Systems Phd Jobs

The SCG Architecture team is hiring a Senior Power Integrity Co-Design Engineer to architect and deliver di/dt mitigation across silicon, package, board, and platform. This role bridges architecture, silicon, and platform — translating product noise targets into shipped specifications, and feeding silicon findings back into the next generation's build. Success in this role requires strong systems thinking and a willingness to accept ambiguity. It also requires the ability to apply AI as a force multiplier while maintaining rigorous engineering judgment. What you'll be doing: Architect voltage-noise mitigation across the full stack — silicon, package, board, platform — and own the codesign trade-offs between them. Co-design noise features with Speed, Power, Reliability, Circuit Design , Power-Arch, ASIC, and platform teams. You're the connective tissue across the codesign web. Work with other team members to define product-level voltage noise targets, drive them to closure, and sign them off at shipment. Build and take ownership of the Sim-to-Si correlation methodology for noise. You know when a model is lying and when silicon is. Model and prototype next-gen noise features — transient sense, droop response, mitigation IP, and codify them so every future program inherits them. Lead show-stopper noise bugs during bringup. The critical issues stop with you. Drive architecture-level codesign tradeoffs across V/F Power Noise Reliability Thermal (Noise-Variation) and (Noise-to-Closure) boundary work, where the highest-leverage innovation lives. What we need to see: BS / MS / PhD in EE, CE, or related (or equivalent experience). 5+ years in silicon power integrity, voltage noise, or PDN. Deep expertise in at least one of

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role We are seeking a highly skilled Physical Design Engineer with deep expertise in physical design and methodology. This individual contributor role sits within our physical design team and is central to delivering power, performance, and area (PPA) optimized datapath and interconnect solutions for next-generation AI accelerators. You’ll work closely with RTL designers to define and execute on physical design strategies. You will develop tools, flows and methodologies to increase team productivity. Your work will directly impact silicon’s performance and cost efficiency, as well as the team’s execution velocity and quality. In this role, you will: Develop, build and own tools, flows and methodologies for physical implementation Own physical implementation of floorplan blocks from floorplanning to final signoff Collaborate with RTL designers to drive optimal block implementation solutions Analyze and optimize design for timing, power, and area trade-offs, working in collaboration with EDA vendors and ASIC partners Qualifications: BS w/ 4+ or MS with 2+ years or PhD with 0-1 year(s) of relevant industry experience in physical design and methodology development Demonstrated success in taping out complex silicon designs Hands-on experience with block physical implementation and PPA convergence Strong coding experience with python, bazel, TCL Strong experience building physical design tools, flows and methodologies Strong understanding of microarchitecture, RTL design,

NVIDIA is looking for Senior Networking (ETH/IB) Solutions Architect to join its NVIDIA Infrastructure Specialist Team. Academic and commercial groups around the world are using NVIDIA products to revolutionize deep learning and data analytics, and to power data centers. Join the team building many of the largest and fastest AI/HPC systems in the world! We are looking for someone with the ability to work on a dynamic customer focused team that requires excellent interpersonal skills. This role will be interacting with customers, partners and internal teams, to analyze, define and implement large scale Networking projects. The scope of these efforts includes a combination of Networking, System Design and Automation and being the face to the customer! What you'll be doing: Primary responsibilities will include building AI/HPC infrastructure for new and existing customers. Support operational and reliability aspects of large-scale AI clusters, focusing on performance at scale, real-time monitoring, logging, and alerting. Engage in and improve the whole lifecycle of services—from inception and design through deployment, operation, and refinement. Maintain services once they are live by measuring and monitoring availability, latency, and overall system health. Provide feedback to internal teams such as opening bugs, documenting workarounds, and suggesting improvements. What we need to see: BS/MS/PhD or equivalent experience in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, or related fields. At least 5+ years of professional experience in networking fundamentals, Ethernet or InfiniBand World. Hands-on experience with network switch/router platforms like Cumulus Linux, SONiC, IOS, JunosOS, and EOS, etc. Possess solid working knowl

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. Lyft’s Data Science Team builds mathematical models underpinning the platform’s core services. Compared to other technology companies of a similar size, the set of problems that we tackle is incredibly diverse. They cut across optimization, prediction, modeling, inference, transportation, and mapping. We're looking for Masters or PhD students who are passionate about solving mathematical problems with data and are excited about working in a fast-paced, innovative and collegial environment. We are hiring for a variety of Data Science interns, focusing on the following specialties: Optimization: Construct and fit statistical or optimization models that facilitate automated decision making in the app. Machine Learning: Design, build, tune, and deploy machine learning models with a special emphasis on feature engineering and deployment. Inference: Design and analyze tests in our dynamic marketplace, estimating statistical and ML models to enable better decisions, and developing and evaluating algorithmic policies in our pricing, dispatch, and incentives systems. You will report into a Science Manager. Responsibilities: Partner with Engineers, Product Managers, and other cross-functional partners to frame problems, both mathematically and within the business context Perform exploratory data analysis to gain a deeper understanding of the problem Write production modeling code; collaborate with software engineers to implement algorithms in production Design and run both simulated and live traffic experiments Analyze experimental and observational data; communicate findings including working with partner teams and presentations; facilitate launch decisions Experience: Currently pursuing a Masters or PhD degree at a university in Canada (required) in mathematical sciences ( Opera

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. Lyft’s Data Science Team builds mathematical models underpinning the platform’s core services. Compared to other technology companies of a similar size, the set of problems that we tackle is incredibly diverse. They cut across optimization, prediction, modeling, inference, transportation, and mapping. We're looking for Masters or PhD students who are passionate about solving mathematical problems with data and are excited about working in a fast-paced, innovative and collegial environment. We are hiring for a variety of Data Science interns, focusing on the following specialties: Optimization: Construct and fit statistical or optimization models that facilitate automated decision making in the app. Machine Learning: Design, build, tune, and deploy machine learning models with a special emphasis on feature engineering and deployment. Inference: Design and analyze tests in our dynamic marketplace, estimating statistical and ML models to enable better decisions, and developing and evaluating algorithmic policies in our pricing, dispatch, and incentives systems. You will report into a Science Manager. Responsibilities: Partner with Engineers, Product Managers, and other cross-functional partners to frame problems, both mathematically and within the business context Perform exploratory data analysis to gain a deeper understanding of the problem Write production modeling code; collaborate with software engineers to implement algorithms in production Design and run both simulated and live traffic experiments Analyze experimental and observational data; communicate findings including working with partner teams and presentations; facilitate launch decisions Experience: Currently pursuing a Masters or PhD degree at a university in Canada (required) in mathematical sciences ( Opera

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. Lyft’s Data Science Team builds mathematical models underpinning the platform’s core services. Compared to other technology companies of a similar size, the set of problems that we tackle is incredibly diverse. They cut across optimization, prediction, modeling, inference, transportation, and mapping. We're looking for Masters or PhD students who are passionate about solving mathematical problems with data and are excited about working in a fast-paced, innovative and collegial environment. We are hiring for a variety of Data Science interns, focusing on the following specialties: Optimization: Construct and fit statistical or optimization models that facilitate automated decision making in the app. Machine Learning: Design, build, tune, and deploy machine learning models with a special emphasis on feature engineering and deployment. Inference: Design and analyze tests in our dynamic marketplace, estimating statistical and ML models to enable better decisions, and developing and evaluating algorithmic policies in our pricing, dispatch, and incentives systems. You will report into a Science Manager. Responsibilities: Partner with Engineers, Product Managers, and other cross-functional partners to frame problems, both mathematically and within the business context Perform exploratory data analysis to gain a deeper understanding of the problem Write production modeling code; collaborate with software engineers to implement algorithms in production Design and run both simulated and live traffic experiments Analyze experimental and observational data; communicate findings including working with partner teams and presentations; facilitate launch decisions Experience: Currently pursuing a Masters or PhD degree in mathematical sciences ( Operations Research, Computer Science, Statistic

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. Lyft’s Data Science Team builds mathematical models underpinning the platform’s core services. Compared to other technology companies of a similar size, the set of problems that we tackle is incredibly diverse. They cut across optimization, prediction, modeling, inference, transportation, and mapping. We're looking for Masters or PhD students who are passionate about solving mathematical problems with data and are excited about working in a fast-paced, innovative and collegial environment. We are hiring for a variety of Data Science interns, focusing on the following specialties: Optimization: Construct and fit statistical or optimization models that facilitate automated decision making in the app. Machine Learning: Design, build, tune, and deploy machine learning models with a special emphasis on feature engineering and deployment. Inference: Design and analyze tests in our dynamic marketplace, estimating statistical and ML models to enable better decisions, and developing and evaluating algorithmic policies in our pricing, dispatch, and incentives systems. You will report into a Science Manager. Responsibilities: Partner with Engineers, Product Managers, and other cross-functional partners to frame problems, both mathematically and within the business context Perform exploratory data analysis to gain a deeper understanding of the problem Write production modeling code; collaborate with software engineers to implement algorithms in production Design and run both simulated and live traffic experiments Analyze experimental and observational data; communicate findings including working with partner teams and presentations; facilitate launch decisions Experience: Currently pursuing a Masters or PhD degree in mathematical sciences ( Operations Research, Computer Science, Statistic

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. What you’ll be doing: Use and develop AI-powered tools to make software testing smarter, faster, and more effective! Improve test case generation, defect detection, flaky test analysis, regression testing, and test coverage optimization. Work with product, engineering, and cross-functional teams to review requirements and define test strategies. Build test plans, design and execute test cases, and report quality status, risks, bugs, and results. Perform functional, performance, fault-injection, reliability, and regression testing for cloud-native systems. Automate test cases and contribute to scalable test frameworks. Manage the bug lifecycle, reproduce customer issues, and verify fixes. What we need to see: MS or PhD in Computer Science, Engineering, or a related field. 5+ years of QA, test automation, or software testing experience. Hands-on experience using AI tools to improve QA workflows. Strong QA fundamentals, test strategy, test planning, and failure analysis skills. Proficiency with Unix/Linux and shell or Python programming. Exp

Bits Code is Datadog’s cloud coding agent which fixes the problems that Datadog surfaces, as automatically as possible. You’ll shape the architecture, tools, processes, and culture that keep security risk very low while working hands-on with the team to ship product features quickly. At Datadog, we place value in our office culture - the relationships that it builds, the creativity it brings to the table, and the collaboration of being together. We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them. What You'll Do: Own the security architecture and implementation of Bits Code from early design through production operation. Work with engineering, product, infrastructure, and security teams to identify risks and shape the product. Build threat models for new and existing agent risks such as prompt injection. Help the team ship secure software fast by writing and maintaining core libraries, and guiding processes and culture. Work with our sandbox teams on how Bits Code executes untrusted workloads. Be on call. Datadog culture is that you build it, you run it. Who You Are: You have a BS/MS/PhD in Computer Science, Engineering, or a related scientific field, or at least 5+ years of professional experience, with a background in product security, application security, or security architecture. You have experience securing systems that operate across complex trust boundaries. You have experience building and operating full stack systems at scale. You are familiar with security risks in agentic systems and have experience designing against them. You make pragmatic security trade-offs. Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you're passionate about technology and want to grow your skills, we encourage you to apply. Benefits and Growth: Skilled, knowledgeable, and kind teammates to work with a

Why Work with Us Solve impactful problems. At Seven Research, our close-knit team unites brilliant minds passionate about pushing technological and scientific boundaries in the global markets. We provide state-of-the-art resources and cultivate intellectual freedom, enabling our people to develop groundbreaking solutions to the most complex challenges of our field. Our collaborative approach celebrates excellence from everyone, regardless of tenure. Your Role Our Algorithmic Developers are researchers and engineers who transform complex quantitative concepts into high-performance trading strategies and systems that are efficient, stable, and scalable. You will be continuously challenged to think creatively while conducting rigorous analysis and improvements regarding market dynamics, strategy behavior, and system performance. If you are excited by the prospect of solving these sorts of problems in a collaborative, research-driven environment, we would love to get to know you. What We’re Looking For We seek talented engineers committed to the best algorithmic development practices. No financial knowledge is expected. You should have excellent programming and analytical skills and be excited to apply these talents within our state-of-the-art research and trading infrastructure. You should be motivated to take ownership over core projects and relentlessly drive decisive improvements. The ideal candidate will possess most of the following: PhD, master’s, or bachelor’s degree in computer science or a related field Strong background in data structures and algorithms, and mastery of C++ Applied knowledge of statistical analysis. Passion for solving complex quantitative problems through technology Strong communication skills to articulate complex technical concepts Job Compensation Range The base salary range for this role is prorated based on the full-time salary range of $200,000 - $300,000. This does not include other forms of compensation and benefits that are part of S

Why Work with Us Solve impactful problems. At Seven Research, our close-knit team unites brilliant minds passionate about pushing technological and scientific boundaries in the global markets. We provide state-of-the-art resources and cultivate intellectual freedom, enabling our people to develop groundbreaking solutions to the most complex challenges of our field. Our collaborative approach celebrates excellence from everyone, regardless of tenure. Your Role Our Algorithmic Developers are researchers and engineers who transform complex quantitative concepts into high-performance trading strategies and systems that are efficient, stable, and scalable. You will be continuously challenged to think creatively while conducting rigorous analysis and improvements regarding market dynamics, strategy behavior, and system performance. If you are excited by the prospect of solving these sorts of problems in a collaborative, research-driven environment, we would love to get to know you. What We’re Looking For We seek talented engineers committed to the best algorithmic development practices. No financial knowledge is expected. You should have excellent programming and analytical skills and be excited to apply these talents within our state-of-the-art research and trading infrastructure. You should be motivated to take ownership over core projects and relentlessly drive decisive improvements. The ideal candidate will possess most of the following: PhD, master’s, or bachelor’s degree in computer science or a related field Strong background in data structures and algorithms, and mastery of C++ Applied knowledge of statistical analysis. Passion for solving complex quantitative problems through technology Strong communication skills to articulate complex technical concepts Job Compensation Range The base salary range for this role is $200,000 - $300,000. This does not include other forms of compensation and benefits that are part of Seven Research’s comprehensive package. We

About the Role As a Senior Software Engineer, Machine Learning, you’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the catalog system and our product knowledge graph 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 our product knowledge graph accurate, standardized, semantically rich, easily discoverable, and extensible. We’re looking for someone with a command of production-level machine learning and experience with solving end-user problems who enjoys collaborating with multi-disciplinary teams. We expect Senior Engineers to be able to own projects end-to-end and mentor their junior and mid level peers. This role is hybrid with some in-office time expected and will report to an Engineering Manager. You're excited about this opportunity because you will... Develop advanced machine learning models to improve core parts of your teams scope. Be responsible for the end-to-end ML lifecycle, including ideation, offline model training, online shadowing/deployment, experimentation, and post-launch monitoring/measurement. Work on creating novel ML & AI solutions that push both our business and the industry forward. Scale our systems and services to fuel the growth of our business. Completely disrupt logistics by tackling bleeding-edge, technical problems We're excited about you because you have... M.S., or PhD. in a technical field such as computer science, mathematics, statistics, physics or equivalent. 5+ years ML industry experience with a solid understanding of machine learning algorithms and fundamentals. Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software Experience building data/feature engineering pipelines at scale using Pyspark and Snowflak

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. We are hiring a Post-Doctoral Researcher for our AI Research team. This 24 month fixed-term position is designed for recent PhD graduates looking to deepen their research experience on challenging problems at the frontier of AI — s pecifically, applying machine learning to high-impact real-world domains like medicine, finance, and law. You'll collaborate closely with Snowflake researchers, scientists, and engineers on fundamental and applied problems, publishing high-quality work while helping shape how AI integrates with the world's most consequential industries. AS A POST-DOCTORAL RESEARCHER AT SNOWFLAKE, YOU WILL: Collaborate with research mentors to formulate research projects or novel applications of machine learning aligned with the team's mission, with a focus on AI applied to medicine, finance, or law Conduct independent and collaborative research and publish high-quality work at top AI and domain-applied research venues Design and execute large-scale experiments using modern deep learning frameworks, writing high-quality, reusable code Develop models and systems that bridge AI capabilities with real-world application requirements in high-stakes, regulated domains Engage across teams — including with domain experts and applied engineering — to ground research in pra

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. We're hiring Software Engineers for our Data Platform team to build and evolve Snowflake's real-time stream processing and data transformation platform. If you've built or researched high-throughput streaming systems or scalable transformation engines - in industry or graduate work - we want to talk. AS A SOFTWARE ENGINEER, DATA PLATFORM AT SNOWFLAKE, YOU WILL: Design and implement low-latency stream processing and in-flight data transformation systems at global scale Own correctness and performance of streaming execution (watermarks, exactly-once delivery, out-of-order handling) Build transformation primitives and operators that run reliably at cloud-scale throughput Contribute to architectural decisions for our next-generation streaming and transformation platform Write production-quality systems code and collaborate across product and engineering teams OUR IDEAL CANDIDATE WILL HAVE: BS/MS/PhD in Computer Science or related field — graduate research in streaming, distributed systems, or query/transformation engines is a strong differentiator Deep distributed systems fundamentals: fault tolerance, consistency, state management Hands-on or experience with stream processing or transformation systems (Flink, Kafka Streams, Spark Structured Streaming, or similar) Proficiency i

We are seeking senior engineers to pioneer new methodologies for accurately assessing the performance and capabilities of ground-breaking deep learning models, including LLMs, RAG, agents, and vision models. You will collaborate across the organization to bring the latest flagship models from our community and partners to life. This role offers an outstanding opportunity to craft the future of AI at a fast-growing company at the forefront of the AI revolution. Join our team of world-class software engineers and partners to deliver the most advanced models with lightning-fast inference. You'll work on the most powerful, enterprise-grade GPU clusters capable of hundreds of PetaFLOPS and gain early access to unreleased hardware, making a direct impact on NVIDIA's roadmap and the broader AI landscape! What you’ll be doing: Design and build decision-grade evaluation environments for NVIDIA's frontier models spanning reasoning, multimodal, long-context, and agentic systems, producing auditable accuracy signals that gate every major model release. Research and develop novel evaluation methodologies for emerging model families and capability domains (low-precision numerics, multi-turn agentic tasks, code generation) where established benchmarks don't yet exist or don't generalize. Build and operate the evaluation infrastructure and pipelines including benchmark environments, regression CI systems, and statistical analysis tooling, used by model, product, and applied research teams across NVIDIA. Partner with model research, training, and customer teams to translate evaluation signals into concrete decisions: release go/no-go, training iteration direction, and competitive positioning against external frontier models. What we need to see: BS, MS, or PhD in Computer Science, Machine Learning, Statistics, or a related field <li