Learning And Development Manager Jobs

About the Team DoorDash’s GenAI Platform team sits within Machine Learning Platform and builds the shared infrastructure that helps DoorDash, Wolt, and Deliveroo teams safely bring GenAI-powered products, agents, automation, and personalization to production. Our mission is to increase the velocity of business impact from GenAI. A central pillar of that work is our evaluation platform — the unified evals backbone that lets teams measure, trace, and trust the quality of LLM and agent systems across the company, powering trace/score ingestion, LLM-as-judge workflows, agent simulations, and LLM observability for the tens of millions of daily requests flowing through our LLM Gateway. We also own core platform surfaces including the Agent Gateway, open-weights model serving and batch inference, guardrails, and cost attribution. About the Role You will join a small, high-leverage team building production infrastructure for Generative AI at DoorDash, with a primary focus on our evals and LLM observability platform: the systems that let teams evaluate, trace, and continuously improve the quality of LLM and agent products. You’ll work across evaluation frameworks and SDKs, OpenTelemetry-based trace/score ingestion, LLM-as-judge and offline/online eval pipelines, agent simulations, data pipelines, backend services, and observability. This role is ideal for an engineer who enjoys building reliable measurement and quality primitives in a fast-moving technical area where product needs, model capabilities, vendor ecosystems, and evaluation methodologies are evolving quickly. You’re excited about this opportunity because you will… Build the infrastructure that helps DoorDash teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company. Work on our unified evals platform — evaluation SDKs, OpenTelemetry trace/score ingestion, LLM-as-judge, offline and online eval pipelines, and agent simulations — alongside the LLM Gatew

About the Team DoorDash’s GenAI Platform team sits within Machine Learning Platform and builds the shared infrastructure that helps DoorDash, Wolt, and Deliveroo teams safely bring GenAI-powered products, agents, automation, and personalization to production. Our mission is to increase the velocity of business impact from GenAI. A central pillar of that work is running frontier open-weight LLMs and VLMs (such as GLM, Qwen, Kimi, and DeepSeek) ourselves — real-time GPU serving, high-throughput batch inference, and fine-tuning on autoscaling GPUs — delivering large cost and latency wins (for example, a billion embeddings produced roughly 20× cheaper and visual models served roughly 72% cheaper). We also own core platform surfaces including the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution. About the Role You will join a small, high-leverage team building production infrastructure for Generative AI at DoorDash, leading the design and architecture of our open-weights model platform spanning inference and fine-tuning: real-time GPU serving, high-throughput batch inference, and model fine-tuning. You’ll set technical direction across model serving and inference engines, fine-tuning and training pipelines, GPU autoscaling and utilization, batch pipelines, backend services, and observability, and mentor engineers as you go. This role is ideal for a senior engineer who enjoys owning ambiguous, high-impact systems and pushing the cost/performance frontier of GPU inference and fine-tuning in a fast-moving technical area where product needs, model capabilities, vendor ecosystems, and cost/performance tradeoffs are evolving quickly. You’re excited about this opportunity because you will… Lead the design of infrastructure that helps DoorDash teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company. Own and evolve our open-weights serving stack — real-time GPU endpoints, high-thr

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

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

Notice to applicants: We have seen a rise in recruitment scams. Please note that outreach from Khan Academy recruiters will only come from the @khanacademy.org domain. Our team does not use Gmail or other personal accounts for contacting potential candidates. Khan Academy will never solicit money, equipment fees, or sensitive financial information at any stage of the hiring process. We also do not work with external recruiting agencies, so outreach from headhunters presenting opportunities on behalf of Khan Academy is illegitimate. Please always check the email domain and cross-reference the position with the official Khan Academy Careers page to confirm an opening is valid. ABOUT KHAN ACADEMY Khan Academy is a nonprofit with the mission to deliver a free, world-class education to anyone, anywhere. Our proven learning platform offers free, high-quality supplemental learning content and practice that cover Pre-K - 12th grade and early college core academic subjects, focusing on math and science. We have over 181 million registered learners globally and are committed to improving learning outcomes for students worldwide, focusing on learners in historically under-resourced communities. ABOUT KHAN ACADEMY KIDS Khan Academy Kids is a free and fun learning program for children ages 2 to 8. Kodi Bear and a cast of animated characters lead children on a personalized education journey filled with fun, standards-aligned activities in early literacy, math, executive functioning and social emotional skills. Built by a small but mighty team, Khan Academy Kids is used by millions of children in homes and classrooms around the world. OUR COMMUNITY Our students, teachers, and parents come from all walks of life, and so do we. Our team includes people from academia, traditional/non-traditional education, big tech companies, and tiny startups. We hire great people from diverse backgrounds and experiences because it makes our company stronger. We value diversity, equity,

About the Team The ChatGPT product team is a rapidly evolving, high-impact group within OpenAI that builds intuitive, safe, and useful AI-powered experiences for millions of people worldwide. Our team brings together engineering, design, research, and product to explore how conversational AI can help people learn, create, and solve problems. AI has the potential to transform how millions of people learn, teach, and achieve their goals. Learning is one of the top use cases on ChatGPT— not only for students, but also for adults building new skills in a rapidly changing world. The Education team is focused on advancing how humans learn with AI and working to make high-quality learning accessible. We aim to deliver measurable gains in cognition and achievement, partnering closely with students, educators, and country leaders to ensure new tools are safe, effective, and trusted. About the Role As a Product Manager for Education & Learning, you will shape the strategy and build products to advance learning experiences in ChatGPT— defining a category that could reshape classrooms, careers, and lifelong learning. You’ll collaborate with cross-functional partners—from AI research to design to go-to-market—to build ChatGPT into a true learning agent and ensure those products you build meet the needs of stakeholders throughout the ecosystem. You will also partner closely with product teams across growth, youth well-being, and enterprise to ensure alignment and maximize impact. 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 new employees. In this role, you will: Define and drive the product vision for education and learning in ChatGPT. Partner with research teams to explore how AI can better advance learning outcomes Collaborate with cross-functional teams—including design, engineering, product, and go-to-market—to bring education features to life. Partner with xfn growth, GTM, and

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 on a mission to bring everyone the inspiration to create a life they love. The Applied Science team plays a critical role in this mission by developing cutting-edge machine learning solutions that scale across all of Pinterest engineering teams (see our team’s publications ). We're looking for a highly technical Engineering Manager with a deep understanding of modern recommendation systems to manage, lead and develop a team of machine learning researchers and engineers within the Applied Science team. In this role, you will help the team build a portfolio of work which can balance that addresses both immediate short-term business needs and long-term strategic breakthroughs. You will partner with senior leaders to evolve our technical roadmap and directly drive Pinterest’s core mission forward. W

The MongoDB Atlas team is a diverse group of contributors working together to help our users manage MongoDB at global scale. We are responsible for MongoDB Atlas: our database as a service offering and fastest growing product which allows users to deploy fault-tolerant, globally distributed MongoDB clusters in just minutes. We're seeking a Senior Engineer to join the Atlas Identity and Access Management (IAM) team. IAM is a platform and a product team. We serve internal engineers by providing them a secure and durable suite of services, and we serve external customers by providing them user facing features and products. We are the owners of Atlas’ authentication (OAuth, SSO, Federated Identity) and authorization (RBAC, ABAC) systems, along with many others. The IAM team’s mission is to enable customers to securely build their applications with Atlas through our best in class user experience. We are looking to speak to candidates who are based in New York City, NY for our hybrid working model. Role Responsibilities Design, architect, build, and deliver core pieces of IAM Lead projects from specification to delivery Mentor and grow other team members Improve our codebase, best practices, and design principles Define your top priorities and focuses, communicate them, and execute against them Lead and contribute to complex technical projects and initiatives Candidate Profile 5+ years experience of software engineering, primarily focused on backend systems Proficient in a modern compiled programming language (Java, Go, C#, C++, etc.) Willingness to learn JavaScript and/or TypeScript along with modern frontend technologies (React, Redux, etc.); prior experience a plus Excellent communication skills, both written and verbal Desire to collaborate with colleagues and mentor fellow engineers Is curious, collaborative, empathetic, and intellectually honest Has a passion for problem solving and learning new things in the domains of computer science and software engineering Expe

The Applied AI team designs and builds algorithmically driven features in the Datadog app. We work across a range of applications, primarily focusing on analysis on streaming data such as anomaly detection, error outliers and faulty deployment analysis. As an Applied Scientist you will work on building models and algorithms for machine learning powered features within the Datadog platform. You will work closely with our engineering and product partners to explore, build, scale and deliver these features that we incubate within the Applied AI team. At Datadog, we place value in our office culture - the relationships that it builds, the creativity it brings to the table, and the collaboration of being together. We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them. What You’ll Do: Design solutions for our different use cases. You will research and benchmark relevant algorithms to find the best fit for our use-cases Leverage machine learning algorithms and statistical techniques to build new scalable product features Develop, deploy and monitor new and existing features to production Participate in our journal club by reading and presenting the latest academic research papers to the team Explore, analyze and tell the story behind high volumes of data flowing through Datadog systems Maintain and monitor the models, services and infrastructure owned by your team Participate in your team’s on-call rotation Who You Are: You have a BS/MS/PhD in a Computer Science, Engineering, Machine Learning or related scientific field or equivalent experience You have experience working with high-scale systems and datasets including building models, applying machine learning to real business problems, and writing production data pipelines You can explain complex ideas and algorithms to non-technical audiences You care about code simplicity and performa

The Applied AI team designs and builds algorithmically driven features in the Datadog app. We work across a range of applications, primarily focusing on analysis on streaming data such as anomaly detection , error outliers and faulty deployment analysis . As an Applied Scientist you will work on building models and algorithms for machine learning powered features within the Datadog platform. You will work closely with our engineering and product partners to explore, build, scale and deliver these features that we incubate within the Applied AI team. At Datadog, we place value in our office culture - the relationships that it builds, the creativity it brings to the table, and the collaboration of being together. We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them. What You’ll Do: Design solutions for our different use cases. You will research and benchmark relevant algorithms to find the best fit for our use-cases Leverage machine learning algorithms and statistical techniques to build new scalable product features Develop, deploy and monitor new and existing features to production Participate in our journal club by reading and presenting the latest academic research papers to the team Explore, analyze and tell the story behind high volumes of data flowing through Datadog systems Maintain and monitor the models, services and infrastructure owned by your team Participate in your team’s on-call rotation Who You Are: You have a BS/MS/PhD in a Computer Science, Engineering, Machine Learning or related scientific field or equivalent experience You have experience working with high-scale systems and datasets including building models, applying machine learning to real business problems, and writing production data pipelines You can explain complex ideas and algorithms to non-technical audiences You care about code simplicity and performance You are excited to work on

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Snowflake is looking for an accomplished AI Deployment Specialist to lead the sales of our custom AI solutions. This is a strategic position that will involve close collaboration with the field organization, Product Sales team, Industry team, and AI Product team. The goal is to ensure successful customer adoption of Snowflake's AI and Machine Learning capabilities, achieved through custom-developed solutions that address critical and transformative business problems, delivering significant ROI. RESPONSIBILITIES : Achieve pipeline generation and revenue targets for allocated accounts and/or territory on a quarterly and annual basis by developing a sales strategy in the allocated territory with a target account list. Execute tailored sales plays to capture market opportunities in AI/ML, helping customers optimize their use of Snowflake's AI platform. Work directly with customers to understand their AI/ML needs, communicating insights to inform Snowflake’s product roadmap and ensuring alignment with customer requirements. Become an expert in Snowflake’s Artificial Intelligence and Machine Learning solutions and provide executive-level insights to customers and partners, guiding strategic discussions that emphasize the value of Snowflake’s AI/ML capabilities and solutions to ad

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Snowflake is seeking an accomplished AI Product Sales Specialist to drive sales execution for our AI/ML workload within the Enterprise market. This is a strategic role that works closely with the broader field organization to ensure the successful customer adoption of Snowflake’s Artificial Intelligence and Machine Learning capabilities. RESPONSIBILITIES Achieve pipeline generation and revenue targets for allocated accounts and/or territory on a quarterly and annual basis by developing a sales strategy in the allocated territory with a target account list Execute tailored sales plays to capture market opportunities in AI/ML, helping customers optimize their use of Snowflake's platform. Work directly with customers to understand their AI/ML needs, communicating insights to inform Snowflake’s product roadmap and ensuring alignment with customer requirements. Become an expert in Snowflake’s Artificial Intelligence and Machine Learning solutions and provide executive-level insights to customers and partners, guiding strategic discussions that emphasize the value of Snowflake’s AI/ML capabilities. Work with our Account Executives, Sales Engineers and Field CTOs to drive customer engagements from discovery and qualification through solution implementation and deployment Track, an

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! The integration team is responsible for developing and scaling machine learning algorithms and infrastructure for LLM post-training, with a focus on large-scale, distributed RL methods. We strive for excellence in both engineering and science by meticulously designing experiments and design docs. While tasks are assigned according to everyone’s expertise, there is a global team effort to write production code and support the team research efforts, depending on individual interests and organizational needs. In particular, this role aims to enhance the global quality of the post-training codebase by implementing new tools to ease and support research, optimizing post-training algorithms, and scaling distributed RL to unprecedented levels. Please Note: We have offices in London, Paris, Toronto, San Francisco, New York but we are also remote-friendly! Applicants for this role may work anywhere between UTC−06:00 and UTC+01:00. As a Member of Technical Staff, you will: Design and write high-performing and scalable software for training models. Develop new tools to support and accelerate research and LLM training. Coordinate with other

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. We are hiring a Staff Research Scientist, Physical AI for our AI Research team . You will build the next-generation training and learning platform for physical AI: models that perceive, reason about, and act within structured environments . This is a greenfield (0 to 1) effort at the intersection of representation learning, world models, and policy optimization. You will help define its technical direction from day one. AS A STAFF RESEARCH SCIENTIST YOU WILL: Design and build scalable training infrastructure for representation models (e.g., contrastive and self-supervised approaches like CLIP/SigLIP, DINO/MAE, and joint-embedding predictive architectures) Develop latent world models that learn environment dynamics through imagined rollouts, enabling model-based reasoning and planning (Dreamer-style, I-JEPA/V-JEPA families) Architect and implement action/policy model pipelines, including vision-language-action models and diffusion-based policy learning Build generative simulator frameworks that produce controllable, physically plausible future states (video world models in the spirit of Cosmos/Genie/Sora) Develop multimodal generative model capabilities that fuse visual, language, and structured inputs for downstream reasoning and decision-making Lead cross-team technical de

Human Data Quality Analyst, AI Business Prolific Prolific isn’t just enabling AI innovation – we’re redefining it. While foundational AI technologies are becoming commoditized, Prolific’s human data infrastructure provides the high-quality, diverse data required to train the next generation of AI models. Through our platform, we empower researchers and companies to access a global, ethically curated participant base, ensuring cutting-edge AI research and training grounded in inclusivity and precision. The Role Prolific provides the human data that powers the next generation of AI models, working with frontier labs to capture the complex human judgments researchers need to train, evaluate and improve them. As a Human Data Quality Analyst, you'll be on the front line of making sure that data captures the right signal and is genuinely good enough to do its job. This isn’t traditional, back-office QA. You’ll be doing real analytical work: digging into datasets, identifying patterns and failure modes, investigating why quality has shifted, and turning complex findings into clear insights that help us improve how data is collected, reviewed and delivered. You'll spend real time reading annotations closely, but that is how you gather evidence, not what you produce. What you produce is analysis, practical recommendations and better quality controls. You'll get hands-on exposure to human data, annotation, machine learning pipelines and AI evaluation, working alongside Quality, Engineering, Operations and Delivery on new and evolving problems. There won't always be an established playbook. You'll be guided by our quality engineers, but you'll also need to run your own analysis, test your assumptions and recognise when you need input. It is a role with a steep learning curve from day one and a strong opportunity for someone early in their career to build deep, practical experience in a fast-moving area of AI. What You’ll Be Doing Run day-to-day qualit