supervised fine-tuning (SFT) and reinforcement learning (RL) methodologies. You will develop data pipelines from diverse sources such as code repositories, web browsers, and computer systems. You will implement and adapt popular open-source agent libraries and benchmarks with proprietary datasets and models. You will engage with research teams in frontier AI labs and the wider AI community to understand evolving agent data needs for frontier models and share best practices. You will collaborate closely with frontier AI lab customers to understand their requirements and guide model development. Additionally, you will publish research findings in academic journals, conferences, and blog posts.
TPM Manager
As an Applied Research Engineer at Labelbox, you will create frameworks and tools to construct, train, benchmark, and evaluate autonomous agent capabilities. You will design agent-focused data programs using supervised fine-tuning (SFT) and reinforcement learning (RL) methodologies, develop data pipelines from diverse sources such as code repositories, web browsers, and computer systems, and implement and adapt popular open-source agent libraries and benchmarks with proprietary datasets and models. Your role includes engaging with research teams in frontier AI labs and the wider AI community to understand evolving agent data needs for frontier models and share best practices, collaborating closely with frontier AI lab customers to understand requirements and guide model development, and publishing research findings in academic journals, conferences, and blog posts.
Forward Deployed Engineering Manager
As an Applied Research Engineer at Labelbox, you are responsible for creating frameworks and tools to construct, train, benchmark, and evaluate autonomous agent capabilities. You design agent-focused data programs using supervised fine-tuning (SFT) and reinforcement learning (RL) methodologies. You develop data pipelines from diverse sources such as code repositories, web browsers, and computer systems. You implement and adapt popular open-source agent libraries and benchmarks with proprietary datasets and models. You engage with research teams in frontier AI labs and the wider AI community to understand evolving agent data needs and share best practices. You collaborate closely with frontier AI lab customers to understand their requirements and guide model development. Additionally, you publish research findings in academic journals, conferences, and blog posts.
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.
Forward Deployed Engineer Intern
As an Applied Research Engineer at Labelbox, you will develop systems and methods to create, analyze, and leverage high-quality human-in-the-loop data for frontier AI model developers. This includes designing and implementing advanced systems that align human feedback into AI training processes such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO). You will work on techniques to measure and improve human data quality, develop AI-assisted tools to enhance the data labeling process, and investigate how different types of human feedback impact model performance and alignment. Your work will involve optimizing human feedback collection through novel algorithms, integrating breakthroughs into Labelbox's product suite to make human-AI alignment scalable, engaging with customers and the AI community to understand data needs and share best practices, publishing research, exploring new frontiers in human-AI collaboration, creating technical documentation, blog posts, and educational content, and driving industry innovation through these activities.
Forward Deployed Engineering Manager
As an Applied Research Engineer at Labelbox, responsibilities include developing cutting-edge systems and methods to create, analyze, and leverage high-quality human-in-the-loop data for frontier model developers, designing and implementing advanced systems that align human feedback into AI training processes such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO), working on innovative techniques to measure and improve human data quality, developing AI-assisted tools to enhance the data labeling process, advancing AI alignment methods to better reflect human preferences, improving human-in-the-loop data quality through rigorous measurement and enhancement systems, increasing efficiency and effectiveness in AI-assisted data labeling by creating tools using active learning and adaptive sampling, investigating the impact of different types of human feedback on model performance and alignment, optimizing human feedback collection with novel algorithms, integrating research breakthroughs into Labelbox’s product suite, engaging with customers and the AI community to understand data needs and share best practices, publishing in top-tier journals and conferences, continuously exploring new frontiers in human-AI collaboration and AI alignment, and establishing technical documentation and educational content to influence human-centric AI development.
Forward Deployed Research Scientist
As an Applied Research Engineer at Labelbox, the role involves developing cutting-edge systems and methods to create, analyze, and leverage high-quality human-in-the-loop data for frontier model developers. Responsibilities include designing and implementing advanced systems to align human feedback into AI training processes such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO). The role requires working on innovative techniques to measure and improve human data quality, developing AI-assisted tools to enhance the data labeling process, advancing AI alignment by creating new methods that better reflect human preferences, and increasing efficiency in AI-assisted data labeling through active learning and adaptive sampling. Additionally, the role involves investigating the impact of different types of human feedback on model performance and alignment, optimizing human feedback collection algorithms, integrating research breakthroughs into Labelbox's product suite, engaging with customers and the AI community to understand evolving data needs, publishing research in top-tier journals and conferences, and creating technical documentation and educational content to establish Labelbox as a thought leader in human-centric AI development.
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