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About Sentry Software runs the world and the pace is faster than ever. Sentry helps developers fix errors and performance issues before users notice, so teams can spend less time firefighting and more time building. Trusted by 200,000+ organizations, Sentry is today’s application monitoring standard and our team is building its AI-native future. About the role As a Staff Machine Learning Engineer on Sentry’s AI/ML team, you’ll be directly responsible for developing the models and agents used to make our product smarter and more capable. This role is crucial; you will be at the forefront of integrating AI and machine learning into our core products, from issue triage and resolution to predictive analytics for application performance monitoring. Your work will help companies around the globe gain actionable insights into their software, enabling them to build better products, faster. In this role you will Build state-of-the-art agentic AI systems to triage, debug, and solve real production issues Leverage Sentry’s novel (and massive) dataset of errors, spans, and profiles Own the development of major initiatives in the AI/ML space You'll love this job if you Are driven by impact and enjoy working on high-stakes, high-visibility projects Enjoy building things. You will have the opportunity to join the AI/ML team as one of its foundational members Thrive in cross-functional teams and enjoy building features alongside developers and product teams Qualifications Minimum 4+ years of professional experience with a MS/PhD degree in computer science, machine learning, or a related field Minimum 6+ years of professional experience with Bachelor’s degree in computer science, machine learning, or a related field Demonstrated expertise building production-grade agentic systems and tools You are comfortable writing production quality code (we use Python) Expertise with deep learning frameworks (we use PyTorch) Familiarity in deploying machine learning models at scale in production

The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, we’re behind some of Spotify’s most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them. The Surfaces Podcasts team owns podcast recommendations across some of Spotify’s most visible surfaces, including Home and the Now Playing view. We build and improve the recommendation systems that help listeners discover their next favorite show or episode while helping creators reach the right audiences. As a Staff Machine Learning Engineer, you’ll help shape the future of personalized discovery and engagement at Spotify. You’ll work at the intersection of recommendation systems, large language models, and production-scale machine learning infrastructure to build experiences for listeners at global scale. You’ll take models from research to production, help set technical direction in ambiguous problem spaces, and solve complex personalization challenges across Spotify. What You'll Do Own and improve the machine learning models and systems that power the Home feed, including the Shortcuts experience. Design, build, and ship personalized recommendations that serve millions of Spotify listeners globally. Build content recommendation systems for emerging agentic and AI-powered user experiences. Train, fine-tune, evaluate, and optimize large language models using techniques such as supervised fine-tuning (SFT), distillation, and parameter-efficient training approaches. Partner closely with Product Managers, Engineers, Data Scientists, and Designers to define and execute experimentation strategies. Drive A/B testing, monitoring, model evaluation, and continuous optimization of recommendation quality, reliability, and cost efficiency.

Join us in building the future of finance. Our mission is to democratize finance for all. An estimated $124 trillion of assets will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history. If you’re ready to be at the epicenter of this historic cultural and financial shift, keep reading. ABOUT THE TEAM + ROLE We are building an elite team, applying frontier technologies to the world's biggest financial problems. We're looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn't a place for complacency, it's where ambitious people do the best work of their careers. We're a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards. The AI Platform & Agentic Apps team builds the agent platform behind every AI agent at Robinhood. Today it gives a growing number of engineers and employees an AI teammate that ships code, queries data, and runs operational workflows on their behalf. We're building toward the same platform powering the agents millions of customers interact with directly, in real time. These agents don't just answer questions — they're designed to take real action across carefully curated meta harnesses. This is agentic AI at real scale, in a regulated financial environment, and it will change how Robinhood works! As a Staff Machine Learning Engineer on the AI Platform & Agentic Apps team, you will design and build the harness that every agent at Robinhood runs on. A critical part of the role is making those agents trustworthy at scale: trajectory-level evals that measure how an agent reasons and acts, and action guardrails — permission models, approval gates, and sandboxing — built as platform primitives that other teams adopt. You'll be a technical anchor on a growing, high-caliber team, collaborating with product, infrastructure, and fellow ML engineers to take ambitious ideas from

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

Secure Every Identity, from AI to Human Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence. This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk. About Okta Secures AI Team The Okta for AI Agents Team is building the future of digital identity management. The way companies are using agents and allowing them access is undergoing a fundamental shift, enabling teams to move fast while staying secure. Our North Star is clear: To get AI right, you have to get identity right. We are not just managing identities; we are building the industry's first Identity Security Fabric for the agentic era. As organizations rapidly deploy AI, these non-human entities are acting with access to critical tools, often bypassing traditional perimeters and creating a 'Shadow AI' crisis. Our mission is to move identity from a reactive gatekeeper to a unified control plane, transforming AI risk into business ROI. We are tackling this by implementing a comprehensive four-pillar maturity model: Discover, Onboard, Protect, and Govern. We are driving innovation by defining the standards for Agentic Identity—including pioneer work with securing AI Agents and support for protocols like MCP. You will be helping us build the infrastructure that allows enterprises to scale AI safely, ensuring every access decision—whether made by a human or an automated agent—is authenticated, authorized, and audited at scale. We are a diverse team of engineers, product managers, and designers who are bringing Okta’s expertise in identity to define the future of Agent Identity management, focusing on enablement while staying secure. About the role

Secure Every Identity, from AI to Human Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence. This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk. About Okta Secures AI Team The Okta for AI Agents Team is building the future of digital identity management. The way companies are using agents and allowing them access is undergoing a fundamental shift, enabling teams to move fast while staying secure. Our North Star is clear: To get AI right, you have to get identity right. We are not just managing identities; we are building the industry's first Identity Security Fabric for the agentic era. As organizations rapidly deploy AI, these non-human entities are acting with access to critical tools, often bypassing traditional perimeters and creating a 'Shadow AI' crisis. Our mission is to move identity from a reactive gatekeeper to a unified control plane, transforming AI risk into business ROI. We are tackling this by implementing a comprehensive four-pillar maturity model: Discover, Onboard, Protect, and Govern. We are driving innovation by defining the standards for Agentic Identity—including pioneer work with securing AI Agents and support for protocols like MCP. You will be helping us build the infrastructure that allows enterprises to scale AI safely, ensuring every access decision—whether made by a human or an automated agent—is authenticated, authorized, and audited at scale. We are a diverse team of engineers, product managers, and designers who are bringing Okta’s expertise in identity to define the future of Agent Identity management, focusing on enablement while staying secure. About th

This is Adyen. Adyen provides payments, data, and financial products in a single solution for customers like Meta, Uber, H&M, and Microsoft - making us the financial technology platform of choice. At Adyen, everything we do is engineered for ambition. For our teams, we create an environment with opportunities for our people to succeed, backed by the culture and support to ensure they are enabled to truly own their careers. We are motivated individuals who tackle unique technical challenges at scale and solve them as a team. Together, we deliver innovative and ethical solutions that help businesses achieve their ambitions faster. Financial Products About the Role The Financial Products org at Adyen is at the forefront of our evolution, building the foundational infrastructure that enables our customers to manage their finances, issue cards, and access credits and financing globally. Adyen is building a Machine Learning Engineering team in San Francisco focused on Credit Risk Modeling for Underwriting within Financial Products. This team will develop the models, scorecards, and production systems that enable Adyen to scale its credit products 100x. As a Staff Machine Learning Engineer you will design, productionize, and operate machine learning models and rule-based decision systems that power credit products. You will work across the full model lifecycle, from research and data analysis to training, deployment, monitoring, and continuous improvement. This role is ideal for an engineer who combines strong machine learning and production engineering experience with sound judgment in high-integrity financial systems. You will help build continuous data flywheels that improve underwriting decisions while balancing rapid product innovation with robustness, explainability, and global scale. We are looking for engineers with a customer-problem-first mindset and experience building reliable ML systems in production. You will work closely with product,

About Taskrabbit: Taskrabbit is a marketplace platform that conveniently connects people with Taskers to handle everyday home to-do’s, such as furniture assembly, handyman work, moving help, and much more. At Taskrabbit, we want to transform lives one task at a time. As a company we celebrate innovation, inclusion and hard work. Our culture is collaborative, pragmatic, and fast-paced. We’re looking for talented, entrepreneurially minded and data-driven people who also have a passion for helping people do what they love. Together with IKEA, we’re creating more opportunities for people to earn a consistent, meaningful income on their own terms by building lasting relationships with clients in communities around the world. Taskrabbit is a hybrid company with employees distributed across the US and EU and a Built In — Best Places to Work (2022, 2023, 2024, 2025) continually ranked across multiple national and regional categories. Join us at Taskrabbit, where your work will be meaningful, your ideas valued, and your potential unleashed! This role is hybrid requiring 2 days in office at our San Francisco or NYC hub every Tuesday & Wednesday. About the Role Machine Learning is a cornerstone at Taskrabbit, and we're looking for a Staff Machine Learning Engineer to join our team and lead the next phase of our customer retention strategy. This is a critical, full-stack role for an individual who is passionate about the end-to-end lifecycle: from initial research and model development to building the robust systems that power repeat customer engagement and lifetime value growth at scale. Taskrabbit's greatest growth opportunity lies in deepening customer relationships and accelerating repeat purchases. Our most valuable customers are those who return frequently, discover new service categories, and increase their spending over time. There's significant untapped potential in the marketplace: repeat customers spend 3-5x more than one-time users, and category expan

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 . The AI Security team within Reddit’s Security Platform Engineering organization builds security into Reddit’s products, engineering systems, AI platforms, and operational infrastructure so the secure path is the easiest path for both people and agents. A core part of this work is developing practical, high-quality machine learning systems that detect and prevent risks such as prompt injection, jailbreaks, sensitive data exposure, and unsafe or unauthorized AI behavior. Building on Reddit’s centralized LLM Guardrails Platform, which provides a shared security and safety boundary across major products, we are expanding ML-powered protections as Reddit’s AI systems and threat landscape evolve. We are looking for a founding Staff Machine Learning Engineer to lead the development, training, and optimization of models for AI security at Reddit. This is a strategic and hands-on individual contributor role, combining deep ownership of model architecture, training data, and experimentation with technical leadership across teams. You will help Reddit deliver stronger AI protections while preserving a high-quality product experience. How You’ll Have Impact Select, adapt, fine-tune, evaluate, and deploy pretrained models and lightweight classifiers for Reddit-specific security problems. Build reproducible training and evaluation pipelines on Reddit’s ML platform, partnering with platform engineers to improve inference performance, resource efficiency, and operational reliability. Set the technical vision and multi-quarter modeling

About Taskrabbit: Taskrabbit is a marketplace platform that conveniently connects people with Taskers to handle everyday home to-do’s, such as furniture assembly, handyman work, moving help, and much more. At Taskrabbit, we want to transform lives one task at a time. As a company we celebrate innovation, inclusion and hard work. Our culture is collaborative, pragmatic, and fast-paced. We’re looking for talented, entrepreneurially minded and data-driven people who also have a passion for helping people do what they love. Together with IKEA, we’re creating more opportunities for people to earn a consistent, meaningful income on their own terms by building lasting relationships with clients in communities around the world. Taskrabbit is a hybrid company with employees distributed across the US and EU and a Built In — Best Places to Work (2022, 2023, 2024, 2025) continually ranked across multiple national and regional categories. Join us at Taskrabbit, where your work will be meaningful, your ideas valued, and your potential unleashed! This role is hybrid requiring 2 days in office at our San Francisco or NYC hub every Tuesday & Wednesday. About the Role Machine Learning is a cornerstone at Taskrabbit, and we're looking for a Staff Machine Learning Engineer to join our team and lead the next phase of our customer retention strategy. This is a critical, full-stack role for an individual who is passionate about the end-to-end lifecycle: from initial research and model development to building the robust systems that power repeat customer engagement and lifetime value growth at scale. Taskrabbit's greatest growth opportunity lies in deepening customer relationships and accelerating repeat purchases. Our most valuable customers are those who return frequently, discover new service categories, and increase their spending over time. There's significant untapped potential in the marketplace: repeat customers spend 3-5x more than one-time users, and category expan

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. We are the Fraud Data team within Plaid's Fraud organization. We leverage relationships and activity across Plaid’s network to develop machine learning models that help customers detect and prevent fraud while minimizing friction for legitimate users. Our team owns the full model lifecycle, from data processing and experimentation to feature pipelines, model serving, and monitoring. In this research role, you’ll partner closely with Machine Learning Engineers and Data Scientists to uncover new signals, explore and evaluate innovative modeling approaches, and translate promising research into production-ready solutions that strengthen fraud detection across Plaid’s network. As a Senior Research Scientist, you will lead applied research to develop next-generation fraud detection models across relational graphs, sequential events, images, and video data. You’ll design rigorous experiments and evaluation methodologies that reflect real-world fraud dynamics, while exploring state-of-the-art approaches such as Graph Neural Networks and Transformer-based foundation models. In close partnership with Machine Learning Engineers, you’ll translate promising research into production-ready solu

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. The Data team within Plaid’s Fraud organization builds the machine learning systems that power Plaid’s fraud detection products, leveraging Plaid’s unique network data to identify and stop fraud before it happens. The team owns the full ML lifecycle—from feature pipelines and model training to production serving and monitoring—building reliable, scalable systems that deliver high-quality fraud detection as we grow to support hundreds of customers. As a Senior Machine Learning Engineer, you will own the development of high-performance feature computation and online inference pipelines that power production machine learning systems at scale. You’ll build robust observability, monitoring, and automated debugging capabilities, while leveraging AI-assisted tools to investigate complex system behavior and maintain high reliability. You’ll partner closely with ML Infrastructure, Data Science, and Product teams to execute critical technical initiatives and deliver scalable, high-impact ML solutions. Responsibilities: Build and scale machine learning systems that power a rapidly growing fraud detection product in a fast-paced environment. Solve complex technical challenges at the intersect

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. The Fraud Data team at Plaid builds the machine learning systems that power Plaid’s fraud detection products, leveraging insights from across Plaid’s network to help identify and stop fraud before it happens. Our team works across the full data science and machine learning lifecycle—from discovering new signals and experimenting with models to deploying and optimizing them in production. We continuously learn from real-world model performance and customer feedback to improve our systems and develop new ways to protect customers and consumers from evolving fraud threats. As a Senior Machine Learning Engineer on Plaid's Fraud Data team, you will develop models that improve fraud detection for our customers. You will identify predictive patterns in Plaid's network data and lead projects from initial experiments through model deployment and ongoing improvement. Investigate fraud patterns and model errors to identify new signals, improve detection, and expand coverage across customers and use cases. Develop training datasets and predictive features, addressing challenges such as incomplete labels, class imbalance, data leakage, and changing fraud behavior. Design, train, and tune model

The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, we're behind some of Spotify's most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you'll keep millions of users listening by making great recommendations to each and every one of them. Surfaces Moments is a ML team within the Personalization Mission focused on creating moment-based experiences across Spotify surfaces. The team owns and evolves the experiences that help listeners quickly connect with the content that matters most to them, including the Home Shortcuts experience and the underlying intelligence that powers it. By combining cutting-edge machine learning, recommendation systems, and product thinking, the team delivers highly relevant, personalized experiences to millions of listeners around the world. As a Staff Machine Learning Engineer, you will help shape the future of personalized discovery and engagement at Spotify. You'll work at the intersection of recommendation systems, large language models, and production-scale machine learning infrastructure to build experiences that delight users and drive meaningful impact. This role is ideal for someone who enjoys taking models from research to production, driving technical direction in ambiguous problem spaces, and solving complex personalization challenges at global scale. What You'll Do

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. About the Team The Embedded Insights team supports Plaid’s mission to build a world-class suite of intelligence products. We identify the best opportunities to use machine learning in Plaid products, prove out those opportunities, and collaborate with cross-functional partners to turn them into real-world production systems. About the Role As a Senior Machine Learning Engineer on Embedded Insights, you will help shape Plaid’s future by building machine learning-powered products and features. You will initially support the Plaid App, working on a 0-to-1 consumer-facing product and helping establish product-market fit for a new business line. You will work closely with product managers, data scientists, engineers, customers, and other machine learning engineers to translate ambiguous opportunities into effective ML systems that create measurable customer value. In supporting the Plaid App, you will: Build machine learning-based features for a 0-to-1 consumer-facing product. Partner with product managers to translate ambiguous business requirements into machine learning problems and influence product strategy and roadmap decisions. Rapidly iterate and experiment to help drive product