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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

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). 5

About the Role Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide. In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring. The team is actively building in-house behavioral foundation models that learn identity-preserving representations from long sequences of user activity. This is a role for an engineer who wants to own deep learning systems end-to-end — not consume pre-built ones. Key Responsibilities Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences. Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine-tuning. Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent). Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs. Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization. Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and

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, Identity Verification As a Staff Machine Learning Engineer on the Identity Verification team within the Platform group, you'll own the ML systems that determine whether a person, document, and capture session are legitimate. Every signup, account recovery, and high-risk action at Coinbase depends on these models. You'll lead the technical strategy for IDV ML end-to-end, from architecture through production enforcement, protecting the integrity of millions of accounts. What you'll do: Own the full IDV ML stack, including document authenticity models, 1:1 and 1:N face-match, liveness detection, presentation-attack detection, and deepfake/injection detection from feature pipeline through threshold tuning and production enforcement. Build identity-graph systems using GNNs that cluster accounts sharing biometric, device, and document signals to detect synthetic-identity rings and coordinated fraud at onboarding. Develop behavioral and device-intelligence models for capture-session anomaly detection, bot-vs-human classification, and device-fingerprint-based risk scoring at real-time latency. Drive vendor ML strategy by benchmarking external models against a Coinbase-owned evaluation set, designing dynamic routing logic across providers and geographies, and building the in-house evaluation layer that catches regressions befo

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 . As a Senior Machine Learning Platform Engineer on the ML Platform team within the Platform group, you'll build the foundational infrastructure that powers feature engineering, model training, and model serving across Coinbase. The platform you build supports fraud detection, user personalization, and blockchain analysis. You'll own critical systems spanning stream processing, distributed training, and highly available inference services, directly shaping how ML scales across the company. What You'll Do: Own the design and reliability of ML inference infrastructure serving both predictive models and LLMs, maintaining high availability and low latency at scale. Build and optimize low-latency streaming pipelines that deliver fresh, high-quality feature data to production ML models. Drive improvements to distributed training infrastructure, enabling ML engineers to process large data volumes efficiently. Develop observability tooling to monitor data quality entering models and detect degradations impacting model performance. Mentor junior engineers on building production-grade software, raising the team's engineering standards over time. Required Skills and Experience: 5+ years of industry experience as a software engineer, with demonstrated ownership of distributed systems in production environments. Built and operated low-latency data or ML infrastructure (stream

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 . Team Description: The Ads Retrieval ML team builds the machine learning systems that identify relevant advertising candidates for Reddit users. Retrieval sits at the heart of the ads delivery funnel: before downstream ranking and auction decisions, our models determine which campaigns and ads are eligible to compete. We work on large-scale retrieval across multiple objectives, placements, and geographies. Our work combines representation learning, candidate generation, nearest-neighbor search, behavioral and contextual signals, and rigorous offline and online experimentation. Role Description: We are looking for a Staff Machine Learning Engineer to provide technical leadership for the Retrieval ML team. You will lead the design and evolution of retrieval models and modeling practices that improve relevance, advertiser outcomes, and user experience at Reddit scale. This is an applied ML role centered on retrieval modeling and end-to-end product impact. You will be expected to stay close to the technical details—from data and objective design through model development, evaluation, experimentation, and launch—while setting direction for other engineers. Responsibilities: Define the technical direction and multi-year roadmap for ads retrieval modeling in partnership with engineering, product, data science, and ads stakeholders. Design, develop, and launch candidate-generation and retrieval models for campaigns and ads across Reddit’s advertising surfaces. Apply modern approaches such as two-tower architectures, repres

About the Team DoorDash is building the next generation of causal decisioning systems for New Verticals: grocery, convenience, retail, alcohol, pets, flowers, and other emerging categories. These businesses operate in high-dimensional, dynamic marketplaces where every consumer, merchant, item, promotion, substitution, search result, and delivery promise creates a causal question. We are hiring a Principal Machine Learning Engineer to lead the Causal ML pod and establish the technical foundation for company-level causal decisioning. This is a senior technical leadership role for a practitioner who has built consequential causal systems in production and can turn ambiguous business questions into a coherent measurement and decision platform. A central mandate is to define and build a durable company-level causal value metric: a trusted, long-term signal that estimates the incremental value created by product, growth, and marketplace actions. The metric will connect experiments, observational evidence, and production ML so leaders and product teams can compare investments on a common basis while protecting customer experience and marketplace health. About the Role You will set the multi-year technical direction for causal ML, lead the pod’s portfolio and operating model, and remain close to the hardest modeling and systems work. You will be accountable for both scientific credibility and production impact. You’re excited about this opportunity because you will… Lead the Causal ML pod across technical strategy, architecture, execution, and quality. Create a roadmap that joins foundational platform work with high-value product applications. Define the company-level causal value metric and its measurement framework, including the target construct, time horizon, component outcomes, identification strategy, calibration, uncertainty, and guardrails. Build the metric into a decision system that teams can use for product prioritization, experiment readouts, intervention select

Engineering Manager, Machine Learning Credit Risk 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 Credit Risk team develops intelligent systems that help Stripe identify high-risk accounts, minimize credit losses, and improve profitability. Credit risk is a complex machine learning problem that requires us to distinguish emerging risk from healthy business activity while giving legitimate users a clear and reliable experience. Our team consists of machine learning engineers who build models and systems used across Stripe’s credit-risk products. We work closely with partners in Product, Data Science, Credit Strategy, Operations, and other engineering teams. Together, we help stakeholders make informed decisions and support sustainable growth wherever credit risk affects Stripe’s products. What you’ll do We’re looking for an engineering manager to lead the Credit Risk team and shape how Stripe uses machine learning to manage credit risk at scale. You’ll set the team’s technical and product direction, connect advances in machine learning to measurable business outcomes, and help engineers deliver reliable systems that balance loss prevention with the user experience. You’ll work across engineering, product, data science, and risk to identify the highest-impact opportunities and turn them into a focused roadmap. You’ll also hire and develop engineers, strengthen the team’s technical practices, and contribute to machine learning and engineering leadership across Stripe. Responsibilities Se

Aircall is a unicorn, AI-powered customer communications platform used by 22,000+ companies worldwide to drive revenue, resolve issues faster, and scale customer-facing teams. We’re redefining customer communications by bringing voice, SMS, WhatsApp, and AI together into one seamless workspace. Our momentum comes from a simple idea: help teams work smarter, not harder. Aircall’s AI Voice Agent automates routine calls, AI Assist streamlines post-call work, and AI Assist Pro delivers real-time guidance so people can do their best work. The result is higher revenue, faster resolutions, and teams that scale with confidence. Aircall is headquartered in Paris, our European HQ, with a strong North American presence anchored in Seattle, our North American HQ, and teams across Madrid, London, Berlin, San Francisco, New York City, Sydney, and Mexico City. We’ve built a product customers love and a business that’s scaling quickly, backed by world-class investors and driven by rapid AI innovation across multiple product lines. At Aircall, you’ll join a company in motion. We’re ambitious, product-driven, and execution-focused, with visible impact, fast decisions, and real growth. How we work at Aircall : We’re customer-obsessed, data-driven, and focused on delivering meaningful outcomes. We value ownership, continuous learning, and thoughtful speed. If you thrive in a collaborative, fast-moving environment where trust and impact matter, you’ll feel at home here. Aircall's AI suite includes an AI Voice Agent and AI Messaging Agent that autonomously handle calls, WhatsApp, and SMS, plus AI Assist, which delivers real-time coaching, call summaries, and CRM automation for sales and support teams. We are looking for someone that can build out the evaluation foundation across all of these products and other agentic products. You'll work on voice models, agent capability evals, benchmark design, LLM-as-judge systems, failure analysis, and the infrastructure that ties

We're transforming the grocery industry At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table. Instacart is a Flex First team There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. Overview As a Senior Machine Learning Engineer on the Ads Response Prediction team, you will own and execute the development of ML models that power Instacart's ads ecosystem. This is a research-leaning role focused on theoretical problem formulation, training methodology, and model quality rather than infrastructure or full-stack engineering. You will work on meaningful challenges in pCTR modeling such as mitigating selection bias, position bias, and optimizer's curse in training data, improving model calibration across surfaces and domains, and advancing our multi-task learning and sequence modeling capabilities. You will contribute to our foundation model approach for ads ranking and work on cutting-edge retrieval systems like TIGER (Transformer Index for Generative Recommenders), Semantic ID and domain language models. The Ads Response Prediction team owns all

InMobi (Corporate) InMobi Group is a global technology company shaping the future of agentic commerce and advertising. Through its ecosystem of businesses — including InMobi Advertising and flagship consumer platform Glance — InMobi leverages data, machine learning, and generative AI to help brands reach audiences more precisely and consumers discover products more intuitively. Glance, which is pioneering new models of agentic commerce, is owned and operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of InMobi Pte. Ltd. InMobi Advertising InMobi Advertising, part of global technology company InMobi, is an agentic advertising platform helping brands and merchants achieve their business outcomes. Through its proprietary intelligence, AI-led solutions, and vast consumer reach — including flagship consumer platform Glance — InMobi Advertising delivers the omnichannel performance defining what's next in advertising and commerce. Glance is owned and operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of InMobi Pte. Ltd. To learn more, visit advertising.inmobi.com. Glance Glance is an intelligent shopping agent, redefining the commerce experience. Powered by proprietary agentic intelligence and generative AI, Glance delivers a hyper-personalized consumer experience across mobile and TV — shaping the new era of shopping. Glance is operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of global technology leader InMobi, and is backed by Mithril Capital, Google and Jio Platforms. To learn more, visit glance.com. Overview of the Role: We are looking for a Machine Learning Engineer to develop and optimize our ad science models, open-source LLMs, and the underlying infrastructure that powers them — with a constant eye toward performance and cost. AI and ML sit at the core of InMobi's business, and our science teams are tackling some of the field's most interesting problems: creative optimization, user personalization, targeting, yield m

CSQ427R405 Role overview We are looking for a Lead Learning Product Specialist to own the platform learning experience for Databricks customers, partners, and internal practitioners. You will sit at the intersection of product, UX, and learning science, building the systems that help data and AI teams build skills needed to become successful and confident users of the Databricks platform. You’ll define how users discover, practice, and demonstrate skills across in-product journeys, training, and AI-assisted guidance. You will work closely with engineering, design, curriculum, and go‑to‑market teams to translate pedagogical goals into scalable platform capabilities, from adaptive learning flows and assessments to analytics that prove real skill development. Key responsibilities Define and drive the product vision, strategy, and roadmap for AI‑first learning systems on the Databricks platform, anchored on contextual, in the flow of work enablement and a unified skills infrastructure. Design experiences that surface targeted knowledge precisely when users need it, triggered by real work context (e.g., usage patterns, errors, task types), reducing interruptions and friction. Translate learning and pedagogical goals into platform capabilities such as learner models, skills graphs, progress tracking, adaptive learning paths, assessments, and feedback loops. Partner with UX and content design to create intuitive, embedded guidance and learning patterns that feel like part of the workflow, not separate training destinations. Collaborate with platform and infrastructure teams to ensure learning and skills systems are reliable, secure, performant, and able to serve global enterprise customers at scale. Define and evolve predictive signals and analytics that give proactive visibility into emerging skills and capability gaps, enabling action before they impact business outcomes. Build features and tooling that allow enablement teams to deliver at-scale personalizati