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We offer a hybrid work environment. Most US-based positions can also be performed remotely (any exceptions will be noted in the Minimum Qualifications below.) Our Mission: To actively connect people to their next great opportunity. Who We Are: ZipRecruiter is a leading online employment marketplace. Powered by AI-driven smart matching technology, the company actively connects millions of all-sized businesses and job seekers through innovative mobile, web, and email services, as well as through partnerships with the best job boards on the web. ZipRecruiter has the #1 rated job search app on iOS & Android. Summary : At ZipRecruiter, we sit on a massive universe of data—over a billion archived job postings, tens of millions of dynamic job seekers, and billions of impressions, clicks, and application events. Connecting the right job seeker with the right employer in real time is a complex two-sided marketplace problem , where precision, scale, and latent intent prediction directly impact millions of lives. We are seeking a Staff Machine Learning Engineer / Data Scientist to serve as a technical anchor for our machine learning and AI capabilities. Reporting directly to the Director of Recommendation Systems, you will be a core partner in shaping our multi-year ML roadmap, driving foundational algorithmic architecture, and translating complex machine learning research into high-throughput, low-latency production systems. This is a high-visibility role with org-wide reach. Beyond delivering core algorithmic gains, you will mentor Machine Learning Engineers across the organization and establish best practices for how ML models are built, deployed, and evaluated at scale. Key Responsibilities & Strategic Impact Drive ML Strategy & Roadmap: Partner directly with Engineering and Product Leadership to define and execute the technical vision for core components in the marketplace, including but not limited to recommendation engines and matching alg

About Mach Industries Founded in 2023, Mach Industries is a rapidly growing defense technology company focused on developing next-generation autonomous defense platforms. At the core of our mission is the commitment to delivering scalable, decentralized defense systems that enhance the strategic capabilities of the United States and its allies. With a workforce of approximately 350 employees, we operate with startup agility and ambition. Our vision is to redefine the future of warfare through cutting-edge manufacturing, innovation at speed, and unwavering focus on national security. We are dedicated to solving the next generation of warfare with lethal systems that deter kinetic conflict and protect global security. The Role Mach Industries is building an AI-forward autonomy stack for contested environments where GPS and other sensing are unavailable or unreliable. As a Machine Learning Engineer, you will own and scale the training, data, and edge-inference backbone that every vision and multi-sensor model on our product lines depends on for detection, tracking, search, navigation, targeting, and automatic target recognition. This is a broad, high-ownership role: you'll stand up the data and training infrastructure that lets the autonomy team iterate fast, generate synthetic data to cover the long tail, and get research-grade models running in real time on embedded hardware in flight. We are generalists, so you'll move fluidly between infrastructure, modeling, and deployment. Key Responsibilities Own and evolve the training and data infrastructure the autonomy team builds on: ingestion from flight/sim/HITL, curation and mining, labeling/QA workflows, dataset versioning (DVC/Parquet), and reproducible dataset builds. Stand up and scale training/eval infrastructure: distributed multi-GPU training, experiment tracking, a model registry, and CI-based evaluation with regression gates plus automated field-data to retrain to validate to redeploy loops. Deploy and opt

Why us? Insurify is one of the America’s fastest-growing MIT FinTech startups and has been recognized as one of Inc. 5,000’s fastest-growing private companies in America in 2025, 2024, 2023, 2022 and 2021, Forbes Fintech 50 List for 2023, 2022, and 2021, Forbes Next Billion Dollar Startups of 2022 global and Top 100 InsurTech company. We’re changing the way millions of people compare, buy and manage insurance with artificial intelligence, technology, and superior product design. Our company vision is to be recognized as the preeminent and most trusted digital agent for insurance comparison, purchase, and management. Our team is critical to achieving our vision and fostering the right culture is essential to our team’s success. How you'll make an impact Work and grow in a goal-focused, impact-driven full-stack data science team Partner closely with Marketing to build more sophisticated bidding and targeting across Paid Search, Retargeting, and Email Develop models and algorithms that personalise how results are ranked in the product Spot opportunities, propose experiments, and build models that improve customer conversion What you need to succeed 1+ years of relevant experience Experience building and applying machine learning models with clear business impact Strong Python (numpy, pandas, sklearn) and SQL Strong quantitative and programming skills with a product-driven sensibility Ability to clarify a business problem, identify a good solution, and ship it Curiosity about data and outliers — using data to propose and confirm cause and effect Delivery focus: you get things done within a fixed timeline (math/CS competition background is a plus) Interest in using AI to scale your impact Quick learner with the drive for startup pace Comfortable working iteratively and hands-on in a small team Graduate degree in a technical/quantitative discipline Fluent spoken and written English Benefits 25 days annual leave Enhanced private medical, dental and vision insurance

At Breeze, we're building the AI-powered infrastructure layer for global commerce, making it radically simpler for businesses to sell, get paid, and operate across markets. We go far beyond traditional payment processing. Breeze combines global payments, AI, stablecoins, and a Merchant of Record-like model to take on the complexity businesses typically manage themselves, including compliance, risk, fraud, chargebacks, reconciliation, and customer support. Our goal is simple: let businesses focus on building and selling great products while Breeze handles the complexity behind getting paid. Backed by Sequoia Capital , Multicoin Capital , and The Chainsmokers , Breeze is a successful, rapidly growing, and exceptionally well-capitalized company. We have the runway to think long term while remaining early enough that every person joining today can have a meaningful impact on what we build. We are hiring a Staff Machine Learning Engineer, Risk! As our Staff Machine Learning Engineer, Risk, you'll lead the evolution of our ML platform for payment risk, building the production-grade capabilities behind feature engineering, model training, deployment, monitoring, and continuous improvement. Risk decisions sit at the center of our business, and you'll own how those models get built, shipped, and kept healthy. This role reports to the CTO. You'll work closely with Risk, Software Engineering, and Data Engineering, and you'll be the senior technical voice for ML on the risk team. We're looking for someone who thrives in fast-moving environments, wants meaningful ownership, and is excited to build rather than simply maintain. What You'll Do Design and build ML infrastructure for payment risk detection, using Databricks as the core platform, in close partnership with software and data engineers. Bring structure to the team's ML environment: feature pipelines, versioning, job orchestration, and monitoring. Design and productionize models rather than just prototype them, including

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. The Opportunity We’re looking for an enthusiastic, self-driven Engineering Manager to lead the Personalize Intelligence team. You will lead the team that develops and owns the machine-learning behind Adyen Checkout optimization: our real-time, decision engine that helps merchants to increase their shopper conversion rate to boost seamless checkout experience. It is also the cornerstone of Adyen Uplift, a powerful AI product suite to optimize holistically for conversion, fraud and cost that is used by many of Adyen’s customers today. As an Engineering Manager, you will build, mentor, and coach a diverse and talented team of machine learning scientists and data engineers. You should be comfortable streamlining the team's engineering operations, refining product requirements together with product and merchants and encouraging effective collaboration. As a critical role in the continued success of our organization, you will help to iterate our culture and build an amazing team environment that will create future leaders for Adyen. What You'll Do: Lead the Personalize Intelligence team with hands-on technical leadership, driving architecture strategy and raising the bar through active mentoring and technical guidance. Work cross-functionally with Design, Product, Data Engineers, Machine Learning Engineers, Developers and other key

Senior Machine Learning Engineer, Perception - Autonomous Driving — 3 Locations. Apply via Workday.

We're transforming the grocery industry At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table. Instacart is a Flex First team There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. Overview As a machine learning engineer in the Economics team, you will build state-of-the-art systems that blend rigorous economic thought with sophisticated machine learning algorithms to tackle some of the company's most challenging problems. Working in a horizontal team, you will have the opportunity to collaborate closely with partners across multiple functions to operate on a highly diverse set of problems, contributing both economic and engineering expertise in a fast-paced environment filled with exciting opportunities for technically-minded economists. The Economics team at Instacart works on a range of interesting and challenging problems across our platform, from aligning the incentives in our multi-sided marketplace to analyzing the role of prices and product placement in our customers' decision-making. Some of the core areas of focus for our team inclu

We're transforming the grocery industry At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table. Instacart is a Flex First team There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. Overview As a machine learning engineer in the Economics team, you will build state-of-the-art systems that blend rigorous economic thought with sophisticated machine learning algorithms to tackle some of the company's most challenging problems. Working in a horizontal team, you will have the opportunity to collaborate closely with partners across multiple functions to operate on a highly diverse set of problems, contributing both economic and engineering expertise in a fast-paced environment filled with exciting opportunities for technically-minded economists. The Economics team at Instacart works on a range of interesting and challenging problems across our platform, from aligning the incentives in our multi-sided marketplace to analyzing the role of prices and product placement in our customers' decision-making. Some of the core areas of focus for our team inclu

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 . The Responsible AI team is part of the Advanced Technologies Group (ATG), Pinterest’s advanced machine learning team. ATG’s goal is to keep Pinterest at the forefront of machine learning technology across multiple use cases including recommendations, ranking, content understanding, and more. It is an applied team that works horizontally across the company on state of the art AI and ML and works on directly bringing that technology to the product in collaboration with product engineering teams. The team also publishes its work in applied research conferences, but the main goal of the team is to have a direct impact on business metrics. At Pinterest our goal is to inspire pinners (our users) to live the life they love. The product is powered by state of the art ML algorithms which are used to understand both the billions of visually rich items on

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

NVIDIA is leading company of AI computing. At NVIDIA, our employees are passionate about AI, HPC , VISUAL, GAMING. Our SA team is more focusing to bring NVIDIA new technology into difference industries. We help to design the architecture of AI computing platform, analysis the AI and HPC applications to deliver our value to customers, focusing on defining and solving computational challenges in LLM inference and training acceleration, as well as network communication and data transfer optimization. What You'll Be Doing: Contribute to the development of open-source inference frameworks such as SGLang and vLLM, including feature and operator development, performance optimization, and model support, in collaboration with the community. Develop and optimize KV cache offloading frameworks for LLM workloads, supporting multi-level cache offloading and reuse across CPU, SSD, and remote storage to improve inference efficiency. (Team project: FlexKV) Drive R&D on compute performance in distributed training, and explore methods and technologies for performance optimization. Study computational challenges in machine learning systems, identify common needs and bottlenecks, and build example code, acceleration libraries, or frameworks accordingly. What We Need to See: Over 5 years working experience in the technology industry, with master’s degree or above in computer science, mathematics, electrical engineering, automation, or related fields. Strong interest in accelerated computing, parallel computing, and heterogeneous computing, with the motivation to explore these areas in depth. Solid programming skills, with a good understanding of data structures and computer systems fundamentals. Strong learning agil

Scale works with the industry's leading AI labs to provide high quality data and accelerate progress in GenAI research. We are looking for Research Scientists and Research Engineers with expertise in LLM post-training (SFT, RLHF, reward modeling) and evaluation. This role is on the evaluation pod within the GenAI Research Organization and will focus on building benchmarks and diagnosing model failure modes in both text and multimodal modalities. In this role, you will develop rigorous evaluations and diagnostic methods that reveal where frontier models fail and why. You will collaborate with researchers and engineers to define best practices in evaluation-driven AI development. You will also partner with top foundation model labs to translate failure analysis into technical and strategic input on the next generation of generative AI models. You will: Analyze model behavior to identify, characterize, and diagnose failure modes in frontier LLMs and Agents. You’ll identify everything from capability gaps and reasoning errors to robustness and alignment issues, all focusing on RCA. Design and build benchmarks and evaluation methods that measure LLM capabilities in both text and multimodal modalities. Apply post-training expertise (SFT, RLHF, reward modeling) to connect observed failures to the data and training interventions that address them. Publish research findings in top-tier AI conferences. Ideally you’d have: Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field. Deep understanding of deep learning, reinforcement learning, and large-scale model fine-tuning. Experience with post-training techniques such as RLHF, preference modeling, or instruction tuning, and with LLM evaluation or benchmark development. Excellent written and verbal communication skills. Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals. Previous experience in a customer facing r

Who we are About the team Financial Connections is Stripe's open banking platform, enabling businesses to securely access consumer-permissioned financial data. Our platform connects to thousands of financial institutions, powering use cases from account verification to risk assessment to personal financial management. Across the Financial Connections Engineering org, we focus on delivering high-quality, enriched bank data at scale — building the ML systems that transform raw financial data into actionable signals for both internal Stripe teams and external merchants. Our ML work spans transaction categorization, risk scoring, data enrichment, and the development of intelligent systems that improve data quality across our network. We operate at the intersection of fintech infrastructure and applied machine learning, solving problems that directly impact Stripe's ability to serve millions of businesses and consumers. What you'll do We're looking for machine learning engineers who want to build intelligent systems that provide financial data at scale. You'll play a key role in designing, training, and deploying ML models that improve the quality, accuracy, and usefulness of financial data across Stripe's ecosystem. Responsibilities Design, build, train, evaluate, deploy, and own ML models in production that improve transaction categorization, risk scoring, and data enrichment across Financial Connections Design and build large-scale ML systems that operate on diverse financial data from thousands of institutions Experiment and iterate on ML models (using tools such as PyTorch, TensorFlow, XGBoost) to achieve key business goals around data quality and accuracy Develop pipelines and automated processes to train and evaluate models in offline and online environments Integrate ML models into production systems and ensure their scalability and reliability Collaborate with product, data science, and engineering partners across Stripe to identify opportunities where ML can im

We are now looking for a Deep Learning Software Test Development Engineer Intern! The position is in NVIDIA Deep Learning Software Quality Assurance team that defines, develops and performs tests to validate robustness and measure the performance of NVIDIA‘s Deep Learning software and GPU Infrastructure for autonomous driving, healthcare, speech recognition, natural language processing, and a wide variety of other AI scenarios. This team collaborates with multiple AI product teams to develop new products; derive and improve complex test plans; and improve our workflow processes for a diverse range of GPU computing platforms. You should grow with being in the critical path supporting developers working for billion-dollar business lines as well as intimately understanding the values of responsiveness, thoroughness and teamwork. You should constantly foster and implement efficiency improvements across your domain. Join the team which is building software which will be used by the entire world! What you’ll be doing: Be responsible for functionality, compatibility, and performance tests in NVIDIA AI SW stack release. Develop, maintain, and improve test automation infrastructure with using AI tools. Use AI to improve quality and productivity across QA End-to-End workflow. Work with development teams to triage issues, root cause analysis, verify fixes, define new tests, improve test plans. What we need to see: Pursuing MS or higher degree in CS/EE/CE or a related field. Proven success in leveraging AI tools to significantly improve efficiency, streamline workflows or enhance process automation. Good python, C++ programming skillset, Linux knowledge is required. Experience in software development with popular AI model

PagerDuty (NYSE:PD) is a leader in Digital Operations Management. In an always-on world, organizations of all sizes trust PagerDuty to help them deliver a perfect digital experience to their customers, every time. Teams use PagerDuty to identify issues and opportunities in real time and bring together the right people to fix problems faster and prevent them in the future. Over 13,000 organizations (including 60 of Fortune 100) rely on PagerDuty to succeed with Digital Transformation, Cloud Migration, and DevOps Modernization. Notable customers include GE, Cisco, Genentech, Electronic Arts, Cox Automotive, Netflix, Shopify, Zoom, DoorDash, Lululemon and more. We are expanding rapidly as a platform for Digital Operations Management using AI/ML and Automation and growing our adoption by Development, IT, Customer Service, Security, and other teams across the organization. PagerDuty is looking for a Machine Learning Engineer who is passionate about collaborating with data scientists, product managers and engineers alike. As part of our team, you will help us accelerate the development and extension of products powered by Gen AI and many other shapes of Machine Learning. You’ll be contributing hands-on to the development of the services and pipelines that enable multiple ML/AI features in our product. You will have the opportunity to collaborate with multiple organizations, taking input and guidance from your senior stakeholders and helping bring our initiatives to reality. You’ll succeed by showcasing excellent capacity to manage time, demonstrating emotional intelligence as you navigate stakeholder relationships, and by continuously improving your technical skill set. Key Responsibilities Build and improve the capabilities that enable and accelerate the production of machine learning (ML) and generative AI (genAI) based solutions Partner with data scientists, effectively sharing engineering context and collaborating to support larger initiatives Incorporate the best ava