Machine Learning Fellow Jobs

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 . We’re looking for a Senior Staff Machine Learning Engineer to lead Reddit’s next-generation user understanding initiative: building a unified, high-fidelity representation of each user that powers personalization across the platform. This role requires deep expertise in mainstream ML user modeling approaches (e.g., large-scale embeddings, user interest modeling, affinities, behavioral signals) and the ability to reimagine these systems in the GenAI era—leveraging LLMs and foundation models to unlock step-change improvements in fidelity, adaptability, and expressiveness. You will set the technical direction for this space, leading the design and implementation of Reddit’s core user representation layer—spanning embeddings, interest modeling, and key user attributes. You’ll ensure this foundation is scalable, reliable, and widely adopted across Feeds, Search, Notifications, and Ads, partnering closely with product, infrastructure, and downstream ML teams to drive measurable impact. This is a high-impact role. The systems you build will shape how hundreds of millions of people experience Reddit every day—what they see, what they discover, and the communities they connect with. Your work will directly advance personalization and relevance at global scale, strengthening Reddit as a platform for meaningful connection and belonging. What you'll do: Design User Understanding Strategy: Define a unified user understanding framework and strategy: how users are represented (embeddings, tags, attributes, LLM-based user profile), how

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. As a Machine Learning Engineer on the Foundation AI organization, you will sit at the epicenter of our foundation model efforts. While the research world is focused on architecture, you will be the architect of the data flywheel that makes 3DGen possible. You aren't just building pipelines; you are building the infrastructure that defines how our models perceive and generate virtual worlds in three dimensions and across time. In this role, you will partner directly with our AI researchers to advance beyond experimental datasets and into the realm of dynamic, high-fidelity 3D data synthesis and evaluation. You will bridge the gap between research prototypes working locally to scaling for millions of users. You will design, implement, and scale robust, high-performance infrastructure to crawl, create, curate, store, and serve the massive 3D datasets required for these models. We are seeking accomplished software engineers with a passion for data, experience building large distributed systems, and a commitment to writing high-quality, well-tested code to solve complex data challenges at scale. Your contributions will ensure that our foundation models receive the highest quality data, thereby s

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team Growth Platform builds the machine learning systems that help businesses discover and use the Stripe products that meet their needs. Our recommendations reach users across the Dashboard, email, onboarding, documentation, and AI agent interfaces. We combine an understanding of each business with models that decide which recommendation is useful, when to show it, and how to learn from the outcome. Our work spans recommendation and ranking models, contextual bandits, agent-based recommendations, and the data and evaluation systems behind them. We build shared capabilities that product, marketing, and sales teams can use across Stripe. Success means helping businesses take useful actions and adopt products that help them grow, while keeping recommendations relevant and avoiding unnecessary messages. What you’ll do You will build and operate production ML systems that improve how Stripe recommends products, content, and next steps to businesses. You will own work from problem definition and feature development through training, evaluation, deployment, monitoring, and iteration. Working with data scientists, engineers, and product partners, you will turn model improvements into measurable user and business outcomes. Responsibilities Design, train, evaluate, deploy, and maintain models for recommendation, ranking, and personalized action selection across Growth Platform surfaces. Improve contextual bandit and policy-learning approaches, including e

About Glean: Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles. At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter, safer decisions at every level. Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50+ industries and 1,000+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI-fluent, and turning the superintelligent enterprise from concept into reality. If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more. You'll be deeply embedded where people get things done. You'll ship agentic capabilities on an open, extensible stack, with

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. As a Distinguished Engineer/Technical Director on Discovery, you’ll be in charge of the overall technical vision and execution for all machine learning initiatives across search and discovery teams. The mission of these teams is to connect a billion users with the best connections and content at the right time, covering a very diverse range of search and recommendation problems at a high scale. This is fundamental to how we personalize the experience to drive engagement and retention for Roblox users. With daily active users growing at a record rate, we are looking for expert tech leaders who are passionate about crafting and building machine learning solutions to power Search and Recommendations. You Will: Own the technical direction and implementation for the machine learning solutions that power the search and recommendation systems, driving Roblox content distribution. Author specs for new features and improvements. You would be responsible for setting the AI strategy, pushing the frontier of recommendation technology, and driving business outcomes across candidate generation, ranking, objective functions, and value delivery for both users and developers. Build and evolve in

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 . Reddit has a flexible first workforce! We’re looking for a Staff-level Machine Learning Engineer to join Reddit’s Ads Ranking Org. This role is part of a broader effort to enhance Reddit’s ML-powered ad ranking systems through improved model architectures, contextual embeddings, and conversion-optimized modeling. The ideal candidate will bring strong end-to-end modeling expertise, a deep understanding of modern deep learning techniques, and a passion for building systems that drive measurable impact in ads marketplaces. While ads experience is a plus, strong modelers from recommendation systems, search relevance, or other performance-driven domains are encouraged to apply. This position will sit on the App Ads Modeling team and report directly into the Ranking Org. Responsibilities: Design and train advanced ML models (e.g., DNNs, transformers) to power Reddit Ads Ranking Develop and optimize features including embeddings, contextual signals, and cross-session behavior Work closely with product, infra, and data teams to drive end-to-end model deployment and performance analysis Mentor other MLEs and contribute to modeling best practices across the org Shape the long-term modeling vision across one or more domains (conversion, app ads, shopping, brand, etc.) Required Qualifications: 7+ years of industry experience, including several years in applied ML roles Strong experience with Deep Learning architectures and ML frameworks (TensorFlow, PyTorch) Strong background in recommendation systems, ads ranking, or similar

About the Team The Statsig team within OpenAI builds the experimentation, feature rollout, dynamic configuration, and analytics systems that help OpenAI ship products with speed, safety, and evidence. Our work sits on the critical path for how product, engineering, research, and go-to-market teams learn from real-world usage and make high-confidence decisions. Statsig began as an independent company focused on helping builders move faster through trustworthy experimentation and feature management. After Statsig joined OpenAI, the team began the next chapter: bringing that deep product expertise, customer intuition, and mature platform infrastructure into OpenAI as the experimentation and rollout platform for every product we ship. Today, we support teams across ChatGPT, Codex, model measurement, consumer experiences, business subscriptions, developer products, and the shared infrastructure that connects them. These teams rely on Statsig to safely introduce new capabilities, compare product and model behavior, measure impact, and roll changes forward or back with confidence. We are at a defining moment in the platform journey. OpenAI has the data, product surface area, and pace of innovation to learn faster than almost any organization in the world, but that potential only becomes real if teams can experiment responsibly, measure clearly, and roll out changes safely. We are evolving experimentation systems to help teams learn from product behavior and make better evidence-based decisions. Based out of OpenAI’s Bellevue office, we are a close-knit team that values in-person collaboration, urgency, craft, and impact. We build for other builders, and the best version of this team is one where every OpenAI product team can move faster because the experimentation and rollout layer is dependable, fast, and easy to use. About the Role We are looking for a Machine Learning Engineer to lead the technical direction for ML-powered experimentation and insights capabilities. You

About Ramp Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $200B in annualized spend flows in and out of 70,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it. About the Role We’re seeking someone to lead the future of fraud machine learning at Ramp. In this role, you will help build core machine learning models, design data architectures, and set strategic roadmaps to help Ramp mitigate fraud-related threats while minimizing the friction experience by legitimate users. You will partner closely with product and engineering counterparts across model design, implementation, execution, and analysis. What You’ll Do Employ statistical and machine learning techniques on large datasets to discover patterns of fraud, platform abuse, and identity theft Prototype and productionize machine learning models and rules-based systems to protect Ramp and its users from fraud Partner closely with Fraud Engineering and Data Platform teams to augment and leverage data across first and third party sources, ensuring we’ve added as much context as possible to every decision we make Contribute to the culture of Ramp’s machine learning team by influencing processes, tools, and systems that will allow us to make better decisions in

Perplexity is seeking an experienced Machine Learning Research Engineer to help build the next generation of advanced search technologies, with a focus on retrieval and ranking. Responsibilities Relentlessly push search quality forward — through models, data, tools, or any other leverage available Architect and build core components of the search platform and model stack Design, train, and optimize large-scale deep learning models using frameworks like PyTorch, leveraging distributed training (e.g., PyTorch Distributed, DeepSpeed, FSDP) and hardware acceleration, with a focus on retrieval and ranking models Conduct advanced research in representation learning, including contrastive learning, multilingual, and multimodal modeling for search and retrieval Deploy models — from boosting algorithms to LLMs — in a scalable and performant way Build and optimize RAG pipelines for grounding and answer generation Collaborate with Data, AI, Infrastructure, and Product teams to ensure fast and high-quality delivery Qualifications Deep understanding of search and retrieval systems, including quality evaluation principles and metrics Proven track record with large-scale search or recommender systems Strong proficiency with PyTorch, including experience in distributed training techniques and performance optimization for large models Expertise in representation learning, including contrastive learning and embedding space alignment for multilingual and multimodal applications Strong publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, CVPR, SIGIR) Self-driven, with a strong sense of ownership and execution Minimum of 3 years (preferably 5+) working on search, recommender systems, or closely related research areas

Reolink , a leader in intelligent visual technology for homes and businesses, was founded in 2009 by a group of engineers with a strong commitment to and passion for smarter security solutions. Our products are now trusted by millions of users across more than 110 countries and regions worldwide. Building on this trust, we continue expanding our presence and bringing our innovations to more markets around the globe. Reolink remains committed to delivering advanced, reliable, and user‑centric solutions that empower people to protect what matters most. AI Algorithms Specialist (PHD Holder Only) 5 Work Days Per Week Office Near Tai Seng MRT, Singapore Medical Benefits Provided Entitled to Yearly Bonus & Performance Bonus Job Requirements PHD Holder in Computer Science, Applied Mathematics, Electrical Engineering, Pattern Recognition, Artificial Intelligence, Automatic Control, Operations Research, Biology, Physics / Quantum Computing, Neuroscience, Statistics or a related field. At least 2-5 years of workplace working experiences is preferable for this post. Familiar with common machine learning and deep learning algorithms and keeping track with the latest SOTA implementations. Strong programming skill in Python, C / C++, proficient in mathematical / statistical concepts and exceptional coding skills Hands-on experience with AI / ML frameworks be familiar such as Caffe, PyTorch, TensorFlow, MxNet etc. Have rich project experience in machine learning and deep learning, be familiar with common algorithm models, such as CNN, RNN, LSTM, Transformer, ViT, etc., and be able to improve and innovate models according to actual problems. Experience in familiar the design, parameter tuning and optimization methods of neural network models is a plus Experience in model compression and in the transplantation and optimization of deep learning forward inference on various platforms, including NPU / GPU / DSP / ARM on mobile platforms and CPU / GPU on server platforms is also a p

About the Team The Monetization team is a new cross-functional group working across engineering, product, research, and design to build the foundational systems that will help OpenAI scale access to intelligence responsibly. Our mission is to develop user-first, privacy-preserving monetization products—including next-generation ads experiences—that strengthen user trust, unlock economic opportunity, and support OpenAI’s long-term innovation. Monetization plays a critical role in enabling OpenAI to continue pushing the boundaries of AI capabilities while ensuring the benefits of AGI are broadly shared. We believe monetization must be aligned with user value, uphold rigorous privacy and safety standards, and sustain a healthy ecosystem of developers and businesses. This team operates in a greenfield environment and moves quickly through prototyping, experimentation, and iterative deployment. We partner closely with Product, Design, and Research to bring research breakthroughs into real-world systems at global scale. About the Role As a Research Engineer in OpenAI's Monetization Group, you will have the opportunity to work with some of the brightest minds in AI. You'll contribute to deploying state-of-the-art models in production environments, helping turn research breakthroughs into tangible solutions. If you're excited about making AI technology accessible and impactful, this role is your chance to make a significant mark. In this role, you will: Innovate and Deploy: Design and deploy advanced machine learning models that solve real-world problems. Bring OpenAI's research from concept to implementation, creating AI-driven applications with a direct impact. Collaborate with the Best: Work closely with researchers, software engineers, and product managers to understand complex business challenges and deliver AI-powered solutions. Be part of a dynamic team where ideas flow freely and creativity thrives. Optimize and Scale: Implement scalable data pipelines, optimize mod

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team Link is a digital wallet designed for fast and secure online payments. It allows consumers to save and use their preferred payment methods across the Link network, helping them check out quickly and securely wherever Link is accepted. The Link Fraud and Auth team works to make Link the most trusted and highest-performing way to pay. We protect consumers and merchants from fraud, abuse, and financial loss while maximizing authorization rates for good users. Our work spans consumer-facing experiences, payment infrastructure, and ML powered risk systems. We manage fraud and financial risk across a growing range of novel Link features, including Link’s agentic wallet, stored balance, and LPMs. The team also owns Instant Bank Payments, a proprietary payment method built on ACH rails, offering merchants immediate confirmation while protecting them from bank-initiated returns. IBP is the heart of Link’s revenue engine, giving LFA engineers the opportunity to shape and scale one of Link’s most important products. What you’ll do As a machine learning engineer on Link Fraud and Auth, you’ll build and operate models and risk decisioning systems that protect Link while helping more legitimate payments succeed. You’ll work across the full machine learning lifecycle, from analyzing fraud patterns and identifying opportunities to building, deploying, monitoring, and improving models in production. You’ll use data to form hypotheses, make practical modeling

Our Global Structure Deliveroo is now part of DoorDash, bringing together teams with even greater reach, scale, and ambition. Depending on your role, you may collaborate with teammates, systems, and leaders across DoorDash and Wolt. Together, we’re unlocking new possibilities as one global team. Join us in our mission to transform the way people shop and eat, where impact, innovation and growth drive everything we do. Our Global Engineering teams tackle complex technical challenges across a global, three-sided marketplace, building and scaling systems that serve millions of customers, riders and partners every day. We're looking for a Machine Learning Engineer to join our London office as part of a Global DoorDash Engineering team (working hybrid, 3 days in the office). Our New Verticals team exists to grow monthly active usage outside of restaurant ordering — groceries, retail, and everything in between. That means turning someone who only orders restaurant food into someone who also uses the app for a grocery run, and turning dormant users into active ones. You'll work on the surfaces that make that happen: the homepage, vertical landing pages, and the notifications that bring people back. This role sits closer to the personalisation and ranking side of that mission, working alongside colleagues across DoorDash, Deliveroo and Wolt. What You'll Be Doing Build and improve ranking models for vertical landing pages (grocery, retail), deciding what a user sees first when they land on a store page Design models that blend new-vertical content into a homepage built around restaurant ordering, without disrupting the core experience Build personalisation models that translate a user's existing behaviour into relevant recommendations for verticals they haven't tried yet Contribute to notification targeting — who gets notified, with what message, and when, to drive adoption of new verticals What You'll Need to Thrive Minimum 3+ years of experience as an ML Engineer or Data S

What you’ll do Own the tissue-class segmentation and labeling models for the ultrasound CT clinical analysis layer, and the pipelines that make them trainable and verifiable. Retune across 2D per-slice, 3D volumetric, and 2D×3D fusion as reconstructed image inputs are continuously updated, and clinical indications for use expand. Define training/evaluation pipelines, datasets, and metrics from the ground up or from open source; map model behavior to user needs and design requirements. Work with data labeling contractors, expert clinicians, and our internal cloud/data teams on labeling specs, QC, and dataset versioning. Help productionize models into a versioned, HIPAA-bound analysis service: reproducible/low-latency inference, per-prediction confidence, drift monitoring, and safe fallbacks. What we’re looking for Strong applied ML experience with a track record of developing new models — architecting, training, and evaluating from scratch as well as benchmarking against existing models. Experience with image segmentation (semantic/instance, 2D and ideally 3D/volumetric) and the modeling and training-data choices that make it robust across diverse patient anatomy. Comfortable moving fluidly between open-ended research iteration and producing quantifiable, testable models. Fluent in modern deep-learning tooling (e.g., PyTorch) and current development practices. Comfortable working under design controls, where model changes carry documentation and verification weight. Useful experience Image segmentation and label generation with modern architectures (U-Net / nnU-Net, 3D U-Net, transformer-based and promptable segmentation like SAM), including the geometry that ties voxel- and mesh-level predictions back to a coordinate frame. Learning under limited or noisy supervision: self-supervised / semi-supervised methods (masked autoencoders, contrastive pretraining like DINO/SimCLR), active learning, weak labels, and simulation-driven pretraining. Hands-on experience with data

Adobe is seeking a Machine Learning Engineer to join the Adobe Risk Platform (ARP) team. ARP is Adobe's centralized, adaptive system for detecting, preventing, and mitigating fraud and abuse across products and services — protecting surfaces like Commerce, Stock, and Firefly with real-time risk decisions, without degrading the customer experience. In this position, you will help build and develop machine learning models to identify fraudulent activity, detect abusive account behavior, and protect the experience of hundreds of millions of users. You'll work across the model lifecycle from raw behavioral data and feature engineering, to model training, deployment, and monitoring alongside senior engineers on the team. Key Responsibilities Help build and train ML models covering various fraud and abuse areas. These include financial transaction fraud, device-related deception, and account and identity abuse. The goal is a unified, continuously-updated trust and risk score. Contribute to feature engineering across transaction, device, and behavioral event data. Build and maintain feature pipelines on Databricks and Spark, transforming raw transaction and device event data into high-quality model inputs. Help translate prototypes into production ML systems, working with senior engineers on scalability, reliability, and observability. Support MLOps practices: experiment tracking, model versioning, CI/CD, and production monitoring. Collaborate cross-functionally with data science, product, and platform teams to understand fraud and abuse patterns across Adobe's surfaces. Stay ahead of advances in ML/AI, particularly in fraud detection and behavioral modeling, and bring relevant ideas to the team. Minimum Qualifications Bachelor's degree in Computer Science, Machine Learning, Data Science, or related field (or equivalent experience). 3