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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

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 . Location: Reddit has a flexible first workforce. Don't live near our office? No worries: you can work remotely from anywhere in the UK or the Netherlands. The Ads Foundational Representations (AFR) team develops signals and representations of Reddit’s core entities (ads, posts, users, and so on), capturing the semantic, contextual, and behavioral information that Reddit Ads needs. We work on building embeddings to understand content and users' interests based on the content they engage with. Our team has the potential to highlight one of Reddit's biggest differentiators: genuinely curated, high-quality, extremely relevant, and daily updated organic content. We are a Machine Learning/Data heavy team with a focus on the following areas: Multimodal & Content Embeddings - Make sense of organic (posts, comments, subreddits) and promoted (ads, shopping products, their landing pages) text and media content by embedding them into a shared space. Contextual and Behavioral Relevance - Working with Product & Data Science, establishing definitions of what ads are relevant to users and the content we show them next to, building metrics and fine-tuning embeddings to better reflect relevance. Knowledge Graph Embeddings - Building representations for the Knowledge graph entities, e.g., intellectual properties/brands, to be used for high-precision targeting & business insights. User Intent Modeling - Leveraging various techniques to introduce user representations based on the content they interact with: bat

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 . Location: Reddit has a flexible first workforce. Don't live near our office? No worries: you can work remotely from anywhere in the UK or the Netherlands. The Ads Foundational Representations (AFR) team develops signals and representations of Reddit’s core entities (ads, posts, users, and so on), capturing the semantic, contextual, and behavioral information that Reddit Ads needs. We work on building embeddings to understand content and users' interests based on the content they engage with. Our team has the potential to highlight one of Reddit's biggest differentiators: genuinely curated, high-quality, extremely relevant, and daily updated organic content. We are a Machine Learning/Data heavy team with a focus on the following areas: Multimodal & Content Embeddings - Make sense of organic (posts, comments, subreddits) and promoted (ads, shopping products, their landing pages) text and media content by embedding them into a shared space. Contextual and Behavioral Relevance - Working with Product & Data Science, establishing definitions of what ads are relevant to users and the content we show them next to, building metrics and fine-tuning embeddings to better reflect relevance. Knowledge Graph Embeddings - Building representations for the Knowledge graph entities, e.g., intellectual properties/brands, to be used for high-precision targeting & business insights. User Intent Modeling - Leveraging various techniques to introduce user representations based on the content they interact with: bat

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. As a Distinguished Machine Learning Engineer/Technical Director in the Safety organization at Roblox, you will drive the overall technical vision and execution for all machine learning initiatives focused on maintaining the safety and civility of our users. Our industry-leading safety features ensure Roblox remains a safe and inclusive environment for our community to express themselves creatively and share experiences without fear. The Safety org is the reason why Roblox is the safest place on the internet, protecting users You will provide technical leadership on AI/ML efforts for Trust and Safety as the Roblox platform scales to serve different age groups and geographic locations. You Will: Own the technical direction and implementation of machine learning solutions for safety-related systems Lead and mentor other engineers, fostering a culture of technical excellence and inclusivity Break down long-term product requirements into iterative deliverable stages, ensuring continuous improvement Craft and build large-scale machine learning models with billions of parameters, ensuring production-readiness Facilitate challenging technical decisions across multiple teams, demonstrating empathy a

About the Team & Role: We're building SAGE , Rubrik's Semantic AI Governance Engine, which is the first system designed to monitor, govern, and remediate autonomous AI agents in real time. SAGE powers Rubrik Agent Cloud: enterprises define governance policies in natural language, and SAGE's custom small language models act as judges on every agent action. These models are fast enough to sit in the live request path and accurate enough that customers trust them with allow/block decisions on production traffic. At its core, SAGE is "LLM-as-judge" applied to AI governance, utilizing the same technique most teams use for offline evaluation but productionized for real-time enforcement at enterprise scale. Our first-generation SLM Policy Guard already outperforms the larger frontier models we've benchmarked against on accuracy while running approximately 5x faster on the same workload. We're hiring to push that lead even further. As an Applied ML Engineer on the SAGE team, you'll work end-to-end across the model lifecycle: curating data, training small models, serving them at production latency, and closing the feedback loop with real customer signals. The models you build don't just enforce policies in the live request path; they will also drive Agent Rewind, Rubrik's capability to instantly and precisely undo destructive autonomous-agent actions and restore the affected data to a trusted state. We're a collaborative, applied team that ships models to enterprise customers within weeks, and we're passionate about proving that small, specialized models can outperform frontier LLMs at the problems that matter most for AI safety and governance. Nature of the Specialized Duties ➢ Training, Fine-Tuning, and Distilling Production Small Language Models and Classifiers (25% of time) Owning the full training lifecycle for the SLMs and classifiers in SAGE's real-time enforcement path, including base-model selection, supervised fine-tuning, preference optimization (DPO/RLA

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 . The CX Intelligence team is part of Coinbase’s Enterprise Applications and Architecture org and builds the customer-facing and internal CX experiences that connect the Help Center, chatbots (CBCB), and agent workflows. The team owns the multi-agent platform which powers coinbase chat, Help Center and agent tooling surfaces, partnering closely with Conversation Design, CX, and other Engg teams to deliver secure, compliant, and scalable AI-powered support. Our work provides the trusted and accurate automated and agent-assisted experiences—helping customers get answers faster while enabling human agents to resolve cases more effectively. We are hiring an IC5 Machine Learning Engineer to drive the evolution of our conversational ecosystem by building a seamless hybrid vendor-internal chatbot experience. You will lead the design and implementation of a sophisticated unified orchestration layer that coordinates interactions between vendor AI, internal multi-agent systems, and human participants. This role is pivotal in managing complex state transitions, context sharing, and intent routing across a distributed environment. You’ll thrive here if you enjoy high ownership, architecting complex hand-off logic between disparate LLM frameworks, and building reliable AI-enabled products that are fast, measurable, and scalable. What you'll do: Architect and deploy the orchestratio

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 Data Foundation & AI team within Plaid’s Data organization. Our mission is to build the shared ML and AI infrastructure that powers intelligent capabilities across Plaid’s product suite. We develop the foundational systems, models, and data assets that transform Plaid’s unique financial network data into scalable, general-purpose representations that teams across the company can leverage. Our work spans the full ML lifecycle — from large-scale data curation and model pretraining to production serving, evaluation, and monitoring. As part of the team, you’ll work at the intersection of machine learning infrastructure, applied AI, and distributed systems, helping establish the core AI platform that enables innovation across Plaid. As a Staff Machine Learning Engineer, you will lead the technical strategy and development of Plaid’s foundation models, driving key decisions across pretraining objectives, model architecture, and fine-tuning approaches that power a wide range of downstream product applications. You will serve as the technical lead for the full machine learning lifecycle, overseeing everything from data curation and experimentation to production deployment,

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. As a Senior Software Engineer on the Foundation AI organization, you will sit at the epicenter of our foundation model efforts. While the research world is focused on architecture, you will be the architect of the data flywheel that makes VideoGen and 3DGen possible. You aren't just building pipelines; you are building the infrastructure that defines how our models perceive and generate virtual worlds in three dimensions and across time. In this role, you will partner directly with our AI researchers to advance beyond experimental datasets and into the realm of dynamic, high-fidelity data synthesis and evaluation. You will bridge the gap between research prototypes working locally to scaling for millions of users. You will design, implement, and scale robust, high-performance infrastructure to crawl, create, curate, store, and serve the massive datasets required for these models. We are seeking accomplished software engineers with a passion for data, experience building large distributed systems, and a commitment to writing high-quality, well-tested code to solve complex data challenges at scale. Your contributions will ensure that our foundation models receive the highest quality dat

About the Role Together AI is building the best inference infrastructure for voice applications. Our Voice AI platform powers production-grade, real-time voice agents and applications — serving speech-to-text and text-to-speech models with best-in-class latency and reliability. We're looking for a Staff ML Engineer to drive the model serving layer for voice workloads. You'll work hands-on with inference engines like TRT-LLM and SGLang to optimize how we serve models like Whisper, Parakeet, Orpheus, and Kokoro — pushing latency and throughput to the frontier. You'll profile GPU utilization, design batching strategies for streaming audio, and ensure new model architectures can go from research to production quickly. This is a foundational hire on a small, high-impact team. Voice inference has unique challenges — streaming audio, tokenization, real-time latency budgets — that require dedicated ML engineering focus. You'll shape how Together serves voice models as the industry moves from pipeline architectures (ASR → LLM → TTS) toward end-to-end speech-to-speech. Own the model serving stack that powers Together's voice platform across STT, TTS, and speech-to-speech. Work directly with state-of-the-art accelerators (H100s, H200s, B200s) to optimize voice model inference. Collaborate with model partners (Cartesia, Deepgram, Rime, and others) to bring their models to production on Together's infrastructure. Build quality evaluation frameworks that guide model selection for customers and inform the roadmap. Join a small, early-stage team with outsized impact on a fast-growing product area. Responsibilities Own the voice inference roadmap end-to-end — define and execute the technical strategy for optimizing STT, TTS, and speech-to-speech models across Together's infrastructure, with a clear-eyed view of where the field is heading and how to position the platform ahead of it. Drive best-in-class inference performance — architect and implement systems targeting leading

HackerRank helps companies like NVIDIA, Amazon, and Microsoft hire and upskill the next generation of developers based on skills, not pedigree. Our platform is trusted by over 2,500 of the world’s most innovative companies to build strong engineering teams ready for what’s next. Software has entered an era where humans and AI build side by side. As this shift accelerates, the definition of strong technical talent is changing. We give companies better ways to identify and invest in next-generation skills. People at HackerRank care deeply about the impact of their work and sweat the small details so our customers can be wildly successful with products they genuinely love to use. We move with urgency and believe great outcomes come from high standards. About the role Hiring is one of the most consequential decisions a company makes. 3,000+ enterprises rely on HackerRank to get it right. We are now reinventing how that works for the agentic era. The ML systems that power this platform are not auxiliary features. They are the product. Open Problems The agentic era is reshaping every layer of the hiring stack. These are some of the core problems you'll be working across, none of them fully solved. Chakra: Building an autonomous AI interviewer that conducts, adapts to, and evaluates technical interviews end to end. Integrity: Detecting fraud and suspicious behavior across multiple signal types. The ways candidates game assessments change frequently, and the models need to keep up. Evaluation: Measuring technical skill in a world where AI writes the code. The old proxies no longer hold and the new ones have not been defined yet. Your focus will shift across these depending on where the highest-leverage work is at any given time. What you will do Design and ship production ML systems across Chakra, integrity, and evaluation domains. Own the full ML lifecycle: problem framing, data strategy, experimentation, deployment, and iteration. Build evaluation infrastructure and bench

This is Adyen Adyen provides payments, data, and financial products in a single solution for customers like Meta, Uber, H&M, and Microsoft - making us the financial technology platform of choice. At Adyen, everything we do is engineered for ambition. For our teams, we create an environment with opportunities for our people to succeed, backed by the culture and support to ensure they are enabled to truly own their careers. We are motivated individuals who tackle unique technical challenges at scale and solve them as a team. Together, we deliver innovative and ethical solutions that help businesses achieve their ambitions faster. Senior Machine Learning Scientist Adyen is looking for a Senior Machine Learning Scientist to join our team in Amsterdam, a person sitting at the cornerstone of algorithms, mathematics, and engineering, who can solve problems by designing and implementing production-ready machine learning solutions. You will be responsible for building, developing and deploying algorithms that power data products at Adyen. Teams Currently Hiring: Optimise: Optimize Intelligence at Adyen is a specialized team of Machine Learning Scientists and Data Analysts focused on maximizing transaction success, minimizing transaction costs, and driving payment performance at a global scale. We build and scale advanced ML/DL solutions that operate over massive, complex decision spaces, determining the optimal path for every single transaction in real time. Our systems evaluate high-dimensional data streams to dynamically select the optimal authentication methods, apply smart payment message optimizations, and orchestrate intelligent transaction routing. Our models live-score millions of daily transactions, balancing coupled cost and performance objectives simultaneously. If you are passionate about solving intricate combinatorial challenges and pushing the boundaries of adaptive decision-making systems, join us in shaping the future of payments. Identity an

Zscaler (NASDAQ: ZS) accelerates digital transformation so customers can be more agile, efficient, resilient, and secure. The Zscaler Zero Trust Exchange platform protects thousands of customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location. Distributed across 200+ public data centers globally and thousands of private sites at the edge, the SASE-based Zero Trust Exchange is the world’s largest in-line cloud security platform. We believe the future of work is Human + AI and are building an AI-native enterprise where human potential is amplified by machine intelligence to solve the world’s hardest security challenges. Driven by deep customer obsession, we are committed to the mission, outcome, and to each other. We bring these commitments to life through three core behaviors: ownership and collaboration, trust through outcomes and impact, and a challenge culture with ongoing feedback. Ready to make an impact at the company pioneering security transformation in the AI era? Join us at Zscaler. Role We are looking for a Staff Software Engineer to join our team. This is a Hybrid role, reporting to the Sr. Manager, Software Engineering in the Zscaler Digital Experience (Core Intelligence and Data) department. You will join the team responsible for building the world’s largest cloud security platform, helping us enhance services and increase our global footprint. You will play a pivotal role in enabling organizations worldwide to harness speed and agility through a cloud-first strategy, leveraging our multitenant architecture that serves over 15 million users. What you’ll do (Role Expectations) Own agentic troubleshooting framework, framing high-impact use cases, designing workflows and playbooks, and building processes for all products Evaluate and integrate state-of-the-art GenAI advances to deliver reliable and cost-efficient production features, utilizing LLMs, various machine learning models, data processing, fi

About the Role Together AI is building the best inference infrastructure for voice applications. Our Voice AI platform powers production-grade, real-time voice agents and applications — serving speech-to-text and text-to-speech models with best-in-class latency and reliability. We're looking for a Senior ML Engineer to drive the model serving layer for voice workloads. You'll work hands-on with inference engines like TRT-LLM and SGLang to optimize how we serve models like Whisper, Parakeet, Orpheus, and Kokoro — pushing latency and throughput to the frontier. You'll profile GPU utilization, design batching strategies for streaming audio, and ensure new model architectures can go from research to production quickly. This is a foundational hire on a small, high-impact team. Voice inference has unique challenges — streaming audio, tokenization, real-time latency budgets — that require dedicated ML engineering focus. You'll shape how Together serves voice models as the industry moves from pipeline architectures (ASR → LLM → TTS) toward end-to-end speech-to-speech. Own the model serving stack that powers Together's voice platform across STT, TTS, and speech-to-speech. Work directly with state-of-the-art accelerators (H100s, H200s, B200s) to optimize voice model inference. Collaborate with model partners (Cartesia, Deepgram, Rime, and others) to bring their models to production on Together's infrastructure. Build quality evaluation frameworks that guide model selection for customers and inform the roadmap. Join a small, early-stage team with outsized impact on a fast-growing product area. Responsibilities Optimize inference performance for voice models (STT, TTS, speech-to-speech) — targeting best-in-class TTFB, throughput, and GPU utilization across our curated model set. Productionize voice models on serverless and dedicated endpoints, including batching strategies, streaming inference, and memory management tailored to audio workloads. Build and maintain a voice model

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 Principal 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 deliver

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 . At Reddit, machine learning sits at the heart of how millions of people discover, connect, and engage with the world’s largest collection of human conversations. From powering personalized recommendations and search to optimizing advertising systems and marketplace dynamics, our ML engineers tackle some of the most interesting and impactful problems in large-scale applied machine learning. We are hiring Machine Learning Engineers across our Consumer Engineering organization, giving you the opportunity to work on a wide range of high-impact problems across the Consumer ecosystem. We are looking for Machine Learning Engineers who are excited to build systems end-to-end, from research and modeling to production deployment, and who want to help shape the future of discovery, relevance, and monetization at Reddit. If you love working on complex, real-world ML problems at massive scale, this role is for you. What You’ll Work On We are looking for a Staff Machine Learning Engineer to help drive the next generation of Reddit’s ML ecosystem across recommendations, search, messaging, and foundational AI systems. You will lead high-impact initiatives from ideation to production, shaping both technical strategy and product direction across multiple ML domains. This is a highly cross-functional role partnering with Product, Data Science, and Engineering to deliver meaningful user and business impact. This role sits at the intersection of: Relevance & recommendation systems (content, search, notifications) AI-powered discove