Senior Data Scientist - Data Foundations & AI

About Plaid Plaid builds tools that help developers create new financial products and experiences. Since 2013, Plaid has connected millions of users to over 12,000 financial institutions across the US, Canada, the UK, and Europe. The company partners with organizations like Venmo, SoFi, Fortune 500 firms, and major banks to make linking financial accounts to apps and services easier. Headquarters are in San Francisco, with offices in New York, Washington D.C., London, and Amsterdam. Team: Data Foundation & AI The Data Foundation and AI team designs and maintains the machine learning and AI infrastructure that supports Plaid’s products. This group transforms Plaid’s financial network data into flexible formats used by teams across the company. Responsibilities span the entire system lifecycle: data curation for pretraining, model development, deployment, serving, and monitoring in production. Role Overview: Senior Machine Learning Engineer (Research Scientist) This position focuses on applied research for Plaid’s foundation model. The Senior Research Scientist leads efforts to design model architectures, set pretraining objectives, and implement fine-tuning strategies that work across a range of product needs. The role also involves building and maintaining production machine learning systems, including training pipelines, model serving, feature engineering, and performance monitoring. Key Responsibilities Design model architectures and define pretraining objectives for Plaid’s foundation model Develop and apply fine-tuning methods for diverse product use cases Build and maintain end-to-end machine learning systems, from data pipelines to model serving Engineer features and monitor system performance in production Create evaluation frameworks to measure model quality across multiple tasks and metrics Location This role is based in San Francisco.

Artificial Intelligence is revolutionizing every aspect of our lives. At Scale AI, we are dedicated to accelerating the advancement of AI applications across industries. For nearly a decade, we have established ourselves as a premier AI data foundry, powering groundbreaking innovations in AI, including generative AI, defense systems, and autonomous technologies. With our recent investment from Meta, we are committed to enhancing our state-of-the-art post-training algorithms to achieve unparalleled performance for complex agents serving enterprises globally. The Enterprise ML Research Lab is at the forefront of this AI evolution. Our team develops a suite of proprietary research, tools, and resources tailored for our enterprise clients. As a Machine Learning Research Engineer on the Data Foundation team, you will engage in pioneering research to optimize the data flywheel that drives our entire machine learning ecosystem. Your work will involve exploring synthetic environments, defining tasks, building agents for trace analysis, and contributing to a cutting-edge framework that automates agent building through advanced evaluation techniques. You will create top-tier agents that deliver state-of-the-art results by leveraging sophisticated post-training and agent-building algorithms. If you are passionate about influencing the future of Generative AI, we encourage you to apply!

At Causal Labs, we are on a groundbreaking mission to develop general causal intelligence—artificial intelligence that not only predicts future events but also determines the most effective actions to influence those outcomes.To achieve this monumental goal, we are constructing a Large Physics Foundation Model (LPM). Our focus is on domains governed by physical laws, which inherently exhibit cause-and-effect relationships, setting them apart from traditional visual or textual data.Weather serves as the ideal training environment for our LPM, being one of the most extensively observed physical systems available. It provides immediate, objective feedback from sensory observations and boasts data scales significantly larger than those currently employed to train existing language models.Our team at Causal Labs includes leading researchers and engineers with backgrounds in self-driving technology, drug discovery, and robotics, hailing from prestigious organizations such as Google DeepMind, Cruise, Waymo, Meta, Nabla Bio, and Apple. We firmly believe that achieving general causal intelligence will represent one of the most critical technological advancements for our civilization.We are seeking innovative researchers eager to confront unsolved challenges in the field.This role presents an opportunity to create powerful models rooted in observable feedback and verifiable ground truths. If you possess experience in pioneering research and training large-scale models from the ground up in areas such as language and vision models, robotics, or biology, we invite you to join our mission.

About Scale AI At Scale AI, we are committed to propelling the advancement of AI technologies. For over eight years, we have been a pioneer in the AI data sector, supporting groundbreaking innovations in areas such as generative AI, defense solutions, and autonomous driving. Following our recent Series F funding round, we are enhancing access to premium data to accelerate the journey towards Artificial General Intelligence (AGI). Building on our legacy of model evaluation for both enterprise and governmental clients, we are expanding our capabilities to establish new benchmarks for evaluations in both public and private domains. About This Role This position is at the leading edge of AI research and practical implementation, concentrating on reasoning within large language models (LLMs). The successful candidate will investigate critical data types vital for evolving LLM-based agents, including browser and software engineering agents. You will significantly influence Scale’s data strategy by pinpointing optimal data sources and methodologies to enhance LLM reasoning. To excel in this role, you will require a profound understanding of LLMs, planning algorithms, and fresh approaches to agentic reasoning, alongside inventive solutions to challenges in data generation, model interaction, and evaluation. Your contributions will lead to transformative research on language model reasoning, facilitate collaboration with external researchers, and engage closely with engineering teams to translate cutting-edge advancements into scalable, real-world applications.

About Scale AI At Scale AI, we are dedicated to propelling the advancement of AI applications. Over the past eight years, we have established ourselves as the premier AI data foundry, supporting groundbreaking innovations in fields such as generative AI, defense technologies, and autonomous vehicles. Following our recent Series F funding round, we are intensifying our efforts to harness frontier data, paving the way toward achieving Artificial General Intelligence (AGI). Our work with enterprise clients and governments has enhanced our model evaluation capabilities, allowing us to expand our offerings for both public and private evaluations. About the ACE Team The Agent Capabilities & Environments (ACE) team, a vital part of Scale’s Research organization, unites customer-focused Researchers and Applied AI Engineers. Our primary mission is to conduct research on agent environments and reinforcement learning reward signals, benchmark autonomous agent performance in real-world contexts, and develop robust data programs aimed at enhancing the capabilities of Large Language Models (LLMs). We are committed to creating foundational tools and frameworks for evaluating models as agents, focusing on autonomous agents that interact dynamically with a wide range of external environments, including code repositories and GUI interfaces. About This Role This position sits at the cutting edge of AI research and its practical applications, concentrating on the data types necessary for the development of state-of-the-art agents, including browser and software engineering agents. The ideal candidate will investigate the data landscape required to propel intelligent and adaptable AI agents, steering the data strategy at Scale to foster innovation. This role demands not only expertise in LLM agents and planning algorithms but also creative problem-solving skills to tackle novel challenges pertaining to data, interaction, and evaluation. You will contribute to influential research publications on agents, collaborate with customer researchers, and partner with the engineering team to transform these advancements into scalable real-world solutions.

Join Handshake as a Machine Learning Research Scientist and contribute to groundbreaking projects that leverage advanced algorithms and data analysis to drive innovation. In this role, you will collaborate with a dynamic team to design, implement, and evaluate machine learning models that enhance our products and services. Your expertise will be pivotal in unlocking new insights from data, improving user experiences, and shaping the future of our technology.

At Scale AI, we collaborate with leading AI laboratories to supply high-quality data and foster advancements in Generative AI research. We seek innovative Research Scientists and Research Engineers with a strong focus on post-training techniques for Large Language Models (LLMs), including Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and reward modeling. This position emphasizes optimizing data curation and evaluation processes to boost LLM performance across text and multimodal formats. In this pivotal role, you will pioneer new methods to enhance the alignment and generalization of extensive generative models. You will work closely with fellow researchers and engineers to establish best practices in data-driven AI development. Additionally, you will collaborate with top foundation model labs, providing critical technical and strategic insights for the evolution of next-generation generative AI models.

About Our TeamJoin the Foundations Research team, where we tackle ambitious and innovative projects that could redefine the future of AI. Our mission is to enhance the science behind our training and scaling initiatives, focusing on pioneering frontier models. We are dedicated to advancing data utilization, scaling methodologies, optimization strategies, model architectures, and efficiency enhancements to accelerate our scientific breakthroughs.About the PositionWe are on the lookout for a dynamic technical research lead to spearhead our embeddings-focused retrieval initiatives. You will oversee a talented team of research scientists and engineers committed to developing foundational technologies that enable models to access and utilize the right information precisely when needed. This includes crafting innovative embedding training objectives, architecting scalable vector storage, and implementing adaptive indexing techniques.This pivotal role will contribute to various OpenAI products and internal research initiatives, offering opportunities for scientific publication and significant technical influence.This position is located in San Francisco, CA, where we embrace a hybrid work model, requiring three days in the office weekly, and we provide relocation assistance for new hires.Your ResponsibilitiesLead cutting-edge research on embedding models and retrieval systems optimized for grounding, relevance, and adaptive reasoning.Supervise a team of researchers and engineers in building an end-to-end infrastructure for training, evaluating, and integrating embeddings into advanced models.Drive advancements in dense, sparse, and hybrid representation techniques, metric learning, and retrieval systems.Work collaboratively with Pretraining, Inference, and other Research teams to seamlessly integrate retrieval throughout the model lifecycle.Contribute to OpenAI's ambitious vision of developing AI systems with robust memory and knowledge access capabilities rooted in learned representations.You Will Excel in This Role If You PossessA proven track record of leading high-performance teams of researchers or engineers within ML infrastructure or foundational research.In-depth technical knowledge in representation learning, embedding models, or vector retrieval systems.Familiarity with transformer-based large language models and their interaction with embedding spaces and objectives.Research experience in areas such as contrastive learning and retrieval-augmented generation.

At Scale AI, we are at the forefront of artificial intelligence, driving innovation through our advanced data, infrastructure, and tooling that empower the most sophisticated models worldwide. Our teams thrive at the intersection of pioneering research, extensive engineering, and practical deployment, collaborating with leading labs, enterprises, and government entities to explore the vast potential of Generative AI. As AI technology evolves from static models to dynamic, intelligent systems, Scale AI is dedicated to establishing the essential research foundations, evaluation methodologies, and reinforcement learning infrastructure that will shape this transformative era. Join our high-impact research organization, where you will contribute to advancing large language models, post-training evaluation, and agent-based reinforcement learning environments, influencing the future of AI development and implementation. As the Research Scientist Manager, you will spearhead a distinguished team of research scientists and engineers, define the strategic research roadmap, and oversee projects from initial prototyping to final deployment. You will excel in a fast-paced environment, harmonizing deep technical leadership with effective people management, visionary goal setting, and successful delivery.

About Liquid AIFounded as a spin-off from MIT CSAIL, Liquid AI specializes in creating versatile AI systems designed for optimal performance across various deployment platforms, including data center accelerators and on-device hardware. Our technology emphasizes low latency, minimal memory consumption, privacy, and dependability. We collaborate with leading enterprises in sectors such as consumer electronics, automotive, life sciences, and financial services. As we experience rapid growth, we are on the lookout for exceptional talent to join our team.The OpportunityThe Data team at Liquid AI drives the development of our Liquid Foundation Models, focusing on pre-training, vision, audio, and emerging modalities. With the stagnation of public data sources, the effectiveness of our models increasingly relies on specially curated datasets. We are seeking engineers with a machine learning mindset who can efficiently gather, filter, and synthesize high-quality data at scale.At Liquid AI, we regard data as a research challenge rather than an infrastructural issue. Our engineers conduct experiments, design ablations, and assess how data-related decisions impact model quality. We will align you with a team where you can experience rapid growth and make a significant impact, be it in pre-training, post-training reinforcement learning, vision-language, audio, or multimodal applications.While we prefer candidates in San Francisco and Boston, we are open to considering other locations.What We're Looking ForWe are in search of a candidate who:Thinks like a researcher and executes like an engineer: You should be able to formulate hypotheses, conduct experiments, and evaluate results. Our engineers produce research-level code while our researchers implement production systems.Learns quickly and adapts: You will be working in rapidly evolving modalities, so the ability to quickly grasp new domains and thrive in ambiguity is essential.Prioritizes data quality: We hold data quality in high regard; tasks such as filtering, deduplication, augmentation, and evaluation are key responsibilities, not afterthoughts.Solves problems autonomously: Data engineers operate within training groups (pre-training and multimodal). While collaboration is crucial, we expect ownership and self-direction.The WorkDevelop and maintain data processing, filtering, and selection pipelines at scale.Establish pipelines for pretraining, midtraining, supervised fine-tuning, and preference optimization datasets.Design synthetic data generation systems utilizing large language models (LLMs), structured prompting, and domain-specific generative techniques.

Join Hilberts as a Machine Learning Engineer / Data Scientist in our San Francisco office, where you will leverage cutting-edge technology to drive enterprise-level solutions. You will work collaboratively with cross-functional teams to design, develop, and implement machine learning models that enhance our data-driven decision-making processes.

Join Us as a Founding Data Scientist and Machine Learning EngineerAmplify Your ImpactYou have achieved remarkable milestones in your career—delivering impactful models, influencing key metrics, and showcasing the transformative potential of data science and machine learning. You have positively affected products that touch millions of lives.Now, envision the possibility of enhancing the entire app ecosystem by extending your influence across numerous products and companies, making every app in users’ pockets smarter, more engaging, and indispensable.Your expertise can empower product teams to innovate faster, captivate users, and drive revenue growth, thanks to the intelligence you develop once and deploy universally.We share this ambition; we have successfully achieved it multiple times at leading organizations like Uber, Apple, Meta, Google, and Chime. Our contributions have generated tens of billions of dollars in impacts for essential products relied on by billions, and we are poised to elevate our influence further.If this resonates with the journey you seek, we invite you to continue reading.Our MissionDashboards recount the past; teams require insights for their next move. Palladio AI serves as the intelligence layer between raw data and decisive action, illuminating product opportunities that translate into genuine growth levers and guiding actions so product teams can iterate with confidence and speed rather than wade through noise.Your RoleYou will be part of a team crafting foundational systems in behavioral modeling, causal inference, forecasting, agentic platforms, and beyond. Your contributions will extend these domains: developing ML and AI models to identify and highlight product opportunities, deploying learning loops that enhance with each release. In essence, you will convert fundamental data science principles into a scalable product across various industries.Beyond technical challenges, you will create a platform that aids real people in making informed decisions, transforming data into clarity and clarity into actionable progress.Your ProfilePassion for Craft and Excellence. You dive into complex datasets, prototype swiftly, and refine until insights shine.Impact-Driven Mindset. 6+ years of experience in production ML/DS; you harmonize scientific rigor with a practical approach—“it ships today, iteration follows.”

Become a Pioneer in Sleep FitnessAt Eight Sleep, we're dedicated to unlocking human potential through optimal sleep. As the world's first sleep fitness company, we are revolutionizing what it means to be well-rested by creating the most advanced hardware, software, and AI technology. Our innovative products enhance mental, physical, and emotional performance by transforming each night into a personalized, data-driven recovery journey. Trusted by high achievers, professional athletes, and health-conscious individuals across over 30 countries, we have been recognized by Fast Company as one of the Most Innovative Companies in 2019, 2022, and 2023, and honored twice by TIME's “Best Inventions of the Year.” Our high-performance team operates with speed, focus, and a commitment to impact. We don't just create; we refine and obsess over every detail to ensure our members sleep better and wake up stronger.Every position at Eight Sleep offers the opportunity to innovate cutting-edge technology, collaborate with exceptional talent, and contribute to a future where sleep is a powerful tool for well-being. If you're ready to break away from the ordinary and eager to build at the forefront of possibility, this is your chance to join us in reshaping how the world sleeps and what we can achieve upon waking.Our Culture: High Standards, No CompromiseOur mission demands intensity, and at Eight Sleep, we embody the mindset of the world's top performers: focused, relentless, and committed to being in the top 1% of our field. Inspired by the relentless drive of legends like Kobe Bryant, we apply that mentality to bold ideas, next-gen technology, and impeccable execution. This is not a standard 9-to-5 role; our team is dedicated, often working 60+ hours per week—not out of obligation, but out of passion. If you thrive under pressure and seek to do the most meaningful work of your career, you'll find a home here. If you prefer an easier path, this position is not for you.Your RoleAs a Machine Learning Research Scientist at Eight Sleep, you will be at the cutting edge of sleep innovation. Your mission will be to leverage innovative technology, minimalistic design, and proven clinical science to personalize and enhance sleep experiences, fundamentally changing how people sleep for the better.Our revolutionary temperature-regulated technology, the Pod, has been recognized as a game changer, enhancing health and happiness by transforming sleep. Join us in making sleep count for more.

About the TeamJoin the innovative Post-Training team at OpenAI, where we focus on refining and elevating pre-trained models for deployment in ChatGPT, our API, and future products. Collaborating closely with various research and product teams, we conduct crucial research that prepares our models for real-world deployment to millions of users, ensuring they are safe, efficient, and reliable.About the RoleAs a Research Engineer / Scientist, you will spearhead the research and development of enhancements to our models. Our work intersects reinforcement learning and product development, aiming to create cutting-edge solutions.We seek passionate individuals with robust machine learning engineering skills and research experience, particularly with innovative and powerful models. The ideal candidate will be driven by a commitment to product-oriented research.This position is located in San Francisco, CA, and follows a hybrid work model requiring three days in the office each week. Relocation assistance is available for new employees.In this role, you will:Lead and execute a research agenda aimed at enhancing model capabilities and performance.Work collaboratively with research and product teams to empower customers to optimize their models.Develop robust evaluation frameworks to monitor and assess modeling advancements.Design, implement, test, and debug code across our research stack.You may excel in this role if you:Possess a deep understanding of machine learning and its applications.Have experience with relevant models and methodologies for evaluating model improvements.Are adept at navigating large ML codebases for debugging purposes.Thrive in a fast-paced and technically intricate environment.About OpenAIOpenAI is a pioneering AI research and deployment organization dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We are committed to pushing the boundaries of AI capabilities while prioritizing safety and human-centric values in our products. Our mission is to embrace diverse perspectives, voices, and experiences that represent the full spectrum of humanity, as we strive for a future where AI is a powerful ally for everyone.

Join Hilbert, a pioneering data science-driven growth engine that empowers B2C teams with predictive insights into user behaviors, revenue drivers, and sustainable growth strategies. Our innovative approach compresses lengthy decision-making processes into mere minutes.Trusted by Fortune 10 enterprises and beloved brands like FreshDirect, Blank Street, and Levain Bakery, Hilbert is the backbone of their growth strategies. We are also collaborating with leading AI companies to push the boundaries of what’s possible.We are seeking a talented Data Scientist who possesses a deep understanding of B2C business challenges, develops actionable models using real-world data, and delivers impactful analyses that facilitate significant growth outcomes — all with the initiative and urgency typical of a founder.This is not a role where you simply receive tasks; you will take ownership of problems from start to finish — from problem framing and modeling to measuring impact — for enterprise clients where the stakes are high and feedback is rapid. If you understand the nuances of churn analysis for different sectors, can create effective recommendation systems from sparse data, and can clearly communicate your causal analysis to clients, we want to meet you.ROLE OVERVIEWYou will closely collaborate with the founding team, engineering, product, and go-to-market teams to enhance the data science systems integral to Hilbert. Daily responsibilities include building models, conducting experiments, analyzing data, and producing analyses that influence key decisions. Our focus is B2C, and the challenges we tackle — such as demand forecasting, customer lifecycle management, personalization, and activation — require an individual who can translate business contexts into effective modeling choices. You will thrive in a high-autonomy, high-ambiguity environment where data is often messy, incomplete, or scarce.Key Responsibilities:Develop ML models that enhance core product features: recommendation systems, search relevance, customer segmentation, demand forecasting, and activation optimization.Contribute to configurable, multi-tenant model architectures that adapt to various customer contexts and business needs, avoiding the need for custom solutions for each case.Build effective models using available data — leveraging limited, noisy, or sparse datasets while determining the appropriate level of complexity.Design and implement rigorous A/B tests and recognize when causal inference methods are necessary.

About FaireFaire is a dynamic online wholesale marketplace dedicated to empowering local businesses. We believe in a future where independent retailers can thrive and compete against retail giants like Walmart and Amazon. By harnessing technology, data, and machine learning, we connect a vibrant community of entrepreneurs globally. Imagine helping your favorite local boutique source outstanding products from around the world to enhance their offerings. Our mission is to provide the tools and insights that enable small businesses to succeed in a competitive landscape.By championing the growth of independent businesses, Faire fosters positive economic impacts within communities worldwide. We are on the lookout for intelligent, resourceful, and passionate individuals to join our team as we drive the shop local movement. If you share our commitment to community, we invite you to be part of ours.About this roleAt Faire, we leverage advanced machine learning and data insights to transform the wholesale industry, enabling local retailers to stand strong against larger competitors. The Data Science team is pivotal in developing and sustaining a variety of algorithms and models that enhance our marketplace. We focus on creating innovative machine learning models that empower our customers to succeed.As a member of the Brand Data Science team focused on Listing Quality, your role will involve enhancing the quality of product listings, enabling retailers to effectively discover and assess products on Faire. You will apply ML and AI to address key challenges, including improving image and text quality, extracting structured product attributes, and accurately identifying duplicates and product variants. Utilizing deep learning, multi-modal LLMs, and a human-in-the-loop approach, you'll deliver high-performance solutions. In this rapidly evolving domain, you will be at the forefront of applying cutting-edge technology to drive tangible outcomes. You will independently design and implement solutions while collaborating with the cross-functional Listing Quality pod, which includes product, design, engineering, analytics, and operations, to tackle challenges comprehensively.

At Exa, we are revolutionizing the way AI applications access information by building a cutting-edge search engine from the ground up. Our team is dedicated to developing a robust infrastructure capable of crawling the web, training advanced embedding models, and creating high-performance vector databases using Rust to facilitate seamless searches.As part of our ML team, you'll be instrumental in training foundational models that refine search capabilities. Our mission? To deliver precise answers to even the most complex queries, effectively transforming the web into an incredibly powerful knowledge database.We are seeking a talented Machine Learning Research Engineer who is passionate about crafting embedding models that enhance web search efficiency. Your responsibilities will include innovating novel transformer-based architectures, curating extensive datasets, conducting evaluations, and continuously improving our state-of-the-art models.

Innovating Self-Improving SuperintelligenceAt Letta, we are on a transformative journey to enhance artificial intelligence to be as adaptive and capable of learning as the human brain. Our mission is to create self-improving AI agents that continuously learn from their experiences and evolve over time.Founded by the visionary team behind MemGPT at UC Berkeley’s Sky Computing Lab—the birthplace of Spark and Ray—we are supported by industry leaders like Jeff Dean and Clem Delangue. Our cutting-edge agents are already in action at leading companies such as 11x and Bilt Rewards, improving their operations daily.Join our elite team of researchers and engineers as we tackle one of AI's greatest challenges: developing machines that can think, remember, and learn in ways similar to humans.This position is in-person (no hybrid), requiring presence 5 days a week at our downtown San Francisco office.

At Scale AI, we are the premier partner for data and evaluation in the rapidly evolving field of artificial intelligence. Our commitment to advancing the assessment and benchmarking of large language models (LLMs) positions us at the forefront of AI innovation. We are dedicated to creating leading-edge LLM evaluation methodologies that set new benchmarks for model performance. Our research teams collaborate with the top AI laboratories in the industry to provide high-quality data, accelerate progress in generative AI research, and inform what excellence looks like in this domain. As a Staff Machine Learning Research Scientist on our LLM Evals team, you will spearhead the creation of novel evaluation methodologies, metrics, and benchmarks to assess the strengths and weaknesses of cutting-edge LLMs. Your work will shape our internal strategies and influence the broader AI research community, making this role essential for establishing best practices in data-driven AI development.