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Adobe Firefly’s Generative AI Services team is seeking a Senior Staff Machine Learning Engineer for our GenAI Services area. In this high-impact role, you will work with a team of talented engineers in building scalable, high-performance generative AI systems—powering features across Adobe products like Firefly, Photoshop, Illustrator, Express, Stock, and Premiere. You will design and develop efficient inference pipelines, optimize models for latency and through at inference, and build APIs and ecosystems that integrate both Adobe’s first-party and third-party generative models into Adobe suite of products that serve individual and enterprise customers. You will tackle Adobe’s most complex engineering challenges at the forefront of the the industry, set technical direction, and mentor other ML engineers. Job Responsibilities Design and Development of core GenAI services and APIs that integrate a wide range of generative models into Adobe’s flagship products. Design and build ML workflows for enterprise-scale model customization, serving, and ecosystem integration. Collaborate with Adobe Research and other model developer teams with a focus on model inference strategies and productization of those model Build and optimize GPU-accelerated pipelines for both (customized) model training and inference—prioritizing performance, scalability, and reliability. Foster a culture of innovation , technical excellence, and continuous improvement across the organization. What You’ll Need to Succeed MS or PhD in Computer Science, Machine Learning, or a related field—or equivalent industry experience. 10+ years of experience in machine learning, including production-scale deployments. 3+ years of experience leading large-scale, GPU-intensive GenAI systems (training,

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 . As a Machine Learning Engineer at Pinterest, you’ll develop and improve machine learning systems that power product experiences and discovery. You’ll partner with engineering, product and design teams to build, evaluate and iterate on scalable solutions that help Pinners find relevant, inspiring content. In this role, you’ll grow your technical expertise while contributing to reliable, high-impact systems. What you’ll do: Conduct applied machine learning research to address product and platform challenges at Pinterest. Collect, analyze and synthesize data to develop data-driven machine learning models. Write maintainable, efficient code and contribute to testing and code-review practices. Apply machine learning techniques, including natural language processing and graph-based approaches, to modeling and ranking challenges across discovery, ads a

The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, we’re behind some of Spotify’s most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them. Growlithe is a fast-moving squad within Spotify's Conversation Product Area, focused on bringing emerging Search and agentic experiences to life. We explore, define, build, validate, and optimize new ideas to help listeners discover the content they love. Our work spans personalized Search, music video discovery and ranking, audiobook Search expansion, and the foundations of more social Search experiences. We're looking for a Senior Machine Learning Engineer to help shape the next generation of Search at Spotify. You'll work at the intersection of machine learning, personalization, and product development, turning complex challenges into intuitive discovery experiences for millions of listeners. What You'll Do Build and improve machine learning models that make Spotify Search more relevant, personalized, and engaging for listeners. Develop and optimize ranking and backend retrieval systems that help listeners discover music, videos, audiobooks, and other content. <span styl

About the Team DoorDash Drive powers deliveries placed through merchants' own channels—including their websites, mobile apps, and phone orders—using DoorDash's logistics network. The Drive Machine Learning team builds the prediction and intelligence systems that power this business, including delivery and pickup time estimation, merchant prep-time prediction, order release optimization, logistics decision-making, and AI-powered delivery quality signals. Drive presents a unique machine learning challenge. Every merchant has different operational workflows, preparation patterns, and customer expectations, requiring models that generalize across millions of deliveries while adapting to highly diverse merchant behavior. Our team has significant opportunities to improve prediction accuracy, optimize logistics decisions, and build AI-native experiences that directly improve merchant, consumer, and dasher outcomes. About the Role As a Machine Learning Engineer on the Drive team, you'll own machine learning systems end-to-end—from feature engineering and model development to experimentation, deployment, monitoring, and continuous iteration. Your work will span several high-impact problem areas: Build next-generation machine learning models for delivery ETA, pickup ETA, merchant prep-time estimation, and order release prediction that improve reliability for merchants and consumers. Develop deep learning models that leverage large-scale spatiotemporal, marketplace, and behavioral signals to improve prediction accuracy. Apply reinforcement learning and optimization techniques to improve logistics decision-making, assignment strategies, and marketplace efficiency. Build AI-native product experiences using large language models (LLMs) and vision-language models (VLMs). For example, transform pickup photos, item verification flows, receipts, and drop-off images into structured quality signals that help verify orders, prevent delivery defects, and improve issue resolution. Design

About the Team Agentic Foundations, part of New Verticals ML, builds the core AI/ML intelligence that powers high-quality, scalable agents at DoorDash, and proves it through flagship agent experiences such as the Ask grocery assistant. We operate as a closed loop: new models, evals, and representations make our agents better, and real-world agent usage surfaces the gaps that drive our next foundations work. About the Role We’re looking for a Senior Machine Learning Engineer to join Agentic Foundations with a primary focus on Vertical Agent Development. You will own the continuous improvement of our flagship agents, starting with the Ask Assistant, using an eval-driven loop to make them faster, more cost-efficient, and more reliable for millions of customers. You will also help us bring this approach to new agentic experiences for Merchants and Dashers, turning promising ideas into production-ready agents. This is a chance to work at the intersection of LLMs, agents, post-training, and evals on problems that ship. You will partner closely with engineers working on agentic memory, agent-compatible product representations, and post-training of small language models (SLMs), so that what we learn from production agents feeds directly back into our foundations, and what we build there makes our agents better. You will report into the engineering lead on our New Verticals AI/ML team. We expect this role to be hybrid with some time in-office and some time remote. You’re excited about this opportunity because you will… Raise the quality of our Consumer agents: build and refine our eval harness-optimization loop to pinpoint failure modes, then iterate on prompts, tools, skills, and context, and measure the impact on real customer tasks. Find the best balance of quality, latency, and cost: run rigorous experiments, including fine-tuning open-weights models and deploying small language models (SLMs) where they can replace or augment larger LLMs. Launch new agents: take ag

About the Team Come help us build the world's most reliable on-demand, logistics engine for last-mile retail delivery! We're looking for an experienced machine learning engineer to help us develop modern growth and personalization models that power DoorDash's growing retail and grocery business. About the Role We’re looking for a passionate Applied Machine Learning expert to join our team. As a Staff Machine Learning Engineer, you’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the growth and personalization experiences at the heart of our fast-growing grocery and retail delivery business. You will use our robust data and machine learning infrastructure to implement new ML solutions to make the consumer search experience more relevant, seamless, and delightful across grocery, convenience, and many other retail categories. You will demonstrate a strong command of production level machine learning, experience with solving end-user problems, and collaborate well with multi-disciplinary teams. You will report into the engineering manager on our Personalization team. We expect this role to be hybrid with some time in-office and some time remote (#LI-Hybrid). You’re excited about this opportunity because you will… Develop production machine learning solutions to build a world class personalized shopping experience for a diverse and expanding retail space Partner with engineering and product leaders to help shape the product roadmap applying ML Mentor junior team members, and lead cross functional pods to create collective impact You can find out more on our ML blog here We’re excited about you because you have… 8+ years of industry experience developing machine learning models with business impact, and shipping ML solutions to production. Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring an

About the Team The Fulfillment Planning team builds the intelligence that powers DoorDash’s logistics network. We optimize how deliveries are planned and executed across the full delivery lifecycle, improving customer experience, merchant outcomes, Dasher efficiency, and DoorDash profitability. Our mission is to improve fulfillment quality while reducing fulfillment cost. We do this by applying machine learning, optimization, and systems engineering to the core decisions behind assignment, routing, batching, timing, and fulfillment estimation. The team works on some of DoorDash’s most important logistics systems, including: The core assignment engine that matches deliveries with Dashers in real time. Real-time ETA and fulfillment estimation systems for consumers, Dashers, and merchants across diverse geographies and all business lines. Assignment and planning algorithms for specialized delivery types, including grocery, retail, parcel, and catering. ML models and optimization algorithms that shape demand, improve service quality, and reduce cost. Tier-0 logistics services that require high reliability, low latency, and strong operational discipline. The team also builds reusable ML systems and modeling patterns that scale across DoorDash’s logistics ecosystem. This role will help define the technical direction and best practices for logistics ML at DoorDash. About the Role We’re looking for a Staff Machine Learning Engineer to lead the design, development, and deployment of large-scale production ML systems that drive real-time decisioning across DoorDash’s fulfillment ecosystem. You will start by owning ML systems for assignment and fulfillment estimation, partnering closely with Product, Data Science, Engineering, and Platform teams to improve delivery quality, cost, and efficiency. Over time, you may also contribute to adjacent areas such as batching, fulfillment execution, demand shaping, and logistics optimization across DoorDash’s business lines. This is

NVIDIA is leading groundbreaking developments in Artificial Intelligence, High Performance Computing and Visualization. The GPU -- our invention -- serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables groundbreaking creativity and discovery, and powers inventions that were once considered science fiction, including artificial intelligence to autonomous cars. We are the GPU Communications Libraries and Networking team at NVIDIA. We build communication libraries like NCCL, NVSHMEM, and UCX that are crucial for scaling Deep Learning and HPC. We're seeking a Senior Software Architect to help co-design next-gen data center platforms and scalable communications software. DL and HPC applications have a huge compute demands and already run at scales of up to tens of thousands of GPUs. GPUs are connected with high-speed interconnects (e.g. NVLink, PCIe) within a node and with high-speed networking (e.g. InfiniBand, Ethernet) across nodes. Efficient and fast communication between GPUs directly impacts end-to-end application performance. This impact continues to grow with the increasing scale of next generation systems. This is an outstanding opportunity to advance the state-of-the-art, break performance barriers, and deliver platforms the world has never seen before. Are you ready to build the new and innovative technologies that will help realize NVIDIA's vision? What you will be doing: Investigate opportunities to improve communication performance by identifying bottlenecks in today's systems. Design and implement new communication technologies to accelerate AI and HPC workloads. Explore innovative solutions in HW and SW for our next generation platforms as part of co-design efforts involving GPU, Networking, and SW architects. Build proofs-of-concept, conduct experiments,

NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars. We are looking for a motivated Deep Learning engineer to bring advanced communication technologies into AI stacks, including PyTorch, TRT-LLM, vLLM, SGLang, JAX, etc. You will be working with the team that created communication libraries like NCCL, NVSHMEM & technology like GPUDirect -- for scaling Deep Learning and HPC applications. Your customers will have diverse multi-GPU demands, ranging from training on scales up to 100K GPUs to inference down at microsecond latency. Communication performance between the GPUs has a direct impact on AI applications. Your work in AI toolkits will make all of those easier for the community. This is an outstanding opportunity for someone with an AI background to advance the state of the art in this space. Are you ready to contribute to the development of innovative technologies and help realize NVIDIA's vision? What you will be doing: Integrate new communication libraries features in AI frameworks: from PoC to performance analysis to production Perform deep analysis of AI workloads and frameworks to identify multi-GPU communication requirements and opportunities. Collaborate hands-on with teams working on the latest AI models. Improve AI compilers to hide communications or perform automatic fusion. Conduct in-depth AI workload performance characterization on multi-GPU clusters. Design fault-tolerant and elastic solutions for large-scale or dynamic AI workloads. Author

The Zoox 3D Sensor Simulation team’s work is at the core of creating in-the-loop simulation and high fidelity synthetic data for training and testing autonomous vehicle AI. We are looking for a machine learning professional with experience applying ML to sensor data, such as LiDAR, radar, and cameras. This role lies at the intersection of machine learning and 3D simulation at Zoox, ranging from learned sensor models to neural 3D reconstruction. By improving the realism of simulated sensors (lidar, radar, and cameras), you’ll enable the Zoox robotics autonomy stack to learn, improve, and enhance the mobility of humans. In this role, you will: Design, train, and validate ML models that improve the realism of simulated lidar, radar, and camera data in autonomous vehicle (AV) simulation pipelines. Collaborate with Synthetic Data and HIL Sim teams to integrate 3DGS and NeRF-based 3D reconstruction with simulation of all sensor modalities. Build and curate training datasets from real and simulated sensor data, including GenAI-based data augmentation. Partner with Sim V&V and Perception engineers to define sensor realism metrics and close the sim-to-real gaps with the greatest impact on autonomy. Mentor engineers and interns, and establish best practices for training and deploying ML on Zoox sensor data. Qualifications: 2+ years of prior experience as a Technical Lead 5+ years of experience developing ML models in Python with PyTorch or TensorFlow <

Natera’s Research team is building tests that can detect many cancers from a single blood draw. We are looking for a Staff Machine Learning and Bioinformatics Scientist to help design the models at the heart of that effort. You’ll work with large-scale, multi-modal genomic datasets from clinical cohorts and partner closely with molecular biologists and clinical scientists. Your research will advance the state-of-the-art and you will drive new methods from prototype, through scalable implementation, toward clinical validation and, ultimately, to patients. PRIMARY RESPONSIBILITIES: Lead the development of novel statistical and machine learning methods for cancer signal detection on multi-modal genomic data Build interpretable, well-calibrated models, setting the standard for uncertainty estimation and reproducibility across the group Prototype, test and productionize ML models using scalable infrastructure Own key technical deliverables within larger projects and help shape the team’s research roadmap Collaborate closely with molecular biologists and clinical experts on design and analysis of experiments and validation studies Keep raising the bar through code and design reviews across projects Translate complex results into clear stories for technical and non-technical audiences Mentor junior scientists and champion a culture of curiosity, collaboration, and scientific excellence QUALIFICATIONS: PhD in Computer Science, Mathematics, Bioinformatics, or a related quantitative field with 6-12 years of experience post-PhD Hands-on experience developing novel statistical / ML analysis methods applied to complex biological data Proven track record of innovation and technical leadership in complex method development projects Expertise in deep learning and multimodal foundation models Proficient in Python and its scientific computing ecosystem Scientific rigor throughout the R&D process, with eagerness to teach and learn new computational methods and biology Excellent co

Natera’s Research team is building tests that can detect many cancers from a single blood draw. We are looking for a Staff Machine Learning and Bioinformatics Scientist to help design the models at the heart of that effort. You’ll work with large-scale, multi-modal genomic datasets from clinical cohorts and partner closely with molecular biologists and clinical scientists. Your research will advance the state-of-the-art and you will drive new methods from prototype, through scalable implementation, toward clinical validation and, ultimately, to patients. PRIMARY RESPONSIBILITIES: Lead the development of novel statistical and machine learning methods for cancer signal detection on multi-modal genomic data Build interpretable, well-calibrated models, setting the standard for uncertainty estimation and reproducibility across the group Prototype, test and productionize ML models using scalable infrastructure Own key technical deliverables within larger projects and help shape the team’s research roadmap Collaborate closely with molecular biologists and clinical experts on design and analysis of experiments and validation studies Keep raising the bar through code and design reviews across projects Translate complex results into clear stories for technical and non-technical audiences Mentor junior scientists and champion a culture of curiosity, collaboration, and scientific excellence QUALIFICATIONS: PhD in Computer Science, Mathematics, Bioinformatics, or a related quantitative field with 6-12 years of experience post-PhD Hands-on experience developing novel statistical / ML analysis methods applied to complex biological data Proven track record of innovation and technical leadership in complex method development projects Expertise in deep learning and multimodal foundation models Proficient in Python and its scientific computing ecosystem Scientific rigor throughout the R&D process, with eagerness to teach and learn new computational methods and biology Excellent co

Location: Bristol, London or Cambridge, UK About the job Help scale state-of-the art AI models across thousands of accelerators. As a Senior Machine Learning Engineer in the Applied AI team, you will contribute to advancing AI technology by developing and optimising new and existing AI models for our specialised hardware. You will work on large scale systems where performance is critical to the success of our projects. Having visibility of the entire pipeline from novel accelerator hardware to state-of-the-art AI applications, and the software stack in-between, you will play a critical role in identifying opportunities to innovate and differentiate Graphcore’s technology. Furthermore, you’ll work closely with researchers in a rapidly-evolving field, where even the most senior engineers are constantly learning and adapting to exciting new challenges. We seek engineers with strong technical foundations who are curious and eager to understand and advance AI model implementation, at scale. We currently have multiple opportunities available in the team at Senior, Staff and Principal level and offer flexibility through a hybrid working model, from any of our UK offices. If you're excited about advancing the next generation of AI models on cutting-edge hardware, we’d love to hear from you! The team and culture The Applied AI team’s role is to be proxies for our customers, we need to understand the latest AI models, applications, and software, as well as our own hardware and software stack, to ensure that Graphcore’s technology works seamlessly with the AI ecosystem and at scale. Our work spans from low-level kernel development and optimisation for novel hardware through to implementing and scaling research-level algorithms for the latest AI models. We collaborate with the Research team to develop and publish novel ideas in domains such as efficient compute, model scaling and di

Location: Bristol, London or Cambridge, UK About the job Help scale state-of-the art AI models across thousands of accelerators. As a Senior Machine Learning Engineer in the Applied AI team, you will contribute to advancing AI technology by developing and optimising new and existing AI models for our specialised hardware. You will work on large scale systems where performance is critical to the success of our projects. Having visibility of the entire pipeline from novel accelerator hardware to state-of-the-art AI applications, and the software stack in-between, you will play a critical role in identifying opportunities to innovate and differentiate Graphcore’s technology. Furthermore, you’ll work closely with researchers in a rapidly-evolving field, where even the most senior engineers are constantly learning and adapting to exciting new challenges. We seek engineers with strong technical foundations who are curious and eager to understand and advance AI model implementation, at scale. We currently have multiple opportunities available in the team at Senior, Staff and Principal level and offer flexibility through a hybrid working model, from any of our UK offices. If you're excited about advancing the next generation of AI models on cutting-edge hardware, we’d love to hear from you! The team and culture The Applied AI team’s role is to be proxies for our customers, we need to understand the latest AI models, applications, and software, as well as our own hardware and software stack, to ensure that Graphcore’s technology works seamlessly with the AI ecosystem and at scale. Our work spans from low-level kernel development and optimisation for novel hardware through to implementing and scaling research-level algorithms for the latest AI models. We collaborate with the Research team to develop and publish novel ideas in domains such as efficient compute, model scaling and di

Location: Bristol, London or Cambridge, UK About the job Help scale state-of-the art AI models across thousands of accelerators. As a Senior Machine Learning Engineer in the Applied AI team, you will contribute to advancing AI technology by developing and optimising new and existing AI models for our specialised hardware. You will work on large scale systems where performance is critical to the success of our projects. Having visibility of the entire pipeline from novel accelerator hardware to state-of-the-art AI applications, and the software stack in-between, you will play a critical role in identifying opportunities to innovate and differentiate Graphcore’s technology. Furthermore, you’ll work closely with researchers in a rapidly-evolving field, where even the most senior engineers are constantly learning and adapting to exciting new challenges. We seek engineers with strong technical foundations who are curious and eager to understand and advance AI model implementation, at scale. We currently have multiple opportunities available in the team at Senior, Staff and Principal level and offer flexibility through a hybrid working model, from any of our UK offices. If you're excited about advancing the next generation of AI models on cutting-edge hardware, we’d love to hear from you! The team and culture The Applied AI team’s role is to be proxies for our customers, we need to understand the latest AI models, applications, and software, as well as our own hardware and software stack, to ensure that Graphcore’s technology works seamlessly with the AI ecosystem and at scale. Our work spans from low-level kernel development and optimisation for novel hardware through to implementing and scaling research-level algorithms for the latest AI models. We collaborate with the Research team to develop and publish novel ideas in domains such as efficient compute, model scaling and di