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.
Software Engineer, Agent (Cantonese Speaking)
Design and deliver production-grade AI agents that are highly performant, reliable, and intuitive, driving revenue directly to Sierra's growth across industries like finance, healthcare, and commerce. Take complete ownership and autonomy in the Agent Development Life Cycle from initial pilot through deployment and continuous iteration, including building, tuning, and evolving AI agents in production environments while defining best practices. Work directly with leaders of large enterprises and cutting-edge startups to understand their business challenges and develop AI agents that transform their operations at scale. Guide the evolution of Sierra's core platform through customer interaction, surfacing unmet needs, prototyping new tools and features, and collaborating with research, product, and platform teams to shape AI agent development and Sierra's products.
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, Backend
As a backend engineer, you would play a critical role in the search architecture at Exa. Your work may involve building massive-scale machine learning systems, working on projects based on your skills and interests, such as recreating Google-level keyword search over 10 billion pages in one month, building state-of-the-art crawling systems that work optimally for any website, and building custom vector databases that can run over a billion vectors in under 100 milliseconds.
AI Deployment Engineering Manager, Startups
The AI Deployment Engineering Manager, Startups, is responsible for leading and scaling the Startups AI Deployment Engineering team to help high-growth startups move quickly from experimentation to production, unlock meaningful usage, and build durable technical partnerships with OpenAI. This includes crafting and continuously refining the strategic vision and operating model for the team to align with OpenAI's broader objectives and startup customer needs, leading and mentoring a team of technical individual contributors, identifying technical blockers for startups and advising on architecture and deployment paths, partnering closely with Sales to accelerate adoption and account growth, representing the technical voice of startup customers by synthesizing feedback, translating recurring startup needs into repeatable playbooks and assets, serving as a senior technical escalation point for priority startup customers, balancing urgent customer needs with OpenAI's product and platform priorities, coaching AI Deployment Engineers on various skills including technical quality and executive communication, and collaborating across multiple OpenAI departments to enhance support for startups from early adoption through scaled production usage.
Solutions Architect (APAC)
The Solutions Architect is responsible for designing scalable, highly-available infrastructure for AI platform deployments including compute, storage, networking, security, enterprise integration patterns, Infrastructure as Code (Terraform, Helm), multi-region HA/DR strategies, and CI/CD pipelines. They also design multi-agent systems using different patterns, implement agent logic with modern frameworks (langchain/langgraph), create evaluation frameworks, optimize prompts with A/B testing, and guide deployment and operations. Additionally, they lead technical maturity assessments, work directly with enterprise customers to understand requirements and offer recommendations, and collaborate with Engagement Managers and Product/Engineering teams.
Sales Development Representative, West
Debug and fix issues in the platform and ship PRs with fixes. Build internal tools and copilots powered by generative AI to support the team. Rapidly prototype proof-of-concepts for customer use cases. Collaborate across Engineering, Product, and Solutions teams to unblock customers and advance AI adoption.
Machine Learning Engineer (Singapore)
Build and scale systems for ingesting, processing, and delivering large-scale video and multimodal data for model training. Own the full pipeline from raw content to curated, filtered, and training-ready datasets focusing on speed, reliability, reproducibility, and cost-efficiency. Design and scale distributed data pipelines for preprocessing, dataset generation, and repeated dataset refreshes. Own workflow orchestration, job scheduling, monitoring, and failure recovery for large-scale data processing jobs. Implement and maintain containerized pipeline infrastructure using Kubernetes or equivalent orchestration systems. Optimize cloud-based data storage and movement across providers (AWS, GCS, or Azure) for cost, throughput, and operational efficiency. Define and implement best practices for dataset storage layout, versioning, caching, retention, and access patterns. Design and implement curation pipelines for selection, filtering, and retention of video and image content for model training including image-text pair datasets. Build and improve VLM-based captioning and metadata generation workflows at scale across video and image data. Develop and apply quality and aesthetic scoring models, CLIP-based semantic filtering, and other signal-extraction approaches for data selection. Build tooling to support deduplication workflows at scale, including near-dedup and exact deduplication pipelines over large video corpora. Analyze dataset composition, identify quality issues, iterate on curation logic to improve training outcomes. Define and evolve standards for high-quality, training-ready video data across different training regimes.
Research Scientist (Singapore)
Drive foundational research on video generation models, taking ownership across the full research cycle and driving post-training research. Collaborate closely with data, infrastructure, and adjacent modeling teams to translate research findings into durable model improvements. Build and maintain scalable systems for ingesting, preprocessing, and delivering large-scale video data for model training. Design and scale distributed data pipelines for preprocessing, dataset generation, and repeated dataset refreshes. Own workflow orchestration, job scheduling, monitoring, and failure recovery for large-scale data processing jobs. Implement and maintain containerized pipeline infrastructure using Kubernetes or equivalent orchestration systems. Optimize cloud-based data storage and movement across providers (AWS, GCS, or Azure) for cost, throughput, and operational efficiency. Define and implement best practices for dataset storage layout, versioning, caching, retention, and access patterns. Build tooling to support deduplication workflows at scale, including near-dedup pipelines over large video corpora. Research and develop distillation methods for large-scale diffusion and flow-based video generation models, including guidance distillation and adversarial distillation, focusing on preserving or improving generation quality while reducing inference cost. Develop reward models and preference-based fine-tuning pipelines that align video generation quality with human judgments across aesthetics, motion quality, and prompt adherence. Analyze the relationship between base model behavior and post-training outcomes, working with foundation model team to inform pretraining decisions accordingly.
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