Research Scientist / Research Engineer - Foundations, Search

About the Team The Foundations Research team works on high-risk, high-reward ideas that could shape the next decade of AI. Our goal is to advance the science and data that enable our training and scaling efforts, with a particular focus on future frontier models. Pushing the boundaries of data, scaling laws, optimization techniques, model architectures, and efficiency improvements to propel our science. The Search team sits within Foundations, building agentic search by co-designing model–system interfaces with the core search stack (serving, indexing, retrieval) to translate model intent into reliable, real-world actions. Operating at the frontier of AI and information retrieval, the team develops large-scale systems that transform and index vast corpora, enabling models to reason over global knowledge and act dependably. In close partnership with researchers, we rapidly bring modeling breakthroughs into production and redefine how intelligent systems discover, retrieve, and synthesize information at planetary scale. About the Role We’re looking for a researcher focused on our search efforts. You’ll work with a a team of world-class research scientists and engineers developing foundational technology that enables models to retrieve and condition on the right information, at the right time. This includes designing new embedding training objectives, scalable vector store architectures, and dynamic indexing methods. This work will support retrieval across many OpenAI products and internal research efforts, with opportunities for scientific publication and deep technical impact. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. Responsibilities Tackle embedding models and retrieval systems optimized for grounding, relevance, and adaptive reasoning. Collaborate with a team of researchers and engineers building end-to-end infrastructure for training, evaluating, and integ

P-1428 At Databricks, we are passionate about enabling data and AI teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started. As part of the AI Platform team, you’ll build the substrate that powers everything from data apps, AI agents, model training, model serving, and vector search. You’ll be joining a high-agency, high-visibility team operating at the frontier of AI infrastructure — with deep ties to research, product, and real-world enterprise use cases. Databricks Mosaic AI is one of our fastest-growing businesses helping thousands of our customers democratize AI within their organizations. We’re building the infrastructure that powers the next generation of AI. We’re hiring across multiple teams in our AI Engineering org including: The impact you will have: Build infrastructure that powers our flagship offerings like MLflow, AI Gateway, Databricks Apps, Agent Framework, Agent Bricks, and Foundation Model APIs, to state a few. Improve reliability, latency, and efficiency of distributed AI workloads Collaborate with platform, infra, and ML teams to deliver seamless end-to-end experiences Shape how developers and data scientists build and interact with AI on Databricks What we look for: 5+ years of experience in backend or infrastructure engineering Strong programming skills in Scala, Go, or Python Experience with distributed systems, scalable APIs, or cloud-native infrastructure Familiarity with service

Perplexity serves tens of millions of users daily with reliable, high-quality answers grounded in an LLM-first search engine and specialized data sources. The Answer Quality team ensures that our prompts, tools, search systems, datasets, and models work together to create the best possible experience for our users. As our product and agent capabilities evolve, we need evaluation systems that are fast, reliable, production-faithful, and actionable. In this role, you will build and improve the technical foundations that support Answer Quality across Perplexity. This includes our shared evaluation infrastructure and the platform used to replay and analyze agent traces. You will work closely with data scientists, engineers, and product teams to identify quality problems, measure their impact, and turn evaluation findings into product improvements. Responsibilities Build shared evaluation infrastructure that helps teams run reliable evals, analyze results, and make product and model decisions Develop the platform for replaying and analyzing agent traces to reproduce production behavior and diagnose failures Build and operate scalable systems for processing, storing, and monitoring interaction, trace, and evaluation data Partner with data scientists, engineers, and product teams to turn answer-quality problems into evaluations, analyses, and product improvements Operate in a small, high-impact team where your work directly shapes how Perplexity measures and improves Answer Quality Qualifications 4+ years of software, data, or machine learning engineering experience shipping and operating production systems Strong proficiency in Python and SQL, with solid fundamentals in system design, data modeling, and distributed systems Experience building big-data systems, including distributed compute, large-scale storage, and high-volume pipelines Demonstrated ownership of ambiguous technical projects from initial design through production operation Ability to work effectively with

About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible. At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI. Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here . At Pinterest Labs , you'll work on tackling new challenges in machine learning and multi-modal large language models along with a world-class team of research scientists, and machine learning engineers. You'll conduct research that can be applied across Pinterest engineering teams and engage in external collaborations and mentoring, while also performing research in any of the following areas: natural language processing (NLP) and reasoning capability, computer vision for multi-modality, graph neural network, inclusive and responsible AI, reinforcement learning, user modeling, and recommender systems. What you’ll do: Contribute to cutting-edge research in machine learning and LLM that can be applied to Pinterest problems, especially search agent, recommendation agent, reason and planning agent Collect, analyze, and synthesize findings from data

Perplexity is seeking top-tier AI Research Scientists and Engineers to advance our AI products and capabilities. We're building the future of AI-powered search and agent experiences through our Sonar models, Deep Research Agent, Comet Agent, and Search products. Join us in creating SOTA experiences that handle hundreds of millions of queries and continue to scale rapidly. Team Structure Depending on your interests and expertise, you'll work on one of three specialized teams: 1. Core Research Team (Horizontal) Focus on generating and improving base models that power all our products. This team works on foundational model capabilities, post-training techniques, building RL infra and infrastructure that benefits the entire organization. 2. Agent Products Team (Vertical) Concentrate on fine-tuning and optimizing models for our Deep Research Agent and Labs/Canvas products. This team bridges research and product, ensuring our agent capabilities deliver exceptional user experiences. 3. Comet Agent Team (Vertical) Dedicated to developing and enhancing our Comet Agent product. This specialized team focuses on the unique requirements and optimizations needed for Comet's specific use cases. Responsibilities Research & Development Post-train SOTA LLMs using the latest supervised and reinforcement learning techniques (SFT/DPO/GRPO) Leverage our rich query/answer dataset to scale model performance across Sonar, Deep Research, Comet, and Search products Stay current with the latest LLM research, especially in model training, optimization, and personalization techniques Implement preference optimization and personalization capabilities to enhance user experience Invent in-house improvements and optimizations to enhance SOTA models Turn research ideas into algorithms and run experiments to launch new models Infrastructure & Implementation Own full-stack data, training, and evaluation pipelines required for model development Build robust and effective training frameworks (on

About the Team The Proactivity Research team, within OpenAI’s broader Personal AGI team, is focused on making our models in ChatGPT and future potential products proactive in ways that are truly useful. We're laying the technical foundations for AI that can anticipate what users need in real time, adapt as their goals and preferences shift, and build a deeper, evolving understanding of the person it's helping. About the Role As a Research Engineer / Scientist, you will research and develop improvements to our models’ personalization and agentic capabilities. Our team works on reinforcement learning, dataset creation, evaluations, and other post-training methods. We partner closely with research and product teams across the company to realize the vision of a highly personalized, collaborative, and proactive assistant. We're looking for individuals with strong ML engineering skills and research experience, especially with novel and highly capable models. An ideal candidate is passionate about product-driven research. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Own and pursue a research agenda to improve the proactivity and ability of our models to further user goals. Build robust evaluations for tracking modeling improvements. Design, implement, test, and debug code across our research stack. Collaborate closely with the other research and product teams to influence the shape of technical solutions in the product You might thrive in this role if you: Have a deep understanding of machine learning and machine learning applications. Have a working knowledge of LLM post-training and evaluation approaches Are passionate about, or have experience thinking about, personalization and enabling users to achieve their goals Are comfortable diving into a large ML codebase to debug. Thrive in a dynamic and technically complex environment. About OpenAI