Machine Learning Fellow Jobs

Altana is the network for trusted trade. Our AI-powered product network empowers governments and businesses to build a more resilient and secure global economy while keeping trade flowing. The Opportunity at Altana We're looking for a Senior Staff Machine Learning Engineer to lead the AI behind Altana Nexus, our agentic platform for global trade. You'll work on a continuous stream of public and proprietary trade data (shipments, customs filings, trade documents), and build the models and AI agents that turn it into real-time decisions on risk, compliance and enforcement for governments and the world's largest companies. Your Responsibilities Drive the technical direction for agentic systems. Define the architecture and roadmap for the models and agentic systems that process trade data as it arrives (shipments, customs declarations, invoices, bills of lading) and turn it into risk, classification and compliance decisions in real time. Build the pipeline from ingestion to decision. Lead the design of reliable, cost-efficient systems for ingestion, document understanding, entity resolution, inference and routing across models, at the scale of global trade. Adapt models to trade. Use analyst feedback, production outcomes and agent traces to fine-tune and post-train models (SFT, DPO, RL with domain graders) where it measurably beats prompting, and keep every model improving over time. Design the observability and feedback stack. Build the evals, tracing and feedback loops that decide what ships and catch regressions in production. Lead beyond your team. Mentor senior engineers, raise the technical bar across Engineering, and help shape how we hire and level ML talent. Shape the product and the business. Work with software engineers, data scientists, product managers and customers to generate new business ideas and turn ambiguous problems into shipped capabilities. About You BS/MS in Computer Science, Machine Learning, or a related field, or equivalent experience 8+

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 . About Pinterest Millions of people across the world come to Pinterest to find new ideas every day. It’s where they get inspiration, dream about new possibilities and plan for what matters most. Our mission is to help those people find their inspiration and create a life they love. As a Pinterest employee, you’ll be challenged to take on work that upholds this mission and pushes Pinterest forward. You’ll grow as a person and leader in your field, all the while helping users make their lives better in the positive corner of the internet. Our new progressive work model is called PinFlex, a term that’s uniquely Pinterest to describe our flexible approach to living and working. Visit our PinFlex landing page to learn more. Staff Machine Learning Engineer, Applied Science As part of Pinterest Labs , you'll work on tackling new challe

About the Team Our team builds classification and evaluation systems that give post-training and research teams fast, reliable feedback on model behavior. We combine machine learning and production engineering to make these capabilities accurate, efficient, and useful at scale, helping researchers assess changes and improve models and products. We work closely with research teams to develop useful measurements and make them practical to run. Our work spans classifier development, rigorous evaluation, and efficient production inference, with ownership from initial experimentation through deployment and continued improvement. About the Role We’re looking for a machine learning engineer to develop new classification and evaluation capabilities and bring them into production. You’ll help turn open-ended questions about model behavior into well-defined ML problems, build methods that address them, and establish how reliably those methods work. This role combines traditional machine learning, model evaluation, and real-time engineering. You’ll own classifiers throughout their lifecycle, including supervised fine-tuning, distillation, evaluation, deployment, and production inference. You’ll work closely with researchers to identify valuable problems and with software engineers to make promising approaches useful at scale. In this role, you will: Build, deploy, and operate production classification and evaluation systems that provide useful feedback to post-training and research teams. Optimize production inference, including model serving, batching, caching, throughput, latency, compute utilization, and cost. Train and optimize classifiers using supervised fine-tuning and distillation, validating quality and efficiency before deployment. Own the reliability and observability of deployed classifiers, diagnose production issues, and improve their performance over time. Design rigorous evaluations that assess accuracy, calibration, generalization, and potential measurement bi

About the team The Growth team drives user and revenue growth across ChatGPT’s consumer and business segments and other OpenAI products worldwide. We work across the full funnel—from awareness and acquisition through activation, retention, and expansion—using experimentation, data science, machine learning, and product engineering to help more people discover and realize durable value from OpenAI’s products. Our team partners closely with Product, Engineering, Data Science, Design, Finance, Lifecycle, and Growth Marketing. We move quickly, test rigorously, and hold a high bar for measurement, privacy, reliability, and user trust. About this role: We are looking for a Machine Learning Engineer to build the models and decision systems that optimize paid marketing investment for ChatGPT and other OpenAI products. You will develop budget allocation and control algorithms that determine where to invest, how quickly to spend, and how to adjust as performance changes. The challenge is to make reliable decisions from noisy, delayed, and incomplete signals. You will combine machine learning, optimization, and experimentation to improve incremental growth and long-term user value within budget and efficiency constraints. You will own this work from problem formulation and prototyping through deployment, evaluation, and ongoing operation. This role is based in San Francisco, CA. We use a hybrid work model of three days in the office per week and offer relocation assistance to eligible employees. In this role, you will: Build budget allocation algorithms. Decide how to distribute investment across channels, campaigns, and markets using estimates of marginal returns, while accounting for diminishing returns, uncertainty, and business constraints. Develop spend pacing and adaptive control systems. Adjust budgets and spending rates as performance changes, balancing responsiveness with stability when feedback is delayed or noisy. Build in spend limits, monitoring, and safe fallback

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 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 Engineering Manager to join us in London on a hybrid basis (3 days a week). In this role, you’ll lead the team that owns intelligence across our ordering experience, from store page and item selection through cart, upsell and checkout, plus bundling. Personalisation is one of the biggest levers we have to drive value to our customers, and your team will make the app feel less like a generic catalogue and more like one that understands what each customer is trying to do. What you’ll be doing Lead and develop a high-performing team of around 12 Machine Learning Engineers, distributed across multiple pods and spread across London, Finland and Berlin, fostering a culture of technical excellence, ownership and rapid experimentation. Own and evolve the ranking, recommendation and personalisation systems across ordering surfaces, helping customers find the right items quickly as our selection grows. Set the technical vision for the team’s ML ecosystem, balancing near-term product goals with longer-term bets on areas like on areas across recommendations to agentic approaches, and intent-aware experiences that adapt dynamically to how customers browse and act. Lead the unification of our recommendation models across brands (Deliveroo, Wolt and DoorDash) and markets, working out how to power personalisation at global scale while keepin

The Zoox 3D Sensor Simulation team’s work is at the core of creating in-the-loop simulation and high fidelity synthetic data for training and testing autonomous vehicle AI. We are looking for a machine learning professional with experience applying ML to sensor data, such as LiDAR, radar, and cameras. This role lies at the intersection of machine learning and 3D simulation at Zoox, ranging from learned sensor models to neural 3D reconstruction. By improving the realism of simulated sensors (lidar, radar, and cameras), you’ll enable the Zoox robotics autonomy stack to learn, improve, and enhance the mobility of humans. In this role, you will: Design, train, and validate ML models that improve the realism of simulated lidar, radar, and camera data in autonomous vehicle (AV) simulation pipelines. Collaborate with Synthetic Data and HIL Sim teams to integrate 3DGS and NeRF-based 3D reconstruction with simulation of all sensor modalities. Build and curate training datasets from real and simulated sensor data, including GenAI-based data augmentation. Partner with Sim V&V and Perception engineers to define sensor realism metrics and close the sim-to-real gaps with the greatest impact on autonomy. <li style="font-size: 11pt; font-family: Ar

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. ML Platform powers hundreds of use cases and billions of inferences per day across Discovery, Safety, Economy, and Creation. We build the primitives that let teams train, evaluate, deploy, and operate models quickly and safely—so a new ML idea can reach production in weeks or less. We’re looking for a Principal Platform Engineer who treats platform as a product : someone who can turn complex ML/AI infrastructure into clear, durable APIs, SDKs and easy-to-use CLIs/UIs that our internal developers love. This role blends product thinking, developer experience, backend engineering, and infrastructure at scale. Hands‑on ML experience is a plus; a track record of building internal platforms that developers love is a must. You Are Have 10+ years of professional experience and have a wealth of system design experience upon which to draw to build a scalable, reliable ML platform for all of Roblox. A Code Machine; you love not only to design and communicate ideas but also to actually ship product. You obsess about user feedback, and constantly drive towards getting platform features in customers hands. Proficient in API design and developer experience—gRPC/REST APIs, SDKs, CLIs, and simple UIs that d

About the opportunity We are seeking an experienced Lead Technical Product Manager for our Machine Learning Platform within the Intelligent Operations Platforms segment. In this role, you will define the long term vision, platform architecture, and execution strategy for core Machine Learning infrastructure across N26. Your work will enable squads across the bank to automate risk assessments, prevent financial crime, and scale operational efficiency. Our ML Platforms team serves as the central nervous system for real-time, data-driven decisioning. We build and scale horizontal ML capabilities that allow critical functions including Financial Crime, Credit Risk, and Core Operations to execute with absolute precision at scale. As a Lead Product Manager, you will drive multi-squad strategy, transforming complex predictive models into resilient, compliant, sub-second scoring systems. In this role, you will Drive the Core ML Platform Vision: Define and execute the multi-year roadmap for the central Machine Learning platform, ensuring alignment with broader engineering architecture and executive business goals. Lead High-Volume ML Portfolios: Oversee complex predictive ML capabilities supporting high-throughput banking systems, focusing on real-time transaction monitoring, automated credit risk scoring, and operational workflow decisioning. Scale Enterprise MLOps Infrastructure: Own the platform tooling and standards across the full ML lifecycle. Drive strategic decisions around feature stores, real-time feature extraction pipelines, model registries, automated retraining triggers, and CI/CD for ML. Establish Model Evaluation and Governance: Define robust offline and online model evaluation frameworks, shadow deployment mechanics such as champion and challenger testing, and real-time monitoring for model drift, inference latency, and data quality degradation. Bridge Technical Leadership and Compliance: Partner closely with Principal Data Scientists, ML Engineers, Security

About the opportunity We are seeking an experienced Lead Technical Product Manager for our Machine Learning Platform within the Intelligent Operations Platforms segment. In this role, you will define the long term vision, platform architecture, and execution strategy for core Machine Learning infrastructure across N26. Your work will enable squads across the bank to automate risk assessments, prevent financial crime, and scale operational efficiency. Our ML Platforms team serves as the central nervous system for real-time, data-driven decisioning. We build and scale horizontal ML capabilities that allow critical functions including Financial Crime, Credit Risk, and Core Operations to execute with absolute precision at scale. As a Lead Product Manager, you will drive multi-squad strategy, transforming complex predictive models into resilient, compliant, sub-second scoring systems. In this role, you will Drive the Core ML Platform Vision: Define and execute the multi-year roadmap for the central Machine Learning platform, ensuring alignment with broader engineering architecture and executive business goals. Lead High-Volume ML Portfolios: Oversee complex predictive ML capabilities supporting high-throughput banking systems, focusing on real-time transaction monitoring, automated credit risk scoring, and operational workflow decisioning. Scale Enterprise MLOps Infrastructure: Own the platform tooling and standards across the full ML lifecycle. Drive strategic decisions around feature stores, real-time feature extraction pipelines, model registries, automated retraining triggers, and CI/CD for ML. Establish Model Evaluation and Governance: Define robust offline and online model evaluation frameworks, shadow deployment mechanics such as champion and challenger testing, and real-time monitoring for model drift, inference latency, and data quality degradation. Bridge Technical Leadership and Compliance: Partner closely with Principal Data Scientists, ML Engineers, Security

Ready to do the most impactful work of your career? At Coinbase , we are uncompromising on our mission to increase economic freedom. The bar is high, the environment is intense, and we like it that way. This isn't a place for complacency, it’s a place to be pushed past your perceived limits. If you're ready to build the future of finance alongside people who refuse to settle for "good enough," you belong here. Coinbase is a remote-first, but not remote-only company. Expect to get together quarterly for intense in-person working sessions called “surges.” learn more about working at Coinbase . Staff Machine Learning Engineer, CX Intelligence CEE Intelligence (CEEI) is the ML and intelligence layer for Coinbase's customer engagement ecosystem, building customer intelligence, evaluation systems, routing and classification models, safety guardrails, and decisioning capabilities that power Help Center, chat, voice, agent tooling, and other customer support runtimes. As a Staff Machine Learning Engineer, you'll lead the design and delivery of production ML systems across the org, shaping technical strategy, building scalable intelligence capabilities, and partnering with product and engineering stakeholders to improve resolution quality, automation coverage, safety, and customer experience. What you'll do: Own the design and delivery of production ML systems, applying LLMs, deep learning, graph neural networks, and classical ML techniques to solve complex customer experience problems. Partner with leadership and cross-functional stakeholders to define strategic roadmaps and translate vision into quarterly execution plans with measurable outcomes. Lead high-impact ML projects end-to-end, from architecture through production, managing priorities, risks, and dependencies. Build scalable, secure, and reliable ML systems, evaluation pipelines, and data workflows using high-quality, well-tested Python code. Mentor engineers and establish team-wide standards fo

About Vercel: Vercel is the agentic infrastructure company, freeing people and agents to ship what's next. For more than a decade we've helped builders move from idea to production with speed, security, and exceptional developer experience. Now we're scaling our products for both agents and people to ship and run software, built in the open and trusted by OpenAI, PayPal, Ramp, Supreme, and millions of developers worldwide. About the Role: We are looking for a Machine Learning Engineer on our Trust & Safety Engineering team, to build and operate the production systems that detect and stop abuse on the platform at internet scale. This is an engineering-first role, roughly 80% writing and shipping production code (services, pipelines, and infrastructure) and 20% modeling, taking detection and classification approaches from prototype to hardened systems. This is a hybrid role based in San Francisco or New York City, with three days a week in the office. What you’ll do: Design, build, and operate production systems that detect and disrupt abusive behavior across the platform, with an emphasis on reliability, scalability, and observability Build and maintain machine learning (ML) infrastructure, including training pipelines, feature pipelines, serving infrastructure, and evaluation frameworks Ship large language model (LLM) and classical ML approaches for abuse detection and classification, turning prototypes into maintainable code Own systems end to end, from integrating a model into the platform through deployment, monitoring, and iteration Work with security, product, and infrastructure teams to turn abuse patterns and detection logic into production systems What you need: 5+ years of software engineering experience building and operating production systems at scale Wrote production code in Python, plus JavaScript/TypeScript or Go Built and maintained ML infrastructure, such as training and serving pipelines, feature stores, or evaluation tooling Integrated LLMs in

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 . The User and Content Foundations org is responsible for deeply understanding our content, users and their interactions by generating high-quality, reusable signals and insights that power relevance and personalization across the platform. We work at the intersection of distributed machine learning, large multimodal models, data infrastructure, and product adoption to build foundational capabilities that support our partners, including Discovery, Search, Growth, and Ads. We are looking for an experienced Staff Machine Learning Engineer / Technical Lead who can drive the team’s technical direction, lead the team in building scalable content understanding systems, and make an impact on core Pinterest user and business outcomes. In this role, you will lead ambiguous, high-impact scientific and engineering efforts that create reusable represent

Who we are At Twilio, we’re shaping the future of communications, all from the comfort of our homes. We deliver innovative solutions to hundreds of thousands of businesses and empower millions of developers worldwide to craft personalized customer experiences. Our dedication to remote-first work , and strong culture of connection and global inclusion means that no matter your location, you’re part of a vibrant team with diverse experiences making a global impact each day. As we continue to revolutionize how the world interacts, we’re acquiring new skills and experiences that make work feel truly rewarding. Your career at Twilio is in your hands. . Hiring and how we work We use Artificial Intelligence (AI) to help make our hiring process efficient. That said, every hiring decision is made by real Twilions! Also, while we are a remote-first company, you may be asked to report in person on an ad-hoc basis for team gatherings, functional off-sites or customer meetings. . See yourself at Twilio Join the team as our next Machine Learning Engineer III in Twilio’s Customer Memory Team.. About the job In this role, you will build the foundational AI/ML capabilities that power intelligent engagement experiences for Twilio customers - letting any application, human or AI agent, or channel know who a customer is, what they have done, and how best to reach them. This role requires a builder and curious mindset, someone who can bridge the gap across theoretical research, production-grade experimentation and enterprise quality software. You will lead rapid research cycles to solve complex problems regarding contextual intelligence and large scale ML problems ensuring every interaction feels seamless and personalized. Responsibilities In this role, you’ll: Build out new functionality within a fast moving product seeing rapid adoption. Take a nebulous idea, design a rigorous experiment, and execute it within weeks. Conduct deep-dive research into

Senior Machine Learning Engineer — NY - New York. Apply via Workday.

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 The Radar ML team builds the machine learning systems that power Stripe’s Radar fraud and abuse product suite. Our models make real-time decisions across a significant share of internet commerce - helping businesses stop fraud, increase authorization, and grow safely. By turning economy-scale datasets into compelling ML products, the team directly improves customer outcomes and drives meaningful revenue for Stripe. The team is growing, and expanding into multiple new areas. We are building abuse protections across the full customer life-cycle, adaptive and customizable fraud systems for enterprises, and risk intelligence for 50+ global payment methods. What you’ll do You will own ML problems from conception to production: analyzing new fraud and abuse patterns, exploring large-scale datasets, experimenting with model architectures, designing evaluations, deploying models, and monitoring performance. Your work may include developing transformer architectures for payment data, building graph-powered intelligence across consumers, leveraging LLMs to detect emerging attacks, or rapidly prototyping new protections with frontier AI labs. You will have opportunities to ship 0→1 products, and to improve mission-critical ML models operating at Stripe scale. Responsibilities Build, train, evaluate, and deploy models that detect fraud and abuse across Stripe’s global network. Investigate emerging fraud and abuse patterns, identify whe