Machine Learning Research Engineer - Agent Data Foundation | Scale AI

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.

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 increasingly becoming a pivotal element across all sectors of society. At Scale AI, we are committed to accelerating the evolution of AI applications. For nearly a decade, we have been the premier AI data foundry, propelling groundbreaking advancements in areas such as generative AI, defense applications, and autonomous vehicles. Following our recent investment from Meta, we are intensifying our efforts to develop advanced post-training algorithms that are essential for sophisticated agents in enterprises worldwide.The Enterprise ML Research Lab is at the forefront of this AI revolution, leveraging a suite of proprietary research, tools, and resources to support our enterprise clients. As a Staff Machine Learning Research Engineer focusing on Agent Post-training, you will be instrumental in creating our next-generation Agent Reinforcement Learning training platform. Your work will enable the training of top-tier Agents that deliver state-of-the-art results in real-world enterprise applications.You will incorporate cutting-edge research into our training framework, empowering ML Research Engineers on the Enterprise AI team to deploy use cases ranging from next-generation AI cybersecurity firewalls to training foundational healthtech search models. If you are passionate about shaping the future of the GenAI movement, we welcome your application!

Artificial Intelligence (AI) is becoming increasingly crucial across all sectors of society. At Scale AI, our mission is to expedite the advancement of AI applications. With nine years of experience, we have established ourselves as the leading AI data foundry, facilitating groundbreaking developments in AI, including generative AI, defense applications, and autonomous vehicles. Following our recent investment from Meta, we are committed to enhancing our capabilities by developing cutting-edge post-training algorithms that are essential for optimizing complex agents in enterprises globally.The Enterprise ML Research Lab is at the forefront of this AI revolution. We are dedicated to crafting a suite of proprietary research tools and resources that cater to all of our enterprise clients. As a Machine Learning Research Engineer focusing on Agents, you will apply our Agent Reinforcement Learning (RL) training and building algorithms to real-world enterprise datasets across our clients and benchmarks. Your role will involve developing top-tier Agents that achieve state-of-the-art results through a blend of post-training and agent-building algorithms.If you are passionate about influencing the trajectory of the modern Generative AI movement, we would love to hear from you!

Join Scale AI's ML platform team (RLXF) as a Machine Learning Research Engineer, where you will play a pivotal role in developing our advanced distributed framework for training and inference of large language models. This platform is vital for enabling machine learning engineers, researchers, data scientists, and operators to conduct rapid and automated training, as well as evaluation of LLMs and data quality.At Scale, we occupy a unique position in the AI landscape, serving as an essential provider of training and evaluation data along with comprehensive solutions for the entire ML lifecycle. You will collaborate closely with Scale's ML teams and researchers to enhance the foundational platform that underpins our ML research and development initiatives. Your contributions will be crucial in optimizing the platform to support the next generation of LLM training, inference, and data curation.If you are passionate about driving the future of AI through groundbreaking innovations, we want to hear from you!

As AI continues to play a crucial role across various sectors, Scale AI is committed to accelerating the evolution of AI applications. For nearly a decade, we have been at the forefront of AI data solutions, driving significant innovations such as generative AI, defense technologies, and autonomous vehicles. With recent funding from Meta, we are intensifying our efforts to develop cutting-edge post-training algorithms essential for enhancing the performance of complex enterprise agents globally. The Enterprise ML Research Lab is at the forefront of this AI transformation. Our team is dedicated to creating a suite of proprietary research and resources tailored for our enterprise clientele. As a Machine Learning Systems Research Engineer, you will play a pivotal role in developing algorithms for our next-generation Agent Reinforcement Learning (RL) training platform, support large-scale training operations, and integrate state-of-the-art technologies to optimize our machine learning systems. You will collaborate with other ML Research Engineers and AI Architects on the Enterprise AI team to apply these training algorithms to various client use cases, from next-gen AI cybersecurity firewalls to foundational healthtech search models. If you are passionate about shaping the future of AI, we want to hear from you!

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.

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.

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.

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.

Superhuman embraces a hybrid working model designed to offer team members the ideal balance of focused work and collaborative, in-person interactions that cultivate trust, innovation, and a vibrant team culture.About SuperhumanSuperhuman, now inclusive of Grammarly, is an AI productivity platform dedicated to unleashing the superhuman potential within everyone. Our suite of applications and agents extends AI capabilities across 1 million+ applications and websites. Our products include Grammarly's writing assistance, Coda's collaborative workspaces, Mail's inbox management, and Go, a proactive AI assistant that intuitively understands context and provides automated support. Since our inception in 2009, Superhuman has empowered over 40 million individuals, 50,000 organizations, and 3,000 educational institutions globally to reduce busywork and concentrate on what truly matters. Discover more at superhuman.com and explore our core values here.The OpportunitiesJoin us in developing a groundbreaking platform for AI Agents, designed to collaboratively tackle complex tasks, utilizing Superhuman's intuitive UI. As a Machine Learning Engineer on this pioneering team, you will play a critical role in our company's transformation.Shape the Future of Productivity: Take on a vital role in evolving Grammarly from a cherished writing assistant into an indispensable AI-driven productivity suite for enterprises.Build an Innovative AI Agent Platform: Lead the charge in creating a new platform where multiple AI agents work together to address intricate user challenges. You will oversee the core orchestration, routing, and planning systems.Own Key ML Systems: Design and implement advanced machine learning models that enhance core product experiences, including search ranking and proactive suggestions that anticipate user needs.

Join Scale Labs as a Research Scientist — Agent RobustnessScale is the premier partner for data and evaluation within the forefront of AI innovation, playing a crucial role in understanding and safeguarding AI models and systems. Building on our extensive expertise, Scale Labs has initiated a dedicated team focused on policy research, aiming to connect AI research with global policymakers to facilitate informed, scientifically grounded decisions regarding AI risks and capabilities.Our research addresses complex challenges in agent robustness, AI control protocols, and AI risk evaluations, empowering governments, industries, and the public to comprehend and mitigate AI risks while promoting AI adoption. This team collaborates across various sectors, including industry, public services, and academia, and regularly disseminates our findings. We are actively inviting skilled researchers to contribute to this vision.As a Research Scientist specializing in Agent Robustness, you will tackle foundational challenges in creating AI agents that are both safe and aligned with human values. Your responsibilities may include:Investigating the science behind AI agent capabilities, focusing on safety, risk factors, and benchmarking methodologies.Designing and building testing harnesses to evaluate AI agents' tendencies to engage in harmful actions under user pressure or environmental manipulation.Creating exploits and mitigations for new failure modes that emerge as AI agents gain capabilities such as coding, web browsing, and computer usage.Characterizing and developing mitigations for potential failure modes or broader risks involving multiple interacting AI agents.

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.

Join Scale's innovative Large Language Model (LLM) post-training platform team, where you will contribute to the development of our internal distributed framework designed specifically for LLM training. This sophisticated platform empowers Machine Learning Engineers (MLEs), researchers, data scientists, and operators to perform rapid and automated training and evaluation of LLMs. Additionally, it underpins the training framework for our data quality evaluation pipeline.Scale is at the forefront of the Artificial Intelligence sector, acting as a vital provider of training and evaluation data, as well as comprehensive solutions for the entire machine learning lifecycle. In this role, you will collaborate closely with Scale’s ML teams and researchers to construct the foundational platform that supports all our ML research and development initiatives. Your work will involve building and optimizing this platform to facilitate the training, inference, and data curation of next-generation LLMs.If you are passionate about driving the future of AI through groundbreaking innovations, we invite you to connect with us!

At Superhuman, we embrace a dynamic hybrid working model, allowing team members to enjoy a balance of focused work time and collaborative in-person interactions that foster trust, innovation, and a vibrant team culture.About SuperhumanSuperhuman, now inclusive of Grammarly, is an innovative AI productivity platform dedicated to unlocking the superhuman potential in individuals. Our suite of applications and agents seamlessly integrates AI into the workflow, connecting with over a million applications and websites. Our products range from Grammarly’s writing assistance to Coda’s collaborative environments, Mail’s inbox management, and Go, a proactive AI assistant that understands context and provides assistance automatically. Since our inception in 2009, Superhuman has empowered over 40 million users across 50,000 organizations and 3,000 educational institutions globally, enabling them to eliminate busywork and concentrate on what truly matters. Discover more atsuperhuman.com and explore our values here.The OpportunitiesJoin our team as we develop a pioneering platform for AI Agents to collaboratively tackle complex tasks using Superhuman's intuitive UI. As a Machine Learning Engineer, you will be a key player in our company's transformation.Shape the Future of Productivity: Be instrumental in transitioning Grammarly from a beloved writing companion to a crucial, AI-driven productivity suite for enterprises.Build a Groundbreaking AI Agent Platform: Lead the creation of a new platform where AI agents work together to address intricate user challenges. You will manage the essential orchestration, routing, and planning systems.Own Critical ML Systems: Design and implement advanced ML models for fundamental product experiences, including search ranking and proactive suggestions that foresee user needs.Integrate Cutting-Edge AI: Work at the cutting edge of AI technology, developing ML components that utilize the latest models to craft extraordinary user experiences.Thrive in a High-Impact Environment: Join a foundational team where you will enjoy a high level of autonomy and product insight in a fast-paced, evolving atmosphere.

Join Prima MenteAt Prima Mente, we are pioneers in the field of biology-focused artificial intelligence. Our mission is to generate unique datasets, develop versatile biological foundation models, and translate scientific breakthroughs into real-world clinical applications. Our primary focus is on understanding the brain in-depth, safeguarding it from neurological disorders, and enhancing its function during health. Our dynamic team of AI researchers, experimentalists, clinicians, and operational experts are strategically located in London, San Francisco, and Dubai.Your Role: Foundation Models for BiologyAs a Machine Learning Engineer, you will be instrumental in the design, implementation, and scaling of foundational AI models and infrastructure for multi-omics at an unprecedented scale. Your contributions will facilitate significant advancements in scientific comprehension and lead to groundbreaking applications in the medical and biological fields.Key Responsibilities:Develop high-performance machine learning algorithms optimized for large-scale applications, ensuring utmost reliability and efficiency.Design, implement, and maintain comprehensive experimentation pipelines that allow for rapid iterations, precise assessments, and reproducible research results.Refactor and enhance prototype research code into clean, maintainable, and efficient repositories prepared for production-level deployments.Create fast data processing workflows that can effectively manage extensive datasets to expedite research and model development.Engage in experimental design, with a focus on high-impact experiments that yield the greatest signal-to-noise ratio.Growth ExpectationsIn 1 month, you will initiate initial experiments utilizing state-of-the-art machine learning models, review and apply advanced research papers, and enhance existing code for improved efficiency and precision.By 3 months, you will take ownership of a prototype model architecture, showcasing notable algorithmic enhancements, and contribute to methods for large-scale data ingestion and training.Within 6 months, you will have significantly impacted the implementation of a high-performance foundation model, incorporating key algorithmic optimizations that improve scalability and throughput, along with publishing internal benchmarks that demonstrate substantial effects.

Why Join Achira?Become part of an elite team comprising scientists, machine learning researchers, and engineers dedicated to transforming the predictability of the physical microcosm and revolutionizing drug discovery.Explore uncharted territories: we are on a mission to innovate next-generation model architectures that merge AI with chemistry.Engage in large-scale operations: harness massive computational resources, extensive datasets, and ambitious objectives.Take ownership of significant projects from inception to deployment on large-scale infrastructures.Thrive in a culture that values precision, speed, execution, and a proactive mindset.About the PositionAt Achira, we are committed to developing state-of-the-art foundation models that tackle the most complex challenges in simulation for drug discovery and beyond. Our atomistic foundation simulation models (FSMs) serve as world models of the physical microcosm, incorporating machine learning interaction potentials (MLIPs), neural network potentials (NNPs), and various generative models.We are seeking a Machine Learning Research Engineer (MLRE) who excels at the intersection of advanced machine learning and rigorous research methodologies. Collaborate closely with our research scientists to design and enhance intelligent training systems that propel us beyond contemporary architectures into a new era of ML-driven molecular modeling.Your mission is clear yet ambitious: to establish the foundational frameworks for training atomistic simulation models at scale. This entails a deep dive into architecture, data, optimizers, losses, training metrics, and representation learning, all while constructing high-performance systems that maximize the potential of our models. In this role, you will be instrumental in creating a blueprint for pretraining FSMs similar to today’s large-scale generative AI systems, making a significant impact on drug discovery.At Achira, you will have the chance to pioneer models that comprehend and simulate the physical world at an atomic level, achieving unprecedented speed and accuracy.

Company Overview:At Specter, we are pioneering a software-defined control plane for the physical realm, beginning with safeguarding American enterprises through comprehensive monitoring of their physical assets.Our innovative approach leverages a connected hardware-software ecosystem built on advanced multi-modal wireless mesh sensing technology. This breakthrough enables us to reduce the deployment costs and time for sensors by a factor of 10. Our ultimate goal is to establish a perception engine that provides real-time visibility of a company’s physical environment and facilitates autonomous operations management.Co-founders Xerxes and Philip are dedicated to empowering our partners in the rapidly evolving landscape of physical AI and robotics. Join our dynamic and rapidly expanding team comprised of talents from Anduril, Tesla, Uber, and the U.S. Special Forces.Position Overview:We are seeking a Perception AI Engineer who will be instrumental in transforming sensor data pipelines into actionable insights for our clients.Key Responsibilities:Implement and deploy a range of deep-learning models, including vision, vision-language, and large language models, within our sophisticated distributed perception system.Design and scale a production-ready data collection, labeling, and model retraining platform.Lead the design of a multimodal software user interface.

About UsAt XOXO AI, we are at the forefront of innovation, crafting intelligent interfaces that seamlessly integrate into everyday life. As a dynamic research lab comprised of dedicated engineers, designers, and researchers, we tackle unique challenges that extend beyond the workplace.Having achieved significant breakthroughs in infrastructure, architecture, and model layers, we are looking for passionate builders to help us realize our vision through the development of robust interface and application layers.About the RoleWe seek a talented Data/Machine Learning Engineer to establish our data infrastructure and production-ready ML systems, ensuring our product is responsive, dependable, and intelligent. This full-cycle role involves designing high-throughput pipelines, defining resilient data models, and deploying low-latency feature and model serving that can withstand real-world demands.You will collaborate closely with our founders and the early engineering team to transition prototypes into production, transforming complex real-world signals into reliable datasets and real-time functionalities that enhance core product experiences.What You’ll DoDevelop and manage high-throughput batch and streaming pipelines for analytics, training, and product signals.Lead real-time feature pipelines and online feature serving for low-latency inference.Design and oversee dimensional data models, skillfully managing schema evolution to avoid disrupting downstream consumers.Optimize model serving infrastructure to meet stringent latency and reliability service level objectives (SLOs).Establish and enforce event schemas, telemetry standards, and data contracts across multiple teams.Collaborate with engineering, product, and research teams to translate ambiguous product requirements into measurable, sustainable systems.