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About DevRev At DevRev, we're building the future of work with Computer – your AI teammate. Unlike traditional tools, Computer unifies all your data sources, tools, and workflows into a single AI-ready platform, giving employees real-time insights, proactive suggestions, and powerful agentic actions. It extends your existing software with AI-native apps and agents that work alongside your teams and customers – updating workflows, coordinating across teams, and eliminating repetitive work. We call this Team Intelligence: human-AI collaboration that breaks down silos, brings people back together, and frees you to solve bigger problems. Backed by Khosla Ventures and Mayfield with $150M+ raised, DevRev is trusted by global companies across industries. About the Role: As a Forward Deployed Engineer at DevRev, you will work closely with our pre- and post-sales Customer Experience team by developing solutions over the core platform via applications written in TypeScript, JavaScript, Python, etc. One of the key functions of this role is, building the Knowledge Graph that includes integration of DevRev with other SaaS and non-SaaS systems through webhooks and APIs and/ or real-time communication architectures. You will be expected to employ your creativity and expertise to ideate and shape how AI, analytics and workflows are used in customers' processes. What You'll Do: Customer-facing: 30% working directly with customers. Coding & Integration: 70-80% hands-on technical implementation Build & Deploy Solutions: Design, develop, and launch AI agents, integrations, and automations that connect DevRev with customers' existing tech stacks and workflows. Integrate Systems: Connect DevRev with SaaS and non-SaaS platforms through APIs, webhooks, and real-time communication architectures for seamless data flow. Optimize AI Performance: Apply prompt engineering, fine-tune semantic search engines, and leverage generative AI techniques to enhance agent ac

About DevRev At DevRev, we're building the future of work with Computer – your AI teammate. Unlike traditional tools, Computer unifies all your data sources, tools, and workflows into a single AI-ready platform, giving employees real-time insights, proactive suggestions, and powerful agentic actions. It extends your existing software with AI-native apps and agents that work alongside your teams and customers – updating workflows, coordinating across teams, and eliminating repetitive work. We call this Team Intelligence: human-AI collaboration that breaks down silos, brings people back together, and frees you to solve bigger problems. Backed by Khosla Ventures and Mayfield with $150M+ raised, DevRev is trusted by global companies across industries. About the team The Applied AI Engineering team ensures our customers get the optimal experience from DevRev. As customers go through their DevRev journey, they may identify needs for integration with existing enterprise systems and services, workflow and process automation, or customization of the DevRev platform to achieve their business objectives. Our team works with customers to understand requirements and design, develop, and implement solutions to meet customer goals. Your mission is to systematically help customers find value with DevRev by developing a thorough understanding of their needs, owning coordination between internal and external stakeholders, and engineering the solution to get the job done. You are a product expert and will use your application development and AI/ML skills to ensure our customers get the most out of the DevRev platform. About the role As a Forward Deployed Architect, you will serve as a hands-on senior technical architect on the Applied AI Engineering team, owning the end-to-end design and delivery of AI-driven business transformation projects. You will work closely with pre-sales teams to scope technical integration and implementation strategies, translating business requirement

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 . Pinterest is seeking an experienced Staff Data Scientist to join our Finance & Business Operations team. This is a hybrid data-science / applied-AI / product-engineering role inside Pinterest's CFO organization. It sits at the intersection of forecasting and finance analytics, internal tool-building, and AI adoption — and the person in it is expected to operate across all three. The core mandate is to make the CFO org's forecasting and planning work faster, be more rigorous, and more self-serve. In practice that has meant owning a forecasting product end to end (data pipeline through user-facing UI), partnering directly with Finance, BizOps, and Core/Monetization stakeholders to embed it in their workflows, and turning the company's emerging AI platform capabilities into tools that finance teams actually use day to day. This is a high-

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. With a billion rides per year and counting, Lyft is solving hard problems in a rapidly growing domain with a lot of data and creative solutions in Rider, Marketplace, Growth, and beyond. While traditional approaches to optimization and problem decomposition are sufficient to disrupt transportation, building a next-generation platform for low-cost, ultra-immersive transportation to improve people's lives warrants modern ML utilizing peta-byte scale data. Our highly motivated Machine Learning Engineers work on these challenging problems and define solutions to directly impact various aspects of our core business. If you are a critical thinker with experience in machine learning workflows and LLMs, passionate about solving business problems using data and working in a dynamic, creative, and collaborative environment, we are searching for you. We are seeking a Senior Machine Learning Engineer to join the Rider Applied AI team and lead the design, development, and deployment of state-of-the-art machine learning and artificial intelligence systems. This role requires a strategic thinker who can balance high-level system architecture with hands-on technical implementation. You will collaborate across teams to shape the future of ride-sharing by leveraging AI, Machine learning and Data science. Responsibilities: Model Development & Research: Design, build, and deploy machine learning models for real-time applications, including translating state-of-the-art research into production-ready solutions. System Design: Architect scalable, reliable ML pipelines that integrate seamlessly with existing backend systems. Innovation & Applied Research: Stay ahead of the curve by exploring emerging algorithms, technologies (such as LLMs and LLM-based applications), and frameworks — critically eva

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. With a billion rides per year and counting, Lyft is solving hard problems in a rapidly growing domain with a lot of data and creative solutions in Rider, Marketplace, Growth, and beyond. While traditional approaches to optimization and problem decomposition are sufficient to disrupt transportation, building a next-generation platform for low-cost, ultra-immersive transportation to improve people's lives warrants modern ML utilizing peta-byte scale data. Our highly motivated Machine Learning Engineers work on these challenging problems and define solutions to directly impact various aspects of our core business. If you are a critical thinker with experience in machine learning workflows and LLMs, passionate about solving business problems using data and working in a dynamic, creative, and collaborative environment, we are searching for you. We are seeking a Senior Machine Learning Engineer to join the Rider Applied AI team and lead the design, development, and deployment of state-of-the-art machine learning and artificial intelligence systems. This role requires a strategic thinker who can balance high-level system architecture with hands-on technical implementation. You will collaborate across teams to shape the future of ride-sharing by leveraging AI, Machine learning and Data science. Responsibilities: Model Development & Research: Design, build, and deploy machine learning models for real-time applications, including translating state-of-the-art research into production-ready solutions. System Design: Architect scalable, reliable ML pipelines that integrate seamlessly with existing backend systems. Innovation & Applied Research: Stay ahead of the curve by exploring emerging algorithms, technologies (such as LLMs and LLM-based applications), and frameworks — critically eva

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, Washington D.C., London and Amsterdam. Our Fraud team's mission is to help companies detect and prevent fraud using Plaid's financial network data. We believe that transaction patterns, device signals, identity linkages, and behavioral data are dramatically underleveraged tools in fraud prevention. Our products — including Protect and Signal — operate at network scale and depend on real-world investigation and research to stay ahead of adaptive adversaries. As a Senior Fraud Researcher, you will sit at the intersection of live fraud investigation, applied data science, and product innovation. You will lead complex investigations, translate findings into detection improvements, and collaborate tightly with Data Science, ML, and Product teams to shape the next generation of Plaid's fraud capabilities. This is not a purely operational role — your research directly drives features, model inputs, and product design. Responsibilities: Live Fraud Investigation & Reconstruction Lead investigations into complex fraud cases across identities, accounts, devices, and transaction surfaces Provide support to day-to-day fraud operations including SEVs and alert triage Reconstruct attacker sequences and hypothesize actor intent and tooling Distill p

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. Our Fraud team's mission is to help companies detect and prevent fraud using Plaid's financial network data. We believe that transaction patterns, device signals, identity linkages, and behavioral data are dramatically underleveraged tools in fraud prevention. Our products — including Protect and Signal — operate at network scale and depend on real-world investigation and research to stay ahead of adaptive adversaries. As a Fraud Intelligence Analyst, you will sit at the intersection of live fraud investigation, applied data science, and product innovation. You will lead complex investigations, translate findings into detection improvements, and collaborate tightly with Data Science, ML, and Product teams to shape the next generation of Plaid's fraud capabilities. This is not a purely operational role — your research directly drives features, model inputs, and product design. Responsibilities: Live Fraud Investigation & Reconstruction Lead investigations into complex fraud cases across identities, accounts, devices, and transaction surfaces Provide support to day-to-day fraud operations including SEVs and alert triage Reconstruct attacker sequences and hypothesize actor intent

Senior Python / Agentic AI Engineer Capco: Where Innovation Meets Opportunity Capco is a global leader in consulting, uniquely positioned to drive innovation and transformation in the financial services industry. More than consultants, we are strategic partners and problem solvers for some of the largest organizations in Banking, Insurance, Investment Management, Data/Digital Transformation, Regulatory Compliance, and ICT Risk. Our business and technology professionals thrive in a dynamic environment where creativity, collaboration, and cutting-edge solutions are at the heart of everything we do. Experience the Capco difference and explore how you can shape your future with us at capco.com . Capco’s AI Lab The AI Lab is a cross-disciplinary research and engineering centre that translates advanced AI research into robust, deployable solutions for clients and internal products. It runs applied research (ML and agentic/LLM systems, planning, simulation and evaluation), rapid prototyping and production engineering (APIs, CI/CD, observability, data platforms and cost/latency optimisation), and rigorous evaluation and safety work (benchmarks, red-teaming, monitoring, governance and compliance). The lab also supports client engagements and internal innovation by building demonstrators and reference architectures, transferring technology to delivery teams, publishing technical learnings, and maintaining the shared infrastructure, tooling and best practices that enable reproducible, secure and scalable AI deployment. Responsibilities Design and implement scalable, production-grade Python services that leverage LLMs and Agentic architectures. Design and implement robust agent workflows and orchestration patterns to automate complex business processes in regulated enterprise environments. Build and maintain reliable AI-enabled backend systems , including orchestration layers, tool integrations, knowledge a

P-1504 The Applied AI team at Databricks sits at the forefront of advancing GenAI-powered products. Over the past years, we’ve launched Databricks Assistant , AI/BI Genie , and Agent Bricks working with product teams, and made significant strides in LLM quality for these products. These products are used by 100s of thousands of Databricks users every day. We are tackling challenging problems like code suggestion, error detection and correction, text-to-sql generation, automatic pipeline generation, knowledge QA and many others. As our GenAI products continue to evolve, we are seeking multiple GenAI Engineers from junior levels to more senior levels to drive the next phase of development. In 2025, we will focus on enhancing LLM quality, expanding GenAI capabilities across Databricks products, and strengthening our platform architecture to enable seamless AI interactions at scale. Key Responsibilities Shape the direction of our applied AI areas and intelligence features in our products . Drive the development and deployment of state-of-the-art AI models and systems that directly impact the capabilities and performance of Databricks' products and services (e.g., Databricks Assistant and AI/BI Genie). Develop novel data collection, fine-tuning, and LLM technologies that achieve optimal performance on specific tasks and domains. Design and implement ML pipelines for data preprocessing, feature engineering, model training, hyperparameter tuning, and model evaluation, enabling rapid experimentation and iteration. Work closely with cross-functional teams, including AI researchers, ML engineers, and product teams, to deliver impactful AI solutions that enhance user productivity and satisfaction. Build scalable, reusable backend systems to support GenAI products across the company. Develop robust logging, telemetry, and evaluation harnesses to ensure reliable model performance. What We’re Looking For 2-8 years of machine learning engineering expe

About the team The Applied team at OpenAI safely brings cutting-edge technology to the world. We have released widely used products such as ChatGPT, Sora, and the OpenAI API, powering models including GPT-5 and a growing set of multimodal capabilities across text, image, audio, and video. Our team also manages large-scale inference and platform infrastructure that supports these experiences at global scale. With much more on the horizon, our impact continues to grow. Our customers build fast-growing businesses using our APIs, unlocking product capabilities that were previously unimaginable. ChatGPT and Sora exemplify the breadth of what’s now possible across text, image, audio, and video experiences. As these capabilities expand, we prioritize the responsible use of our technology, emphasizing safe and thoughtful deployment over unchecked growth. Within Applied Engineering, the Financial Engineering team builds the frontend platforms and workflows that power how OpenAI’s products are priced, purchased, and managed at scale. We work closely with Go-To-Market and Finance partners to translate complex commercial requirements into clear, reusable billing experiences. We’re looking for a frontend engineer to help define and evolve these billing platforms, shaping the primitives, interfaces, and patterns that product teams rely on as our offerings and customer needs continue to grow in complexity. In this role, you will: Create scalable, reusable UI components and patterns that support a wide range of billing use cases across products. Partner closely with backend engineers to deliver end-to-end billing capabilities spanning subscriptions, usage-based pricing, and enterprise offerings. Build tools and surfaces that allow teams like Finance, Sales, Support, and GTM to manage and reason about billing data efficiently. Create AI powered features for billing workflows and platform tooling to automate repetitive tasks, surface insights, and improve decision-making. Help define

P-1408 The Applied AI team at Databricks sits at the forefront of advancing AI/ML-powered products . Databricks’ customers are continuously creating new assets (tables, notebooks, dashboards, datarooms, pipelines, sql queries, ml models etc.) on the platform. Some of them can have hundreds of millions of assets. Finding an asset is a critical user journey for Databricks’ customers which helps them accomplish their tasks. As our Search product continues to evolve, we are seeking a Staff Engineer to lead enhancements to our Search Quality. In 2025, we will focus on enhancing search ranking, improving query understanding, building robust evals and growing the coverage of assets to enable seamless search at scale. Key Responsibilities Drive the development and deployment of ML based search and discovery relevance models and systems integrated with Databricks' products and services. Design and implement automated ML and NLP pipelines for data preprocessing, query understanding and rewrite, ranking and retrieval, and model evaluation, enabling rapid experimentation and iteration. Collaborate with product managers and cross-functional teams to drive technology-first initiatives that enable novel business strategies and product roadmaps for the search and discovery experience. Contribute to building a robust framework for evaluating search ranking improvements - both offline and online. What We’re Looking For BS+ (M.S. or PhD preferred) in Computer Science, or a related field. 10+ years experience developing search relevance systems at scale in production or in high-impact research environments. Experience applying LLM to search relevance Experience in one or more of the following: Query understanding NLP Text mining Recommendations Personalization Discovery Conversational AI Strong understanding of computer science fundamentals. Contributions to well-used open-source projects. Why Join Us? At Databricks,

P-1408 The Applied AI team at Databricks sits at the forefront of advancing AI/ML-powered products. Databricks’ customers are continuously creating new assets (tables, notebooks, dashboards, datarooms, pipelines, sql queries, ml models etc.) on the platform. Some of them can have hundreds of millions of assets. Finding an asset is a critical user journey for Databricks’ customers which helps them accomplish their tasks. As our Search product continues to evolve, we are seeking a Staff Engineer to lead enhancements to our Search Quality. In 2026, we will focus on enhancing search ranking, improving query understanding, building robust evals and growing the coverage of assets to enable seamless search at scale. Key Responsibilities Drive the development and deployment of ML based search and discovery relevance models and systems integrated with Databricks' products and services. Design and implement automated ML and NLP pipelines for data preprocessing, query understanding and rewrite, ranking and retrieval, and model evaluation, enabling rapid experimentation and iteration. Collaborate with product managers and cross-functional teams to drive technology-first initiatives that enable novel business strategies and product roadmaps for the search and discovery experience. Contribute to building a robust framework for evaluating search ranking improvements - both offline and online. What We’re Looking For BS+ (M.S. or PhD preferred) in Computer Science, or a related field. 10+ years experience developing search relevance systems at scale in production or in high-impact research environments. Experience applying LLM to search relevance Experience in one or more of the following: Query understanding NLP Text mining Recommendations Personalization Discovery Conversational AI Strong understanding of computer science fundamentals. Contributions to well-used open-source projects. Why Join Us? At Databricks, w

P-926 The Applied AI team at Databricks sits at the forefront of advancing AI/ML-powered products. Databricks’ customers are continuously creating new assets (tables, notebooks, dashboards, datarooms, pipelines, sql queries, ml models etc.) on the platform. Finding an asset is a critical user journey for Databricks’ customers which helps them accomplish their tasks. As our Search product continues to evolve, we are seeking multiple senior engineers to drive enhancements to our Search Quality. In 2026, we will focus on enhancing search ranking, improving query understanding, building robust evals and growing the coverage of assets to enable seamless search at scale. Key Responsibilities Drive the development and deployment of ML based search and discovery relevance models and systems integrated with Databricks' products and services. Design and implement automated ML and NLP pipelines for data preprocessing, query understanding and rewrite, ranking and retrieval, and model evaluation, enabling rapid experimentation and iteration. Collaborate with product managers and cross-functional teams to drive technology-first initiatives that enable novel business strategies and product roadmaps for the search and discovery experience. Contribute to building a robust framework for evaluating search ranking improvements - both offline and online. What We’re Looking For BS+ (M.S. or PhD preferred) in Computer Science, or a related field. 5+ years experience developing search relevance systems at scale in production or in high-impact research environments. Experience applying LLM to search relevance Experience in one or more of the following: Query understanding NLP Text mining Recommendations Personalization Discovery Conversational AI Strong understanding of computer science fundamentals. Contributions to well-used open-source projects. Why Join Us? At Databricks, we are building state-of-the-art AI solutions that red

About the Team Our Applied team brings OpenAI technologies to consumers and businesses around the world. We collaborate across research, engineering, design and business functions to turn cutting-edge AI advancements into impactful real-world applications. Our team has been behind notable product launches ( ChatGPT , API , Sora ), creating tools that help developers write code, enable businesses to operate more efficiently, and empower individuals to learn and create. As AI capabilities rapidly evolve, we focus on ensuring that our products are safe, accessible, and beneficial to all. About the Role As a Data Scientist on the Applied Product team, you will contribute to a data-driven product development culture for consumer and enterprise products at OpenAI. This is critical as our products reach millions of users and businesses worldwide. We are focused on aligning both research and product development to drive measurable impact for these individuals and organizations alike. You should expect to define our north-star metrics, design A/B tests, and establish source-of-truth dashboards that the entire company can use to answer their own product questions. Most importantly, you should expect to be a core member of the product development team. This role is based in San Francisco, CA or Seattle, WA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Embed with the product development team as a trusted partner, uncovering new ways to improve the product and drive growth Define and interpret A/B tests that help answer critical questions about the impact of model and UX changes to our product Establish a data-driven product development culture by defining, tracking, and operationalizing feature-, product-, and company-level metrics Develop and socialize dashboards, reports, and other ways of enabling the team and company to answer product data questions in a self-serve way You might thrive

Overview: Guidepoint is investing in applied AI across its research platform. We're building conversational AI systems that interact with our global expert network at scale — combining real-time voice, LLM-driven orchestration, and retrieval over large proprietary datasets. You'll be an early engineer on a new initiative: taking working prototypes to production and shaping the architecture as the product scope grows. This is a hands-on role with broad ownership, close to product and leadership. This role is hybrid in our Toronto Office What You'll Do: Real-time conversational AI: voice- and chat-based agents with low-latency requirements, built on modern telephony and real-time media infrastructure. LLM orchestration: agentic systems that pursue goals over multi-turn conversations, with hard behavioral guarantees enforced in code — not just prompts. Multi-channel session management: stateful user interactions that move seamlessly across channels. Retrieval and grounding: connecting LLM systems to large internal knowledge assets, with structured data flowing back. Evaluation infrastructure: measuring conversational quality against human baselines and gating rollouts on metrics, not vibes. Own services end-to-end: design, implementation, deployment (Kubernetes/Azure), and operations, including latency-sensitive real-time workloads. Design and tune LLM pipelines: context engineering, structured outputs, model selection, cost/latency tradeoffs across vendors. Build the eval loop and treat quality as a number: define metrics, run experiments, gate releases on them. Integrate third-party AI and voice vendors behind clean abstractions so providers stay swappable. Partner with product and compliance stakeholders to encode domain rules into the system. Set engineering patterns for a growing team: code review, observability, incident habits. What You Have: 6+ years of software engineering, with 2+ years building LLM-powered products in production (not notebooks): orchestratio