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About the Team DoorDash Labs is an independent team within DoorDash. We explore robotics and automation to transform last-mile logistics in the long term. If you have a passion for applying cutting-edge technology to solve complex problems and enhance delivery experiences for millions of users, we want to talk to you! The Air team is focused on bringing the dream of fast, efficient, and delightful last mile delivery through the air to life. As one of the leaders in this space, we believe strongly in the promise of combining advanced robotics, aviation, and operational innovation to bring this high flying technology into our 3-sided marketplace. We are a team of doers, heavily focused on bringing our products and visions to market. About the Role Are you an engineer passionate about using UAV technology to reimagine logistics? Do you thrive in developing and deploying state-of-the-art systems in real-world environments? Do you love getting your hands dirty and turning large visions into practical realities? If so, we want to talk to you! You're excited about this opportunity because you will... Design and implement backend services for autonomous drone delivery that integrates with core DoorDash data, focusing on intelligent path planning, real-time fleet management, and dynamic airspace awareness. Develop and maintain robust, scalable, and fault-tolerant systems to manage a fleet of delivery drones, ensuring safe and efficient operations. Implement simulations and testing frameworks to validate the performance and reliability of path planning algorithms. Improve performance, reliability, scalability, and security for our drone backend systems, with a particular emphasis on ensuring safe and compliant drone operations. Introduce tools and best practices to accelerate our drone development process and ensure adherence to aviation regulations. We're excited about you because you have... B.S., M.S., or Ph.D. in Computer Science, Robotics, Aerospace Engineering, or

About The Role The Inference Research team is dedicated to building the next generation of efficient, scalable, and reliable serving systems for large foundation models, directly contributing to the mission of advancing open and transparent AI. Our work operates at the critical intersection of cutting-edge model architectures, high-performance systems engineering, and deep hardware optimization. We focus on co-designing software, algorithms, and models to significantly lower the cost and latency of modern AI systems. As a research intern, you will dive into the complexities of distributed inference, compiler-aware optimization, and novel inference-time computation strategies (such as speculative decoding and phase-aware execution). You will be tasked with co-designing and implementing cross-layer optimizations across models, systems, and hardware, with a focus on areas like KV cache design and large-scale serving architectures. Projects aim to unlock unprecedented performance and scale for foundation models, enabling faster serving, larger model deployment (e.g., Mixture-of-Experts), and robust, reproducible evaluation under realistic serving workloads. Responsibilities Design and conduct rigorous experiments to validate hypotheses Communicate the plans, progress, and results of projects to the broader team Document findings in scientific publications and blog posts Requirements Currently pursuing a final year of Bachelor's, Master's, or Ph.D. degree in Computer Science, Electrical Engineering, or a related field Strong knowledge of Machine Learning and Deep Learning fundamentals Experience with deep learning frameworks (PyTorch, JAX, etc.) Strong programming skills in Python Familiarity with Transformer architectures and recent developments in foundation models Preferred Qualifications Prior research experience in foundation models , efficient machine learning , or ML systems . Publications at leading conferences in machine learning or systems (i.e., ML

About The Role The Inference Research team is dedicated to building the next generation of efficient, scalable, and reliable serving systems for large foundation models, directly contributing to the mission of advancing open and transparent AI. Our work operates at the critical intersection of cutting-edge model architectures, high-performance systems engineering, and deep hardware optimization. We focus on co-designing software, algorithms, and models to significantly lower the cost and latency of modern AI systems. As a research intern, you will dive into the complexities of distributed inference, compiler-aware optimization, and novel inference-time computation strategies (such as speculative decoding and phase-aware execution). You will be tasked with co-designing and implementing cross-layer optimizations across models, systems, and hardware, with a focus on areas like KV cache design and large-scale serving architectures. Projects aim to unlock unprecedented performance and scale for foundation models, enabling faster serving, larger model deployment (e.g., Mixture-of-Experts), and robust, reproducible evaluation under realistic serving workloads. Responsibilities Design and conduct rigorous experiments to validate hypotheses Communicate the plans, progress, and results of projects to the broader team Document findings in scientific publications and blog posts Requirements Currently pursuing a final year of Bachelor's, Master's, or Ph.D. degree in Computer Science, Electrical Engineering, or a related field Strong knowledge of Machine Learning and Deep Learning fundamentals Experience with deep learning frameworks (PyTorch, JAX, etc.) Strong programming skills in Python Familiarity with Transformer architectures and recent developments in foundation models Preferred Qualifications Prior research experience in foundation models , efficient machine learning , or ML systems . Publications at leading conferences in machine learning or systems (i.e., ML

What You'll Do Midjourney is seeking physicists to develop revolutionary quantum hardware technologies. What We're Looking For Multiple years of experience working in experimental quantum physics (Ph.D.-level or similar) Proficiency in the design, implementation, and troubleshooting of experiments Hands-on experience with laser-based optical measurements Ability to work on-site (up to five days/week) in the San Francisco Bay Area Given the nature of the work, U.S. citizenship is required. Useful Experience: Broad familiarity with quantum computing and/or sensing platforms Track record of delivering cutting-edge systems that work outside the lab Strong physical intuition about fundamental signal origins and noise sources Hands-on experience with optics, optoelectronics, photodetectors, and optical fibers Familiarity with next-generation photonic technologies Experience inventing new tools and processes to address experimental challenges Proficiency using the latest AI tools to accelerate workflow Excellent data visualization and presentation skills

POSITION SUMMARY: Natera is a global leader in cell-free DNA testing. We measure fetal, tumor, or donor cfDNA at the molecular level to help inform personalized health care decisions with a non-invasive test. The Natera R&D Department is looking for a highly motivated Staff Scientist to join the rapidly growing assay development team to help build the future of Natera. In this position, you will be an integral part of high-performing teams across functions to develop laboratory tests that positively impact the business and our patients. A successful candidate will have a deep understanding of molecular biology, strong scientific curiosity and high level of expertise of developing cfDNA assays through product development cycles. You will lead and support dedicated junior scientists and laboratory technicians to plan, execute and manage scientific studies under design control in a regulated environment to help improve and expand Natera's state-of-the-art product portfolios. PRIMARY RESPONSIBILITIES: Lead technical projects and associated timelines and deliverables as a subject matter expert and represent the assay development group in cross-functional project teams. Drive the planning, design, execution, and reporting of feasibility, development, verification and validation studies under the design control quality system. Collaborate closely with research scientists, automation engineers, biostatisticians and data scientists within a dynamic R&D team to develop new and innovative molecular assays. Communicate and document experimental data, scientific findings, and recommendations to R&D and other functions including Lab Operations, Scientific Affairs, Product Management, Quality, and Regulatory groups. May manage Scientists or Research Associates. QUALIFICATIONS: BS, MS. or PhD Degree in genetics, molecular biology, biochemistry or a closely related field. PhD with a minimum of 6 years relevant experience, or a MS degree and mini

Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities. Tenstorrent is looking for a person ready to take up the challenge of working in a high-profile project where we design and integrate multiple chiplets into a System-in-package, in collaboration with external stakeholders. You will work with Tenstorrent worldwide experts and leaders in the USA, Japan and other countries, and help us make our IP even better. In this role, you will be responsible for DFT implementation using industry standard tools for high-speed CPU core design. This role is remote based out of the United States. We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting. Who you are You have a Bachelor’s, Master’s, or PhD in Electrical Engineering, Computer Engineering, or Computer Science, along with 10+ years of relevant DFT experience. You have hands-on experience with industry-standard DFT tools such as Synopsys and Siemens, as well as scripting in TCL and Python. You bring expertise in DFT planning, SoC-level silicon debug, formal verification, and signoff of inserted DFT logic. You are a strong cross-functional collaborator who can assess risk, communicate resource needs, and deliver in a complex technical environment. What we need Build

The Autonomy Behaviors team is responsible for producing our vehicle’s driving trajectory. As a Principal ML Engineer, you will lead the technical development of machine learning algorithms for our next-generation ML Planner. Your impact will range from delivering innovations for immediate milestones to pioneering exploratory projects. You will collaborate closely with teams specializing in Foundation Models, Perception, Simulation, and Safety Validation, in influencing our autonomy stack. Your role will look at problems in a way that crosses team boundaries to prototype new approaches that influence the long term technical direction of multiple organizations within the company. In this role, you will: Set the technical direction of ML development for the future of our Planner Push the frontiers of imitation learning, reinforcement learning, and model scaling to develop new ML models for our onboard Planner Identify and drive force-multipliers for ML development, including metrics, data, and training frameworks Develop new methods to leverage Zoox’s Foundation Models into the ML Planner Provide technical mentorship to the broader group of ML developers at Zoox Collaborate with engineers across Autonomy Software, Simulation, System Design and Mission Assurance, and other departments to solve the overall Autonomous Driving problem in complex urban environments Qualifications BS, MS, or PhD degree in computer science or related field Proven track record of technical leadership in large-scale AI organizations Significant experience with training and deploying Deep Learning models to production Fluency in C++ or Fluency in Python with a basic understanding of C++ Extensive experience with programming and algorithm design-Strong mathematics skills, 10+ years of experience Bonus Qualifications Conference or Journal publications in Machine Learning or Robotics related v

Our Perception team is responsible for using our sensor data to understand the complex and dynamic environments where we drive. In this role, you will have access to the best sensor data in the world and an incredible infrastructure for testing and validating your algorithms. We are creating new algorithms for segmentation, tracking, classification, and high-level scene understanding, and you could work on any (or all!) of these components. As a Senior Staff ML Engineer, you will lead the development of machine learning algorithms that can range in influence from onboard autonomy to offboard autonomy and validation. You will collaborate closely with other teams specializing in Prediction, Planning, Simulation, and Safety Validation, influencing our overall technical stack. Your role will look at problems in a way that crosses team boundaries to prototype new approaches that influence the long term technical direction of multiple organizations within the company. The impact of the role can be in the form of impacting immediate company milestones to leading forward-looking exploratory projects. Responsibilities Develop new algorithms to understand the scene around the robot, and how that scene would evolve through time Build multi-modal foundation models for on-vehicle and offline applications Develop new algorithms to apply generative AI to simulation to improve the realism of our offline validation systems Leverage our large-scale machine learning infrastructure to discover new solutions and push the boundaries of the field Provide technical mentorship to the broader group of ML developers at Zoox Collaborate with engineers on Prediction, Planning, and Simulation to solve the overall Autonomous Driving problem in complex urban environments Qualifications BS, MS, or PhD degree in computer science or related field Experience with training and deploying Deep Learning models on sensor data-Experience

Internship Details Location: Basel, Switzerland Duration: 6 months full-time (1st July 2027 to 31st December 2027) Eligibility: EU or Swiss students enrolled in a Bachelor's, Master's, or Ph.D. degree program, graduating in 2028, and residing in Switzerland Hybrid arrangement: 3 days onsite at the office, 2 days remote work from home Annual Pay : 96,000 CHF The Opportunity Adobe is seeking dedicated and curious Software Engineer interns to help design, build, and evolve the systems that power our products and platforms. As an intern, you’ll work in an AI-enabled development environment, where modern tools accelerate how software is built and tested. You’ll collaborate with experienced engineers and multi-functional partners, including designers and product managers, to contribute to real-world systems while continuing to build your technical and problem-solving skills. In Basel, we build Adobe Experience Manager (AEM) , the industry leader in Experience Management. AEM helps our customers develop, manage, and deliver high-quality digital experiences for the world’s biggest brands. Join us for an internship in one of several open project areas: Backend Development (Java and/or Node.js), Frontend Development (HTTP, HTML, JavaScript), Cloud Engineering, or Data Science. What's in it for you? Real impact: You’ll participate in important engineering projects that have a direct effect on Adobe’s products and platforms. Work alongside knowledgeable teams, address authentic challenges, and grow skills that count. Unforgettable memories: Our Internship Program is built to accelerate both personal and professional growth through curated learning sessions, mentorship, and peer networking. Authentic cu

Internship Details Location: Bucharest, Romania Duration: 6 months full-time (1st July 2027 to 31st December 2027) Eligibility: Romania-based students enrolled in a: Bachelor's degree program, graduating in 2027 Master's or Ph.D. degree program, graduating in 2027 or 2028 Hybrid arrangement: 3 days onsite at the office, 2 days remote work from home Annual Pay : 130.000 RON The Opportunity Adobe is seeking dedicated and curious Software Engineer interns to help design, build, and evolve the systems that power our products and platforms. As an intern, you’ll work in an AI-enabled development environment, where modern tools accelerate how software is built and tested. You’ll collaborate with experienced engineers and multi-functional partners, including designers and product managers, to contribute to real-world systems while continuing to build your technical and problem-solving skills. Join us for an internship in one of several open project areas. Whether your strength is Java, JavaScript, C&#43;&#43;, or Python, there’s a place for you to contribute and grow. What's in it for you? Real impact: You’ll participate in important engineering projects that have a direct effect on Adobe’s products and platforms. Work alongside knowledgeable teams, address authentic challenges, and grow skills that count. Unforgettable memories: Our Internship Program is built to accelerate both personal and professional growth through curated learning sessions, mentorship, and peer networking. Authentic culture: At Adobe, you’re encouraged to be yourself. Our values: Create the future, Own the outcome, Raise the bar, and Be genuine build a culture where innovation thrives, ownership is celebrated, and authenticity is respected. <br

About the Role The Speculative Decoding team at Together AI builds the draft models and algorithms that let large language models generate faster without changing their outputs. Our speculators run in production on frontier open models such as DeepSeek, Kimi and GLM. Our work sits at the intersection of model training, inference algorithms and serving systems, and we co-design all three to lower the cost and latency of generation. As a research intern, you will dive into open questions in speculative decoding: new drafter architectures and verification schemes, knowledge distillation and training recipes that align draft and target models, and drafters that adapt as traffic shifts. You will train and fine-tune draft models at scale on dedicated GPU clusters, and test your ideas against realistic serving workloads, including long-context and agentic traffic. Projects aim to answer important research questions in speculative decoding, enabling faster generation on frontier models, drafters that keep pace with changing workloads, and robust, reproducible evaluation under real traffic. Past Research Speculative decoding research by Together researchers, interns and academic collaborators includes: Speculative Speculative Decoding (ICLR 2026) When RL Meets Adaptive Speculative Training: A Unified Training-Serving System (ICML 2026) Beat the Long Tail: Distribution-Aware Speculative Decoding for RL Training (MLSys 2026) Responsibilities Research and prototype new efficient speculative decoding algorithms and draft model architectures Train, fine-tune and distill draft models, and design rigorous experiments to validate hypotheses. Implement optimized version for serving engines to show efficacy in real world workloads Communicate the plans, progress, and results of projects to the broader team Share findings through technical write-ups and blog posts, and help bring successful ideas into production Requirements Currently pursuing a Master's or Ph.D. degree in Computer Sci

About the Role The Speculative Decoding team at Together AI builds the draft models and algorithms that let large language models generate faster without changing their outputs. Our speculators run in production on frontier open models such as DeepSeek, Kimi and GLM. Our work sits at the intersection of model training, inference algorithms and serving systems, and we co-design all three to lower the cost and latency of generation. As a research intern, you will dive into open questions in speculative decoding: new drafter architectures and verification schemes, knowledge distillation and training recipes that align draft and target models, and drafters that adapt as traffic shifts. You will train and fine-tune draft models at scale on dedicated GPU clusters, and test your ideas against realistic serving workloads, including long-context and agentic traffic. Projects aim to answer important research questions in speculative decoding, enabling faster generation on frontier models, drafters that keep pace with changing workloads, and robust, reproducible evaluation under real traffic. Past Research Speculative decoding research by Together researchers, interns and academic collaborators includes: Speculative Speculative Decoding (ICLR 2026) When RL Meets Adaptive Speculative Training: A Unified Training-Serving System (ICML 2026) Beat the Long Tail: Distribution-Aware Speculative Decoding for RL Training (MLSys 2026) Responsibilities Research and prototype new efficient speculative decoding algorithms and draft model architectures Train, fine-tune and distill draft models, and design rigorous experiments to validate hypotheses. Implement optimized version for serving engines to show efficacy in real world workloads Communicate the plans, progress, and results of projects to the broader team Share findings through technical write-ups and blog posts, and help bring successful ideas into production Requirements Currently pursuing a Master's or Ph.D. degree in Computer Sci

Cohorts: January & June (Yearly) | Function: Multiple, apply once and tell us where you want to build About us: Tamara is Saudi Arabia's first FinTech unicorn, on a mission to help people own their dreams by building the most customer-centric financial super app in the world. There's no playbook for that; our Tamarians are writing it as we go, and we believe potential should be stretched early. The Internship Program is built for students who don't want to sit on the sidelines. Our internships welcome undergraduate and postgraduate students alike (Master's, MBA, or PhD), and if you're hungry to apply what you've learned in a fast-moving, high-performance environment, this is where you begin. We treat our interns and co-ops as future builders, because in many ways, they already are. This year, we're keeping things simple: one application gets you into the running for any area you're interested in. Just share a bit about yourself, highlight what you're great at, and tell us which teams get you excited. We want you to work on what you're passionate about, so we'll look at your interests to match you with a role where you can jump in and start building. Where You Could Build: Product : Get hands-on with how a fintech unicorn builds products people actually love. You'll support Product Managers in shaping features used by millions of shoppers, drawing on data and customer insight to explore areas for improvement, conceptualise new products, and dive into a wide range of financial services, from personal banking to brand new fintech solutions. Engineering: Write code that ships. As part of our engineering team, you'll work alongside senior engineers on live projects, from feature builds to system improvements, gaining mentorship and real production experience from day one. Design: Help design the experiences behind a product millions of people trust with their money. You'll contribute to real flows and interfaces, working closely with Product and Engineeri

Role Overview As a Research Intern in the Model Shaping team, you will work on one or more of the following areas: Advanced post-training methods across supervised learning, preference optimization, and reinforcement learning New techniques and systems for efficient training of neural networks (e.g., distributed training, algorithmic improvements, optimization methods) Robust and reliable evaluation of foundation model capabilities The Model Shaping team at Together AI works on products and research for tailoring open foundation models to downstream applications. We build services that allow machine learning developers to choose the best models for their tasks and further improve these models using domain-specific data. In addition to that, we develop new methods for more efficient model training and evaluation, drawing inspiration from a broad spectrum of ideas across machine learning, natural language processing, and ML systems. Past research led by Model Shaping interns resulted in the following publications: Escaping the Verifier: Learning to Reason via Demonstrations (ICML 2026) Untied Ulysses: Memory-Efficient Context Parallelism via Headwise Chunking (ICML 2026) ​​ FFT-based Dynamic Subspace Selection for Low-Rank Adaptive Optimization of Large Language Models (ICLR 2026) Responsibilities Research and implement novel techniques in one or more of our focus areas Design and conduct rigorous experiments to validate hypotheses Document findings in scientific publications and blog posts Integrate the research results into Together products Requirements Currently pursuing a Bachelor's, Master's, or Ph.D. degree in Computer Science, Electrical Engineering, or a related field Strong knowledge of Machine Learning and Deep Learning fundamentals Experience with deep learning frameworks (PyTorch, JAX, etc.) Familiarity with the Transformer architecture and recent developments in foundation models Preferred Requirements Prior research experience with training foundation m

Role Overview As a Research Intern in the Model Shaping team, you will work on one or more of the following areas: Advanced post-training methods across supervised learning, preference optimization, and reinforcement learning New techniques and systems for efficient training of neural networks (e.g., distributed training, algorithmic improvements, optimization methods) Robust and reliable evaluation of foundation model capabilities The Model Shaping team at Together AI works on products and research for tailoring open foundation models to downstream applications. We build services that allow machine learning developers to choose the best models for their tasks and further improve these models using domain-specific data. In addition to that, we develop new methods for more efficient model training and evaluation, drawing inspiration from a broad spectrum of ideas across machine learning, natural language processing, and ML systems. Past research led by Model Shaping interns resulted in the following publications: Escaping the Verifier: Learning to Reason via Demonstrations (ICML 2026) Untied Ulysses: Memory-Efficient Context Parallelism via Headwise Chunking (ICML 2026) ​​ FFT-based Dynamic Subspace Selection for Low-Rank Adaptive Optimization of Large Language Models (ICLR 2026) Responsibilities Research and implement novel techniques in one or more of our focus areas Design and conduct rigorous experiments to validate hypotheses Document findings in scientific publications and blog posts Integrate the research results into Together products Requirements Currently pursuing a Bachelor's, Master's, or Ph.D. degree in Computer Science, Electrical Engineering, or a related field Strong knowledge of Machine Learning and Deep Learning fundamentals Experience with deep learning frameworks (PyTorch, JAX, etc.) Familiarity with the Transformer architecture and recent developments in foundation models Preferred Requirements Prior research experience with training foundation m