Machine Learning Intern Co Op Jobs

About Appier Appier is an AI-native Agentic AI as a Service (AaaS) company that uses artificial intelligence (AI) to power business decision-making. Founded in 2012 with a vision of democratizing AI, Appier’s mission is turning AI into ROI by making software intelligent. Appier now has 17 offices across APAC, Europe and U.S., and is listed on the Tokyo Stock Exchange (Ticker number: 4180). Visit www.appier.com for more information. Open to overseas candidates/Visa Support This position is based in Tokyo, Japan. For international candidates, Appier's Japan office provides visa sponsorship to ensure a smooth transition to Japan. The Impact You’ll Make at Appier Appier is seeking a Senior Machine Learning Scientist to join our Advertising Cloud Optimization team , which leads the development of core machine learning algorithms driving campaign efficiency and advertiser ROI. Our programmatic advertising platform operates at a massive scale, handling over multi millions queries per second (QPS), all powered by our proprietary deep learning models for bidding, pricing, and personalized content delivery. In this role, you’ll directly impact the efficiency and profitability of ads campaigns by improving models for bidding, pricing , and personalized content recommendation , while ensuring system robustness and scalability in a dynamic market environment. What You’ll Work On Design, implement, and productionize state-of-the-art ML models to improve campaign outcomes. Analyze large-scale user and auction data to discover predictive patterns and alpha signals that enhance bidding and personalization. Collaborate cross-functionally with engineering, product, and data teams to identify opportunities, define roadmaps, and deliver impactful solutions. Continuously improve system performance through offline experimentation and online testing (e.g., A/B tests, incremental learning). What We’re Looking For Bachelor’s degree in Computer Science, Mathem

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . Who We Are: The Machine Learning Platform team at Reddit is a high-impact team that owns the infrastructure that powers recommendations, content discovery, user and content quantification, while directly impacting other teams such as Growth, Ads, Feeds, and Core Machine Learning teams. What You’ll Do: As a Senior Staff Software Engineer, you will help define and lead the vision for Reddit’s large-scale GenAI Platform, shaping the strategy, architecture, and operating model that enable teams across the company to build, deploy, and scale generative AI products with confidence. Contribute to the design, implementation, and maintenance of the LLM Gateway, focusing on features like unified API endpoints for internal/externally hosted LLM, rate/token limit management, and intelligent failover mechanisms to boost uptime and reliability. Lead and execute the vision, strategy, and roadmap for Reddit’s large-scale GenAI Platform. Define the platform architecture and operating model that enable teams to build, deploy, and scale GenAI products reliably. Drive the strategy for a unified LAG Gateway supporting internally and externally hosted LLMs through consistent APIs and abstractions. Set the direction for core platform capabilities such as rate and token limit management, intelligent failover, and production resilience. Shape Reddit’s approach to an enterprise-grade RAG system Establish the strategic direction for agentic AI workflows and tool-use patterns across the platform. Own the end-to-end platform strategy from concept t

Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with leading enterprises and government organizations to accelerate their AI initiatives through our data annotation platform, generative AI solutions, and enterprise AI capabilities. About the General Agents Team The General Agents team, part of Scale’s Enterprise organization, builds robust general agents for customer use cases and applications. The team sits at the intersection of frontier agent development and real-world deployment, translating state-of-the-art reasoning and agentic capabilities into reliable, production-grade systems that drive real economic value. Our agents are scalable systems built around recurring enterprise problem domains, with a strong emphasis on generalization, extensibility, and deployment across many customers. About the Role As a Senior/Staff Machine Learning Engineer (MLE) on the General Agents team, you’ll play a critical role in designing, building, and deploying production-ready AI agents that solve high-impact enterprise problems. You will work across the full agent lifecycle—from model and system design to evaluation, deployment, and iteration—bridging cutting-edge agentic techniques with the constraints and requirements of real customer environments. You will: Design and implement end-to-end agent systems that combine LLM reasoning, tool use, memory, and control logic to solve recurring enterprise use cases. Build scalable, reliable agent architectures that can be deployed across many customers with varying data, tools, and constraints. Develop evaluation frameworks, datasets, environments, and metrics to measure agent performance, reliability, and business impact in production settings. Collaborate closely with product managers, customers, data annotators, and other engineering teams to translate enterprise requirements into robust agent designs. Productionize frontier agent techniques (e.g.,

This role in the Offline Driving Intelligence team is responsible for developing/learning behavior models for road users such as cars, bicycles, and pedestrians. These agents populate Zoox's simulations and must be indistinguishable from real road users, yet fully controllable: promptable into the rare, adversarial, safety-critical behaviors we need to test against. This means the team’s models directly impact how fast Zoox can train, validate and ship its driving stack. Our team collaborates closely with Planner, Simulation and Validation teams to develop and validate our driving performance. As an ML Agents Machine Learning Engineer, you will work on the bleeding edge of the industry, developing novel machine learning pipelines and models to predict the behavior of other agents in the world and planning the best course of action for the ego vehicle. In this role, you will... Develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for human-like driving agents. Work on novel techniques to estimate the quality of those driving plans along the dimensions of safety, progress, comfort and realism. Build generative behavior models (e.g. autoregressive, diffusion) that are conditionable on scenario intent — "cut off the ego vehicle," "jaywalk here" — for targeted stress-testing. Leverage our compute, infrastructure and large corpus of data to push boundaries of the field. Develop metrics and tools to analyze errors and understand improvements of our systems. Collaborate with engineers on Perception, Planning, Simulation, and Validation to solve the overall Autonomous Driving problem. Qualifications PhD degree in computer science or related field and 4+ years of relevant professional experience or master's degree and 7+ years of relevant professional experience

The Autonomy Behaviors team is responsible for producing our vehicle’s driving trajectory. As a Principal ML Engineer, you will lead the technical development of machine learning algorithms for our next-generation ML Planner. Your impact will range from delivering innovations for immediate milestones to pioneering exploratory projects. You will collaborate closely with teams specializing in Foundation Models, Perception, Simulation, and Safety Validation, in influencing our autonomy stack. Your role will look at problems in a way that crosses team boundaries to prototype new approaches that influence the long term technical direction of multiple organizations within the company. In this role, you will: Set the technical direction of ML development for the future of our Planner Push the frontiers of imitation learning, reinforcement learning, and model scaling to develop new ML models for our onboard Planner Identify and drive force-multipliers for ML development, including metrics, data, and training frameworks Develop new methods to leverage Zoox’s Foundation Models into the ML Planner Provide technical mentorship to the broader group of ML developers at Zoox Collaborate with engineers across Autonomy Software, Simulation, System Design and Mission Assurance, and other departments to solve the overall Autonomous Driving problem in complex urban environments Qualifications BS, MS, or PhD degree in computer science or related field Proven track record of technical leadership in large-scale AI organizations Significant experience with training and deploying Deep Learning models to production Fluency in C++ or Fluency in Python with a basic understanding of C++ Extensive experience with programming and algorithm design-Strong mathematics skills, 10+ years of experience Bonus Qualifications Conference or Journal publications in Machine Learning or Robotics related v

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. Recommendation Systems are a key growth lever at Roblox, driving retention, engagement, and monetization for hundreds of millions of users. This role offers the unique opportunity to redefine how users search and discover everything from the most interesting immersive experiences and digital avatars in our Marketplace to personalized advertising. You will solve a diverse range of high-scale ranking, retrieval, and personalization problems across our platform. We combine cutting-edge research —including deep learning, generative AI, and reinforcement learning techniques— with large-scale engineering to bridge experimentation and production; you'll design algorithms that operate at massive scale and shape the next generation of recommender systems for user-generated content. Teams Hiring for This Role Search and Discovery: powers major recommendation surfaces—conducting cutting-edge research in generative modeling, multimodal and MLLM technologies, designing advanced agentic AI algorithms to solve business requirements while achieving technical breakthroughs. Safety, Alt Defense: architects a massive-scale detection engine that identifies recidivist bad actors across billions of account

JOB RESPONSIBILITIES: A highly collaborative, fast-growing team at Internal Alpha Capture (IAC), Point72 is developing AI-driven equity trading signals that leverage rigorous research, state-of-the-art machine learning methods, proprietary data sources, and unparalleled computing power. We are looking for exceptional machine learning researchers to join our efforts. Researchers will work closely with our experienced team members and apply the full breadth of their machine learning knowledge to unique, proprietary datasets, and develop novel trading signals that have high impact. Prior experience in the financial industry is not required. Key responsibilities may include: Managing all aspects of the research process, including ideation, method selection, implementation, evaluation, and eventual application. Identifying, adapting, and extending existing models in the broad field of machine learning; conducting novel research as needed, to develop new signals that can enhance portfolio returns, or predict other variables of interests. Staying up to date on the advances in AI/ML and related technological innovations to provide recommendations on new models and tools and identify emerging opportunities. DESIRABLE CANDIDATES: Master’s or PhD in machine learning, computer science, statistics, or related fields. Knowledge and experience in any of the following areas are strongly preferred: modern sequence models, graph neutral nets, reinforcement learning, LLMs. Prior research experience utilizing machine learning over large, possibly noisy, data sets. Strong analytical and quantitative skills, and a detail-oriented mindset. Strong proficiency in machine learning libraries such as Torch, JAX or TensorFlow. Competence in Python, cluster environment, and general software engineering principles (source control, testing, collaborative workflow). Excellent written and verbal communication skills, willing to proactively engage other team members in helping to foster a highly coll

Figma is growing our team of passionate creatives and builders on a mission to make design accessible to all. Figma’s platform helps teams bring ideas to life—whether you're brainstorming, creating a prototype, translating designs into code, or iterating with AI. From idea to product, Figma empowers teams to streamline workflows, move faster, and work together in real time from anywhere in the world. If you're excited to shape the future of design and collaboration, join us! Figma is seeking a versatile and experienced Machine Learning / AI Engineer to join our growing AI team, working at the intersection of applied machine learning, infrastructure, and product innovation. Whether you’re building intelligent search systems, crafting scalable data pipelines, or enhancing AI-powered creativity tools, your work will drive user productivity, shape new product experiences, and advance the state of AI at Figma. You’ll collaborate closely with engineers, researchers, designers, and product managers across multiple teams to deliver high-quality ML-driven features and infrastructure. This is a high-impact, cross-functional role where you’ll shape both foundational systems and user-facing capabilities. This is a full time role that can be held from one of our US hubs or remotely in the United States. What you’ll do at Figma: Design, build, and productionize ML models for Search, Discovery, Ranking, Retrieval-Augmented Generation (RAG), and generative AI features. Build and maintain scalable data pipelines to collect high-quality training and evaluation datasets, including annotation systems and human-in-the-loop workflows. Collaborate with AI researchers to iterate on datasets, evaluation metrics, and model architectures to improve quality and relevance. Work with product engineers to define and deliver impactful AI features across Figma’s platform. Partner with infrastructure engineers to develop and optimize systems for training, inference, monitoring, and deployment. Explo

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 . Within Pinterest, the Pinterest Labs organization focuses on applied ML research and development. Labs focuses on a broad variety of AI/ML initiatives, such as core computer vision, multimodal representation learning, heterogeneous graph neural networks, generative modeling, recommender systems, etc. This is the group that develops foundation ML models that fully leverage the tens of billions of Pins and the associated knowledge graph to improve the core product. We are currently hiring for the visual modeling team in Labs, which focuses on developing Pinterest Canvas. Canvas is a foundation text-to-image model developed internally for helping various visualization, inpainting, and outpainting products. In this role, you’ll get to work with Pinterest’s rich visual-text dataset to build large-scale generative models which are continuously being s

About Appier Appier is an AI-native Agentic AI as a Service (AaaS) company that uses artificial intelligence (AI) to power business decision-making. Founded in 2012 with a vision of democratizing AI, Appier’s mission is turning AI into ROI by making software intelligent. Appier now has 17 offices across APAC, Europe and U.S., and is listed on the Tokyo Stock Exchange (Ticker number: 4180). Visit www.appier.com for more information. The Impact You’ll Make at Appier Appier is seeking a Senior Machine Learning Scientist to join our Advertising Cloud Optimization team , which leads the development of core machine learning algorithms driving campaign efficiency and advertiser ROI. Our programmatic advertising platform operates at a massive scale, handling over multi millions queries per second (QPS), all powered by our proprietary deep learning models for bidding, pricing, and personalized content delivery. In this role, you’ll directly impact the efficiency and profitability of ads campaigns by improving models for bidding, pricing , and personalized content recommendation , while ensuring system robustness and scalability in a dynamic market environment. What You’ll Work On Design, implement, and productionize state-of-the-art ML models to improve campaign outcomes. Analyze large-scale user and auction data to discover predictive patterns and alpha signals that enhance bidding and personalization. Collaborate cross-functionally with engineering, product, and data teams to identify opportunities, define roadmaps, and deliver impactful solutions. Continuously improve system performance through offline experimentation and online testing (e.g., A/B tests, incremental learning). What We’re Looking For Bachelor’s degree in Computer Science, Mathematics, EE, or related field; Master’s or PhD preferred. 4+ years of industry experience in ad tech , with a focus on performance optimization. Proven experience in applied machine learning , especial

P-1408 The Applied AI team at Databricks sits at the forefront of advancing AI/ML-powered products . Databricks’ customers are continuously creating new assets (tables, notebooks, dashboards, datarooms, pipelines, sql queries, ml models etc.) on the platform. Some of them can have hundreds of millions of assets. Finding an asset is a critical user journey for Databricks’ customers which helps them accomplish their tasks. As our Search product continues to evolve, we are seeking a Staff Engineer to lead enhancements to our Search Quality. In 2025, we will focus on enhancing search ranking, improving query understanding, building robust evals and growing the coverage of assets to enable seamless search at scale. Key Responsibilities Drive the development and deployment of ML based search and discovery relevance models and systems integrated with Databricks' products and services. Design and implement automated ML and NLP pipelines for data preprocessing, query understanding and rewrite, ranking and retrieval, and model evaluation, enabling rapid experimentation and iteration. Collaborate with product managers and cross-functional teams to drive technology-first initiatives that enable novel business strategies and product roadmaps for the search and discovery experience. Contribute to building a robust framework for evaluating search ranking improvements - both offline and online. What We’re Looking For BS+ (M.S. or PhD preferred) in Computer Science, or a related field. 10+ years experience developing search relevance systems at scale in production or in high-impact research environments. Experience applying LLM to search relevance Experience in one or more of the following: Query understanding NLP Text mining Recommendations Personalization Discovery Conversational AI Strong understanding of computer science fundamentals. Contributions to well-used open-source projects. Why Join Us? At Databricks,

Our Perception team is responsible for using our sensor data to understand the complex and dynamic environments where we drive. In this role, you will have access to the best sensor data in the world and an incredible infrastructure for testing and validating your algorithms. We are creating new algorithms for segmentation, tracking, classification, and high-level scene understanding, and you could work on any (or all!) of these components. As a Senior Staff ML Engineer, you will lead the development of machine learning algorithms that can range in influence from onboard autonomy to offboard autonomy and validation. You will collaborate closely with other teams specializing in Prediction, Planning, Simulation, and Safety Validation, influencing our overall technical stack. Your role will look at problems in a way that crosses team boundaries to prototype new approaches that influence the long term technical direction of multiple organizations within the company. The impact of the role can be in the form of impacting immediate company milestones to leading forward-looking exploratory projects. Responsibilities Develop new algorithms to understand the scene around the robot, and how that scene would evolve through time Build multi-modal foundation models for on-vehicle and offline applications Develop new algorithms to apply generative AI to simulation to improve the realism of our offline validation systems Leverage our large-scale machine learning infrastructure to discover new solutions and push the boundaries of the field Provide technical mentorship to the broader group of ML developers at Zoox Collaborate with engineers on Prediction, Planning, and Simulation to solve the overall Autonomous Driving problem in complex urban environments Qualifications BS, MS, or PhD degree in computer science or related field Experience with training and deploying Deep Learning models on sensor data-Experience

The Prediction & Behavior ML team is responsible for developing machine learning (ML) algorithms that learn and predict behaviors from data, applying them both on-vehicle to influence driving behavior and off-vehicle to provide ML capabilities to simulation and validation. Given the tight integration of behavior forecasting and motion planning, our team collaborates closely with the Planner team to advance overall vehicle behavior. We also work closely with our Perception, Simulation, and Systems Engineering teams to accelerate our ability to validate our driving performance. As a Learned Trajectory Machine Learning Engineer you will be responsible for developing deep learned models that produce trajectories for our vehicles to drive. Given the tight integration of behavior prediction and motion planning, you will closely collaborate with the Planner and Perception teams in the advancement of our overall vehicle behavior. In this role, you will: You will develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for our autonomous vehicle. You will also work on techniques to estimate the quality of those driving plans along the dimensions of safety, progress, comfort etc. You will leverage our large-scale machine learning infrastructure to discover new solutions and push the boundaries of the field You will develop metrics and tools to analyze errors and understand improvements of our systems You will collaborate with engineers on Perception, Planning, and Simulation to solve the overall Autonomous Driving problem in complex urban environments Qualifications BS, MS, or PhD degree in computer science or related field Experience with training and deploying transformer-based model architectures and reinforcement learning Experience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelines Fluency in C++

About Scale At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI), and building upon our prior model evaluation work with enterprise customers and governments, to deepen our capabilities and offerings for both public and private evaluations. About the ACE team The Agent Capabilities & Environments (ACE) team, part of Scale’s Research organization, brings together customer-facing Researchers and Applied AI Engineers. Our core mission includes research on agent environments and RL reward signals, benchmarking autonomous agent performance across real-world scenarios and environments, creating robust data programs to improve Large Language Models (LLMs) agentic capabilities and building foundational tools and frameworks for evaluating models as agents. ACE focuses on autonomous agents that dynamically interact with diverse external environments, including code repositories, GUI interfaces, browsers, and more. About This Role This role is at the intersection of cutting-edge AI research and practical application, with a focus on studying the data types essential for building state-of-the-art agents, such as browser and SWE agents. The ideal candidate will explore the data landscape needed to advance intelligent, adaptable AI agents, guiding the data strategy at Scale to drive innovation. This position requires not only expertise in LLM agents and planning algorithms but also creativity in addressing novel challenges related to data, interaction, and evaluation. You will contribute to impactful research publications on agents, collaborate with customer researchers, and work alongside the engineering team to translate t

About Cubist Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources. Role/Responsibilities: We are seeking a quantitative researcher for the Cubist Machine Learning Research group with experience in machine learning, especially recent deep learning and natural language processing technology. Researchers will use a rigorous scientific method to develop sophisticated trading models and shape our insights into how the markets will behave. Successful researchers manage all aspects of the research process including data ingestion and processing, data analysis, methodology selection, implementation and testing, prototyping, and performance evaluation. Researchers will be introduced to industry standard datasets, including understanding which data may be relevant to a certain model or financial problem; how to collect, parse, and clean the data; how to incorporate the data into innovative functional models; how to construct and develop features from raw data; and how to estimate effectiveness of such features. Researchers will also be provided with the opportunity to implement the full breadth of their knowledge and training to actively participate in all stages of research & development of financial models through use of machine learning. Based on experience from working with existing industry-standard models and algorithms, researchers will learn how to construct their own models in order to solve complex financial problems and enhance data prediction capabilities within the financial services industry. Requirements: PhD or PhD candidate in machine learning, computer science, statistics, or a related field Experience with sequential modeling and time