Senior Ai Ml Data Model Architect Jobs

Our mission at Duolingo is to develop the best education in the world and make it universally available. It’s a big mission, and that’s where you come in! At Duolingo, you’ll join a team that cares about educating our users, experimenting with big ideas, making fact-based decisions, and finding innovative solutions to complex problems. You’ll have limitless learning opportunities and daily collaborations with world-class minds — while doing work that’s both meaningful and fun. Join our life-changing mission to develop education for our half a billion (and growing!) learners around the world. Read our blog to learn more. About the role You will... Derive insights from data to influence product roadmaps and drive business results Apply advanced analytical methods, structural approaches, causal inference, and/or machine learning and AI to model user behavior and estimate the impact of potential new feature implementations, and collaborate with product and engineering teams to develop solutions Evaluate product experiments to create and improve user experiences Communicate and present findings and recommendations to senior management Help preserve our culture of fact-based decision making as we scale You have... Pursuing a PhD degree (in at least the third year of PhD program) in a quantitative field (e.g. Economics, Finance, Statistics, Math, Linguistics, Computer Science, Computational Social Science, ML/AI), ideally planning to graduate in Fall 2027 or Spring/Summer 2028 Strong understanding of consumer digital products and an interest in applying advanced analytics and economics to business outcomes Previous experience analyzing large datasets, communicating results to technical and non-technical senior-level audiences, and making actionable recommendations that improve the product or business Skilled in SQL, R or Python, and/or statistical methods to analyze behavioral data Enthusiasm for learning and sharing analytical methods to h

Who we are: Sardine is the leading agentic risk platform for fighting financial crime. Our integrated solution unifies data across risk teams to help organizations stop fraud in real time, prevent AI-driven attacks, and automate fraud and AML operations. Sardine’s platform is strengthened by one of the fastest-growing fraud consortiums in the market, spanning more than 6 billion profiled devices, 800 million consumers, and 3 million businesses worldwide. Leading companies including FIS, GoDaddy, Intuit, Edward Jones, ZoomInfo, and Checkout.com rely on Sardine to secure and grow trust in their products. Our culture: We hire talented, self-motivated individuals with extreme ownership and high growth orientation. We value performance and not hours worked. We believe you shouldn't have to miss your family dinner, your kid's school play, friends get-together, or doctor's appointments for the sake of adhering to an arbitrary work schedule. We're a remote-first team spread across time zones, so no office to report to - work from wherever helps you do your best work. Just a couple of things to keep in mind: pay is based on where you're located, and you'll need to keep a home base in the country you're hired in. So while we love the "coffee shop today, mountains tomorrow" life, this isn't a passport-optional, work-from-anywhere-on-Earth kind of remote - think flexible within your country, not borderless. Location: Remote - United States or Canada About the role We are looking for a Senior Data/ML Engineer to own the data and machine learning foundation that Sardine's compliance decisions run on. Every onboarding decision we make — a payment approved, an account blocked, a KYC case escalated — is the output of a pipeline someone built. This role owns those pipelines end to end: how data arrives, how it becomes a feature, how that feature becomes a model, and how that model stays correct in production. This is a high-impact, highly technical IC role sitting at the intersection

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 Snorkel Snorkel AI is the frontier AI data lab, helping teams build the data and environments behind high-performing frontier and agentic AI. We combine technology with research-driven AI data development to create datasets, benchmarks, evals, and custom solutions for real-world AI systems. Founded out of the Stanford AI Lab in 2019, Snorkel works with leading AI labs and enterprises to move from better data to better outcomes. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler! About the Role Snorkel AI is hiring a Forward Deployed Engineer focused on Synthetic Data Generation to partner with leading AI labs and enterprises on their most critical AI initiatives. In this role, you will lead the technical execution of complex customer engagements where synthetic data is used to improve model training, evaluation, and performance. You will translate ambiguous model and data challenges into effective data strategies, build scalable generation and evaluation pipelines, and use experimentation to continuously improve data quality and downstream model outcomes. You will work across the full delivery lifecycle—from technical discovery and solution design through implementation, evaluation, and production delivery. You will also identify patterns across engagements and turn successful approaches into reusable capabilities, technical standards, and product improvements. Main Responsibilities Synthetic Data Generation & Evaluation Design and build scalable synthetic data generation, transformation, filtering, and evaluation pipelines for complex AI use cases Translate model objectives, failure modes, and data gaps into synthetic data strategies, experiments, and technical specifications Develop LLM- and ML-assisted workflows to generate high-quality training and evaluation datasets across targeted behaviors, domains, and edge cases Build automated evaluators, quality checks, and measurement frameworks to assess correctness, relevance

Perception data is core to how Zoox’s robotaxis understand the world, and as Senior Manager of Perception Data, you will own the flywheel that turns that data into safer, smarter driving. You will lead our perception data organization, spanning data science, data engineering, and data labeling, and own how we mine logs, automate annotation, feed high value data into our models, and close the loop through analysis and measurement. The most valuable examples in our corpus are often the hardest to find: rare behaviors, unusual interactions, safety critical events, and the long tail of real world urban driving. Your role is not only to build the systems that process this data, but to decide what data matters and why, and to build the learning loop that lets Zoox improve model performance faster. This is an opportunity for a leader who blends strategic thinking with strong execution, has done it before at scale, and knows how to partner with ML leaders, metrics pipelines, and infrastructure teams. In this role, you will... Own the perception data flywheel. Run the learning loop from real world fleet signals and model behavior through data discovery, curation, and enrichment into training, evaluation, and measurement. Lead multidisciplinary teams. Manage and develop a 10+ person team of data science, data engineering and data labeling, setting priorities and raising the bar on execution. Automate annotation at scale. Drive auto annotation and auto labeling pipelines, reducing manual cost while improving label quality and throughput. Build advanced data miners. Develop intelligent approaches to surfacing rare, surprising, and safety critical scenarios in very large datasets, using techniques such as embeddings, semantic search, learned representations, and model driven data selection. Own log selection and storage. Define how we select, store, and retrieve fleet logs so the right data reaches our models efficiently and economicall

Zafin is an AI platform company helping regulated institutions modernize how critical work is designed, governed, and delivered. Our technology enables organizations to move faster while maintaining the governance, accountability, and control required in highly regulated environments. Our portfolio includes Zafin AIOS , an agent orchestration platform for governed AI work; the Zafin Banking Platform , which helps banks modernize product, pricing, offers, billing, loyalty, and relationship management; and Zafin IO , an integration platform that connects data, systems, and workflows across complex enterprise environments. Headquartered in Toronto, Canada, Zafin partners with leading financial institutions across North America, Europe, the Middle East, Africa, and Asia-Pacific. As AI transforms the future of financial services, we're building the platforms that help regulated organizations adopt AI responsibly and at scale. What is the Opportunity? As a Machine Learning Engineer, part of Zafin's AIOS Product team , you will build the machine learning and AI systems that power the AIOS Knowledge Fabric and help agents reason over enterprise knowledge with accuracy, traceability, and control. You will work on practical production systems, including retrieval, ranking, embeddings, model integration, evaluation, data pipelines, model serving, observability, and the workflows that make AI outputs reliable for regulated banking environments. This is a hands-on engineering role focused on turning ML and LLM capabilities into dependable product infrastructure. This role is critical for high-quality knowledge, strong evaluation, and reliable model behavior. You will help build the foundation that allows customers to use AI agents with confidence, accountability, and measurable business value. What Will You Do? Design, build, test, and maintain ML and AI capabilities for AIOS, with a focus on knowledge retrieval, model integration, and producti

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

At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most. On the ML Fraud team, you’ll build and improve machine learning systems that make real-time transaction decisions, protecting consumers and merchants while balancing fraud loss, customer experience, and conversion. You’ll work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring as fraud patterns evolve. What you’ll do - You will lead development of new fraud prediction models using a mix of approaches for tabular, graph, and behavioral data - You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed. - You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls. - You productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness. - You will instrument and monitor model and data health, and help define retraining/backtesting workflows as fraud patterns evolve. - Identify and implement foundational improvements to how the team builds models. - You will collaborate across Engineering, Fraud Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences. What we look for - You have 6+ years experience researching, training, tuning and launching ML models at scale. Relevant PhD can count for up to 2 years of experience. - Track record of delivering high impact machine learning models in a low latency live setting - Strong Python skil

About the Team The Ads & Promos Delivery team powers the last-mile delivery of ads and promotions, two marketing products offered to merchants, connecting merchant intent with consumer demand across search and discovery experiences. As a Senior Staff Engineer, you will lead the technical direction for AI-first experiences, including ranking and relevance systems that sit at the core of our ads marketplace and shape how ads are selected, ordered, and personalized in real time across all verticals. You will design and build next-generation AI-first ranking systems using state-of-the-art techniques such as sequence modeling, deep learning, and large language models (LLMs). Your work will span query understanding, user and merchant representation learning, contextual relevance, and multi-objective optimization, balancing advertiser value, consumer experience, and marketplace health at scale. You will set the long-term technical vision, drive cross-team alignment, and translate cutting-edge research into production systems that operate under strict latency, scale, and reliability constraints. As DoorDash expands into 40+ global markets and new verticals such as Grocery and Retail, this role offers a rare opportunity to define how modern AI, including sequential models and LLM-powered decisioning, reshapes ads relevance in a closed-loop marketplace. About the Role Apply state-of-the-art machine learning and LLM techniques to problems across personalization, query understanding, user and content understanding. Rigorously evaluate ML and LLM models using a combination of offline analysis and online experimentation, designing metrics and experiments that clearly measure quality, impact, and tradeoffs. Own the full model lifecycle from research to production, including data analysis, model development, evaluation, offline and online A/B testing, and continuous iteration. Partner closely with product managers, data scientists, and designers to ensure AI-driven systems deli

Who We Are At Justworks, you’ll enjoy a welcoming and casual environment, great benefits, wellness program offerings, company retreats, and the ability to interact with and learn from leaders in the startup community. We work hard and care about our most prized asset - our people. We’re helping businesses get off the ground by enabling them to focus on running their business. We solve HR issues. We’re data-driven and never stop iterating. If you’d like to work in a supportive, entrepreneurial environment, are interested in building something meaningful and having fun while doing it, we’d love to hear from you. We're united by shared goals and shared motivations at Justworks. These are best summed up in our company values, which are reflected in our product and in our team. Our Values If this sounds like you, you’ll fit right in. Department Technology, Data & Analytics Team Who You Are You’re a data scientist with experience in building and deploying machine learning solutions for complex, and sometimes ambiguous, business problems. You have worked end-to-end on the ML model lifecycle, from early data discovery and analysis to deploying and maintaining models in production. You believe that the best data science work accelerates learning, improves decision making, and ultimately drives measurable impact for customers and the business. Justworks is looking for a Senior Data Scientist to join our ML group within the Data & Analytics team. You'll primarily partner with Product and Engineering to identify and frame domain-specific use-cases as data science problems, prototype and evaluate the appropriate technical solutions, and work with our Machine Learning Engineers to deploy them into production and maintain them. Your Success Profile What You Will Work On Lead end-to-end machine learning and data science projects: Partner with product and engineering leaders to identify and scope new data science use-cases within their domains such as Risk &

Join Truecaller – The place where innovation meets impact! Truecaller's mission is to build trust in communication by making it safer, smarter, and more efficient. Born in Sweden, trusted by the world, and here’s why we stand out: We are trusted by over 500 million active users every month across 190+ countries We identify over 15 billion calls daily, helping users avoid spam and scams We are powered by a team of 400+ employees from 45+ nationalities We always look for people who take initiative, own their work, and keep raising the bar. An entrepreneurial mindset matters here, especially when it turns bold ideas into real actions. We stay collaborative and focused, always searching for smarter paths forward. If you want to make an impact and grow with a team that inspires millions, you’ll fit right in. The role: As a Senior ML Engineer, you will be working hands-on to optimise the training and deployment of ML models to be quick and cost-efficient. You will also be at the forefront of putting our ML models on mobile devices to enhance data privacy and customer experience. To achieve this, you will need to collaborate with teams across Truecaller to establish best practices and tools for efficient ML model development and deployment, particularly on mobile platforms. You will be expected to help Truecaller reach and remain at the cutting edge of ML training and deployment, and to explore new frontiers such as federated learning. What you will do Understand the current and future needs of developers and the organisation, and create a roadmap with your manager for meeting those needs. Work with teams across Truecaller, enabling them to deploy models to production quickly and cost-effectively. Work hands-on with teams throughout Truecaller to optimise ML models for mobile devices. Be the go-to expert for developing models for mobile devices as well as deploying them. Staying up to date with the latest trends and technologies in ML Engineering, especially ML on mobile p

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 @ Roblox today supports hundreds of ML use cases and billions of inferences per day across Discovery, Safety, Engine, and much more. As an Infrastructure Engineer on the ML Platform team, you will design, scale, and maintain the foundational infrastructure powering our entire machine learning ecosystem. We are looking for accomplished engineers to spearhead the development of our next-generation ML tooling and platform capabilities. You will: Bootstrap and maintain Kubernetes and Cloud infrastructure for ML Platform components--Serving Layer, Metadata Store, Model Registry, and Pipeline Orchestrator. Set technical strategy and oversee development of high scale and reliable infrastructure systems. Propose and implement new platform tooling to improve time to production for MLEs and Data Scientists across the full ML lifecycle. Work on infrastructure projects such as GPU fleet management, hybrid-cloud orchestration, and writing custom Kubernetes controllers and resources. Stay abreast of industry trends in machine learning and infrastructure to ensure the adoption of leading-edge technologies and practices. Partner across organizations to build tooling, interfaces, and visualizati

#Team Nextdoor Nextdoor (NYSE: NXDR) is the essential neighborhood network. Neighbors, public agencies, and businesses use Nextdoor to connect around local information that matters in more than 350,000 neighborhoods across 11 countries. Nextdoor builds innovative technology to foster local community, share important news, and create neighborhood connections at scale. Download the app and join the neighborhood at nextdoor.com . Meet Your Future Neighbors As Data Scientists at Nextdoor, you will have the opportunity to influence strategic decisions with product analytics, drive better experimentation and build online and offline ML solutions. We use a semi-embedded team structure, with data team members interfacing directly with product and engineering stakeholders. The Data Science group is made up of people from a diverse set of backgrounds and perspectives, trained in fields as wide-ranging as economics, psychology, geography, physics, statistics, and operations research. We are the mathematical decision scientists for the product development organization and play a proactive and collaborative role in building and improving the product. At Nextdoor, we operate in an AI-first environment and expect every team member to actively use AI tools as part of their workflow. We aren't looking for prompt engineers; we’re looking for people who use tools like Claude, Gemini, ChatGPT, and Glean to challenge their own thinking and take full ownership of AI-assisted outputs. We also offer a warm and inclusive work environment that embraces a hybrid employment model, blending an in office presence and work from home experience for our valued employees. The hiring team will go over these expectations with you if you are being considered for a role near one of our offices in San Francisco, Los Angeles, Chicago, Dallas, New York, and London. The Impact You’ll Make As a Senior Data Scientist on the Search team, you will shape how neighbors find the people, places, posts, and business

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Role Overview: We’re looking for a senior engineer to help build, maintain and evolve the training framework that powers our frontier-scale language models. This role sits at the intersection of large-scale training, distributed systems, and HPC infrastructure. You will design and maintain the core components that enable fast, reliable, and scalable model training — and build the tooling that connects research ideas to thousands of GPUs. If you enjoy working across the full stack of ML systems, this role gives you the opportunity and autonomy to have massive impact by working on projects such as: Building a high-performance data loading and caching pipeline. Implementing performance profiling across the ML systems stack Developing internal metrics and monitoring for training runs. Building reproducibility and regression testing infrastructure. Developing a performant fault-tolerant distributed checkpointing system. Key Responsibilities: Build and own the training framework responsible for large-scale LLM training. Design distributed training abstractions (data/tensor/pipeline parallelism, FSDP/ZeRO strategies, memory management,