Systems Phd Jobs

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

NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern deep learning — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, we are increasingly known as “the AI computing company.” We're looking to grow our company and establish teams with the most thoughtful people in the world. We are looking for an excellent engineering manager to own and deliver an end to end manageability stack for Data Center Systems. We are seeking an experienced manager who is deeply technical, hands-on, and has a wide system view. You will manage a team of experts, design & build OpenBMC based manageability software stack for NVIDIA’s next generation Data Center Compute Systems. We want to grow our teams with the smartest people in the world. If you're creative and autonomous, we want to hear from you! What you’ll be doing: Own and deliver OpenBMC based manageability stack for next generation Data Center Compute Systems. Own firmware delivered to data centers in terms of quality, reliability and telemetry performance. Manage and lead a distributed team of software engineers to deliver firmware stack with high quality. Work with data center architects and cloud customers for correct requirements and scope implementation to ensure speed of light product development. Work closely with cross functional teams to ensure scalable manageability architecture for all data centers products Drive efficiency, reliability and optimization in firmware architecture from a data center view point. Work closely with customers and internal teams to resolve issues at Speed of Light. What we need to see: BS, MS, or PhD in EE/CS or related field o

About the Role We’re looking for Senior Backend Engineers to work on both Product and Product Platform based teams. Product focused Engineers work at the intersection of product and infrastructure to solve key business problems with elegant technical solutions. You'll operate our backend services and architecture that support all product functionality and will be challenged to consider the big picture -- collaborating cross-functionally, as well as evaluating and executing on trade-offs to maximize business impact for the company. 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, release and run large-scale web applications Develop and define the backend architecture and tech stack for a product area Improve performance, reliability, scalability and security for our backend systems Be involved in transitioning our monolithic codebase to a microservice-based architecture Completely disrupt logistics by tackling bleeding-edge, technical problems We're excited about you because you have... B.S., M.S., or PhD. in Computer Science or equivalent 5+ years of industry experience Prior experience working with backend tech stacks 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 Ability to analyze and improve efficiency, scalability, and stability of various system resources Experience with service oriented architecture, writing REST API’s, unit testing, and architectural design Understanding of modern web stacks and architecture (HTTP, REST) Experience with SQL and NoSQL databases and other technologies (e.g. Postgres, Redis, Elasticsearch, RabbitMQ) About DoorDash At DoorDash, our mission to empower loca

Why RoboForce RoboForce is an AI robotics company developing Physical AI–powered Robo-Labor for dull, dirty, and dangerous work. The company’s robots are engineered for demanding industrial environments, with a focus on real-world deployment and scalability. We are looking for a motivated Mechanical Design Engineer Intern to join our team. This is a hands-on opportunity to design, prototype, test, and iterate on robotic hardware for real-world deployment. You will work alongside experienced mechanical, electrical, and robotics engineers on ambitious, real-world problems. You will help turn ideas into tested hardware and see how your designs perform as part of complete robotic systems. Responsibilities Design mechanical parts, mechanisms, and assemblies for robotic systems. Create and update 3D CAD models and engineering drawings . Perform basic engineering analysis, tolerance checks, and FEA. Support prototype fabrication, assembly, testing, and design iteration. Collaborate with mechanical, electrical, and robotics engineers during system integration. Requirements Currently pursuing a Bachelor’s, Master’s, or PhD in Mechanical Engineering, Robotics, Mechatronics, or a related field, or graduated within the past 6 months . Strong mechanical design fundamentals. Experience with SolidWorks or Fusion 360 . Hands-on experience designing and building mechanical hardware through coursework, research, internships, robotics teams, or personal projects. Familiarity with manufacturing processes such as CNC machining, sheet metal, or 3D printing. Able to work on-site in Milpitas, CA, five days per week (40 hours/week) Bonus Qualifications Experience with robotic arms, actuators, transmissions, mobile robots, or end-effectors . Familiarity with GD&T, tolerance stack-up, FEA, and DFM . Machine-shop or rapid-prototyping experience. Experience taking a design from CAD to manufactured and tested hardware . Benefits Company-provided lunch and dinner, a fully stocked

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . Location: Reddit has a flexible first workforce. Don't live near our office? No worries: you can work remotely from anywhere in the US or Canada. About the Team The ML Efficiency team builds the infrastructure, tooling, and optimization systems that enable machine learning engineers and researchers to train, evaluate, deploy, and operate models efficiently at scale. We focus on improving developer productivity, reducing infrastructure costs, increasing hardware utilization, and accelerating experimentation across the company’s ML ecosystem. Responsibilities Design and build systems that improve the efficiency of ML training and inference workloads. Develop tooling that helps ML engineers debug, profile, optimize, and monitor model performance. Improve GPU and general resource utilization through scheduling, resource management, caching, and workload optimization. Partner with ML researchers and product teams to identify bottlenecks and drive performance improvements. Build benchmarking frameworks and performance dashboards for training and serving systems. Optimize distributed training infrastructure, data pipelines, and model serving architectures. Lead cross-functional initiatives that improve the productivity of Reddit ML engineers. Drive technical strategy for ML platform scalability, reliability, and cost efficiency. Qualifications Required BS, MS, or PhD in Computer Science or a related field. 5+ years of software engineering experience. Strong proficiency in Python Profiency in at least one systems language (Go,

The Collision Avoidance System (CAS) is responsible for detecting and reacting to imminent collision situations in support of our vehicle’s overall safety goals. CAS Perception is responsible for processing raw sensor data from our vehicle’s world-class sensor suite using a combination of geometric, interpretable algorithms and deep learning to detect near-collisions with obstacles along our intended driving path, in the most challenging dense urban environments and under tight compute resource constraints. Overall CAS is parallel and complementary to our Main Artificial Intelligence (AI) autonomy stack, and has a close relationship with our vehicle hardware and safety teams in order to architect redundancy into our overall driving system. The CAS Verification & Validation (CAS V&V) is a multidisciplinary team data, software and systems engineers defining and building metrics to measure the Collision Avoidance System performance and work with the Systems Design and Mission Assurance (SDMA) and QA teams to develop validation plans for the features. In this role, you will: Apply distributed computing algorithms to analyze petabytes of urban driving data. Develop metrics and tools to analyze errors and system improvements. Work closely with CAS engineers to evaluate system performance. Collaborate with Perception engineers to define metrics for autonomous driving. Partner with Planning engineers to measure performance in complex urban environments. Qualifications: BS, MS, or PhD degree in computer science or a related field Fluency in C++ and/or Python Extensive experience with programming and algorithm design Bonus Qualifications: Experience with analysis of latency for safety-critical software systems Experience with petabyte-scale distributed computing (Spark, Databricks, generic MapReduce pipelines) <li

At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most. Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. On the Underwriting ML team, you’ll build and improve machine learning systems that make real-time transaction decisions, assessing the repayment risk and expected value of every Affirm checkout. You’ll work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring as user behavior and macroeconomic conditions evolve. What you’ll do - You will develop and iterate on underwriting prediction models using a mix of approaches for tabular and sequential data - You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed. - You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls. - You will help productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness. - You will instrument and monitor model and data health, and help define retraining/backtesting workflows - You will collaborate across Engineering, Risk Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences. What we look for - You have a total of 2+ years of experience as a machine learning engineer or a PhD in a relevant field. - Strong Python skills and experience writing production-quality code. - Experience building and evalua

At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most. On the Underwriting ML team, you’ll build and improve machine learning systems that make real-time transaction decisions, assessing the repayment risk and expected value of every Affirm checkout. You’ll work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring as user behavior and macroeconomic conditions evolve. What you’ll do - You will develop and iterate on underwriting prediction models using a mix of approaches for tabular and sequential data - You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed. - You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls. - You will help productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness. - You will instrument and monitor model and data health, and help define retraining/backtesting workflows - You will collaborate across Engineering, Risk Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences. What we look for - You have a total of 2+ years of experience as a machine learning engineer or a PhD in a relevant field. - Strong Python skills and experience writing production-quality code. - Experience building and evaluating models for classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost, or similar). - Experience with a deep learning fra

About the Team DoorDash is building the world's most reliable on-demand, logistics engine for delivery! We're looking for experienced engineers to join our fast-growing engineering team to help us develop a 24x7, global infrastructure system that powers DoorDash’s three-sided marketplace of consumers, merchants, and dashers. About the Role Product engineers work at the intersection of product and infrastructure to solve key business problems with elegant technical solutions. You'll operate our backend services and architecture that support all product functionality and will be challenged to consider the big picture -- collaborating cross-functionally, as well as evaluating and executing on trade-offs to maximize business impact for the company. You're excited about this opportunity because you will... Develop, release and run large-scale web applications Develop and define the backend architecture and tech stack for a product area Improve performance, reliability, scalability and security for our backend systems Be involved in transitioning our monolithic codebase to a microservice-based architecture Completely disrupt logistics by tackling bleeding-edge, technical problems We're excited about you because you have... B.S., M.S., or PhD. in Computer Science or equivalent 8+ years of industry experience Prior experience working with backend tech stacks Ability to analyze and improve efficiency, scalability, and stability of various system resources Experience with service oriented architecture, writing REST API’s, unit testing, and architectural design Understanding of modern web stacks and architecture (HTTP, REST) Experience with SQL and NoSQL databases and other technologies (e.g. Postgres, Redis, Elasticsearch, RabbitMQ) Nice To Haves Experience building large scale, real-time applications Experience with Java/Kotlin Contributor to open source projects Compensation Actual compensation within the pay range listed below will be decided based on factors includ

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars. SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next. ABOUT THE ROLE: As an ML Infrastructure Engineer, you will play a pivotal role in building and optimizing the reliable, high-performance ML platform that powers recommendations on X. We're looking for exceptional engineers who are passionate about our mission and have a strong desire to make a meaningful impact. RESPONSIBILITIES: Designing, building, and scaling GPU compute infrastructure, training frameworks, and experimentation tools to enable rapid iteration on ML hypotheses Developing data pipelines and integrating large-scale data, training, and inference systems Collaborating with ML teams to productionize models and ensure seamless integration across the stack Ensuring scalability, reliability, and efficiency of large-scale machine learning systems Working across the full stack to solve complex problems independently Mentoring junior engineers and contributing to the growth of the team BASIC QUALIFICATIONS: Bachelor, Master, Post-graduate or PhD in computer science, machine learning, or other quantitative discipline; or equivalent work experience 2+ years of industry experience working with high traffic or large-scale production environments, distributed systems, GPU infr

Who We Are At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me , Do the Right Thing , and Get Things Done . These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er. About the Opportunity We are looking for a Principal AI Engineer to lead the architecture and deployment of large-scale, LLM-powered conversational Chatbot systems serving both enterprise and consumer use cases. This is a hands-on technical role focused on building intelligent, autonomous chatbot systems operating in real-time crypto environments. You will define the technical vision and own end-to-end delivery of production-grade conversational AI platforms. What You’ll Be Doing Lead the design, development, and lifecycle management for conversational Chatbots products for enterprise and consumer use cases. Drive experimentation, simulations, and continuous learning pipelines for Chatbot performance optimization. Design multi-level intent routing, classifier training, and fallback strategies. Building dialogue quality evaluation systems. Drive automated bad-case discovery, A/B testing, and continuous iteration loops. Ensure system scalability, security, and regulatory compliance of AI-powered solutions in the crypto exchange domain. What We Look For In You PhD or Master's in Computer Science, AI, Machine Learning, or related field with at least 10 years of industry experience . Deep understanding

Archer is an aerospace company based in San Jose, California building an all-electric vertical takeoff and landing aircraft with a mission to advance the benefits of sustainable air mobility. We are designing, manufacturing, and operating an all-electric aircraft that can carry four passengers while producing minimal noise. Our sights are set high and our problems are hard, and we believe that diversity in the workplace is what makes us smarter, drives better insights, and will ultimately lift us all to success. We are dedicated to cultivating an equitable and inclusive environment that embraces our differences, and supports and celebrates all of our team members. What you’ll do: Lead teams to design and implement flight control algorithms for electric VTOL aircraft Work cross functionally to connect GNC requirements with other system requirements Influence leadership with decisions that are affected by GNC systems. Implement creative solutions to efficiently verify complex systems and software Build simulations to verify and validate system requirements Run trade studies to inform aircraft design and system architecture Work with the Flight Test team to design tests that safely evaluate the vehicle capabilities Improve the fidelity and capabilities of our vehicle and environment simulation models Analyze full-scale and subscale flight data to improve our models Author software requirements and test cases that drive the design of the GNC software Collaborate with Systems Engineering to properly decompose higher level requirements Collaborate with Systems Safety and Systems Engineering to identify and correct any deficiencies in the Flight Control System. What you need: Strong knowledge of vehicle dynamics, optimization, and both classical and modern control theory BS in Aerospace Engineering, Electrical/Computer Engineering, or a related field with at least 10 years of relevant experience OR MS with at least 8 years of relevant experience OR PhD with at

The Prediction & Behavior ML team is responsible for developing machine learning (ML) algorithms that learn and predict behaviors from data, applying them both on-vehicle to influence driving behavior and off-vehicle to provide ML capabilities to simulation and validation. Given the tight integration of behavior forecasting and motion planning, our team collaborates closely with the Planner team to advance overall vehicle behavior. We also work closely with our Perception, Simulation, and Systems Engineering teams to accelerate our ability to validate our driving performance. As a Learned Trajectory Machine Learning Engineer you will be responsible for developing deep learned models that produce trajectories for our vehicles to drive. Given the tight integration of behavior prediction and motion planning, you will closely collaborate with the Planner and Perception teams in the advancement of our overall vehicle behavior. In this role, you will: You will develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for our autonomous vehicle. You will also work on techniques to estimate the quality of those driving plans along the dimensions of safety, progress, comfort etc. You will leverage our large-scale machine learning infrastructure to discover new solutions and push the boundaries of the field You will develop metrics and tools to analyze errors and understand improvements of our systems You will collaborate with engineers on Perception, 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 transformer-based model architectures and reinforcement learning Experience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelines Fluency in C++

The Prediction & Behavior ML team is responsible for developing machine learning (ML) algorithms that learn and predict behaviors from data, applying them both on-vehicle to influence driving behavior and off-vehicle to provide ML capabilities to simulation and validation. Given the tight integration of behavior forecasting and motion planning, our team collaborates closely with the Planner team to advance overall vehicle behavior. We also work closely with our Perception, Simulation, and Systems Engineering teams to accelerate our ability to validate our driving performance. As a Learned Trajectory Machine Learning Engineer you will be responsible for developing deep learned models that produce trajectories for our vehicles to drive. Given the tight integration of behavior prediction and motion planning, you will closely collaborate with the Planner and Perception teams in the advancement of our overall vehicle behavior. In this role, you will: You will develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for our autonomous vehicle. You will also work on techniques to estimate the quality of those driving plans along the dimensions of safety, progress, comfort etc. You will leverage our large-scale machine learning infrastructure to discover new solutions and push the boundaries of the field You will develop metrics and tools to analyze errors and understand improvements of our systems You will collaborate with engineers on Perception, 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 transformer-based model architectures and reinforcement learning Experience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelines Fluency in Pyt