Systems Phd Jobs

About the Role DoorDash is building the world's most reliable on-demand, logistics engine for delivery. We are continuing to grow rapidly and expanding our Engineering offices globally! We’re looking for Senior iOS Engineers who want to work on building an app that’s used by millions of people and critical to local entrepreneurs building their online presence. We’ve seen significant growth in our codebase and number of developers and we’d love for you to bring a piece of your culture and experiences into ours so that we can continue to improve our processes and architecture. Whether you’re an engineer interested in scaling our mobile infrastructure or building awesome product features, we’d love to talk to you! 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… Work within our engineering teams to build exciting features Improve our code base through continuous refactoring Solve challenging real-time data transfer and synchronization problems Extend and implement our APIs Implement unit tests Keep up-to-date with the industry standards of iOS development We're excited about you because you have… 5+ years of industry experience B.S., M.S., or PhD. in Computer Science or equivalent Expert at Swift, and iOS SDK Familiarity with networking, storage, and Cocoapods Systems and OOP knowledge Experience building, launching, and iterating on mobile apps Compensation The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future. In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more

The Trading team develops and operates quantitative trading strategies across prediction markets, equity markets, and crypto markets, including spot and derivatives. We are looking for an experienced Systematic Trader to develop, operate, and continuously improve systematic strategies in fast-moving electronic markets. Responsibilities: Operate and manage systematic trading strategies and frameworks across prediction markets, equities, and crypto, including spot, perpetuals, futures, options, and other derivatives. Monitor live trading, P&L, positions, risk, and execution performance. Research, backtest, deploy, and improve systematic trading strategies. Identify new trading opportunities across markets, products, exchanges, and venues. Analyze strategy and execution performance, including fills, slippage, adverse selection, transaction costs, and latency. Optimize strategy parameters, position sizing, execution, and risk controls. Work closely with quantitative developers and researchers to implement new strategies and improve trading systems. Participate in local trading coverage and rotating weekend coverage. Requirements: 4+ years of experience in systematic, quantitative, electronic, proprietary, or market-making trading. Demonstrated experience developing, operating, or improving profitable trading strategies. Strong understanding of market microstructure, execution, and risk management. Strong Python programming and quantitative data-analysis skills. Experience working with large market and trading datasets; SQL experience is preferred. Strong analytical and problem-solving skills, with the ability to take ownership in fast-moving markets. Master's or PhD degree in a quantitative discipline, or equivalent relevant quantitative experience. Preferred: Experience trading in one or m

The Trading team develops and operates quantitative trading strategies across prediction markets, equity markets, and crypto markets, including spot and derivatives. We are looking for an experienced Systematic Trader to develop, operate, and continuously improve systematic strategies in fast-moving electronic markets. Responsibilities: Operate and manage systematic trading strategies and frameworks across prediction markets, equities, and crypto, including spot, perpetuals, futures, options, and other derivatives. Monitor live trading, P&L, positions, risk, and execution performance. Research, backtest, deploy, and improve systematic trading strategies. Identify new trading opportunities across markets, products, exchanges, and venues. Analyze strategy and execution performance, including fills, slippage, adverse selection, transaction costs, and latency. Optimize strategy parameters, position sizing, execution, and risk controls. Work closely with quantitative developers and researchers to implement new strategies and improve trading systems. Participate in local trading coverage and rotating weekend coverage. Requirements: 4+ years of experience in systematic, quantitative, electronic, proprietary, or market-making trading. Demonstrated experience developing, operating, or improving profitable trading strategies. Strong understanding of market microstructure, execution, and risk management. Strong Python programming and quantitative data-analysis skills. Experience working with large market and trading datasets; SQL experience is preferred. Strong analytical and problem-solving skills, with the ability to take ownership in fast-moving markets. Master's or PhD degree in a quantitative discipline, or equivalent relevant quantitative experience. Preferred: Experience trading in one or m

The Trading team develops and operates quantitative trading strategies across prediction markets, equity markets, and crypto markets, including spot and derivatives. We are looking for an experienced Systematic Trader to develop, operate, and continuously improve systematic strategies in fast-moving electronic markets. Responsibilities: Operate and manage systematic trading strategies and frameworks across prediction markets, equities, and crypto, including spot, perpetuals, futures, options, and other derivatives. Monitor live trading, P&L, positions, risk, and execution performance. Research, backtest, deploy, and improve systematic trading strategies. Identify new trading opportunities across markets, products, exchanges, and venues. Analyze strategy and execution performance, including fills, slippage, adverse selection, transaction costs, and latency. Optimize strategy parameters, position sizing, execution, and risk controls. Work closely with quantitative developers and researchers to implement new strategies and improve trading systems. Participate in local trading coverage and rotating weekend coverage. Requirements: 4+ years of experience in systematic, quantitative, electronic, proprietary, or market-making trading. Demonstrated experience developing, operating, or improving profitable trading strategies. Strong understanding of market microstructure, execution, and risk management. Strong Python programming and quantitative data-analysis skills. Experience working with large market and trading datasets; SQL experience is preferred. Strong analytical and problem-solving skills, with the ability to take ownership in fast-moving markets. Master's or PhD degree in a quantitative discipline, or equivalent relevant quantitative experience. Preferred: Experience trading in one or m

The Applied AI team designs and builds algorithmically driven features in the Datadog app. We work across a range of applications, primarily focusing on analysis on streaming data such as anomaly detection, error outliers and faulty deployment analysis. As an Applied Scientist you will work on building models and algorithms for machine learning powered features within the Datadog platform. You will work closely with our engineering and product partners to explore, build, scale and deliver these features that we incubate within the Applied AI team. 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: Design solutions for our different use cases. You will research and benchmark relevant algorithms to find the best fit for our use-cases Leverage machine learning algorithms and statistical techniques to build new scalable product features Develop, deploy and monitor new and existing features to production Participate in our journal club by reading and presenting the latest academic research papers to the team Explore, analyze and tell the story behind high volumes of data flowing through Datadog systems Maintain and monitor the models, services and infrastructure owned by your team Participate in your team’s on-call rotation Who You Are: You have a BS/MS/PhD in a Computer Science, Engineering, Machine Learning or related scientific field or equivalent experience You have experience working with high-scale systems and datasets including building models, applying machine learning to real business problems, and writing production data pipelines You can explain complex ideas and algorithms to non-technical audiences You care about code simplicity and performa

The Applied AI team designs and builds algorithmically driven features in the Datadog app. We work across a range of applications, primarily focusing on analysis on streaming data such as anomaly detection , error outliers and faulty deployment analysis . As an Applied Scientist you will work on building models and algorithms for machine learning powered features within the Datadog platform. You will work closely with our engineering and product partners to explore, build, scale and deliver these features that we incubate within the Applied AI team. 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: Design solutions for our different use cases. You will research and benchmark relevant algorithms to find the best fit for our use-cases Leverage machine learning algorithms and statistical techniques to build new scalable product features Develop, deploy and monitor new and existing features to production Participate in our journal club by reading and presenting the latest academic research papers to the team Explore, analyze and tell the story behind high volumes of data flowing through Datadog systems Maintain and monitor the models, services and infrastructure owned by your team Participate in your team’s on-call rotation Who You Are: You have a BS/MS/PhD in a Computer Science, Engineering, Machine Learning or related scientific field or equivalent experience You have experience working with high-scale systems and datasets including building models, applying machine learning to real business problems, and writing production data pipelines You can explain complex ideas and algorithms to non-technical audiences You care about code simplicity and performance You are excited to work on

About the Role: The Machine Learning team at Tubi drives the innovation behind personalized user experiences. With the largest inventory in the industry and hundreds of millions of viewers, we tackle problems in the space of recommendations, search, content understanding and ads optimization that shape the future of streaming. We are seeking a highly skilled Staff Machine Learning Engineer to contribute to transformative projects in video personalization. In this role, you will design and implement advanced algorithms and systems to improve our personalization strategy. As a senior technical expert, you will tackle complex problems in machine learning at scale, collaborating closely with cross-functional teams to develop and optimize machine learning-driven solutions. This is a hybrid role in our Toronto office. What You'll Do: Lead the design, development, and implementation of advanced recommendation systems and algorithms for a global audience Conduct deep dives into algorithmic components and systems, ensuring that models are optimized for both performance and scalability across multiple regions and product areas Build and deploy high-impact robust ML pipelines, including data extraction, feature development, model training, testing, and deployment Continuously monitor, evaluate, and optimize the performance of deployed models, ensuring they meet business goals and provide high-quality user experiences. Work closely with Product, Engineering, and Data Science teams to align on product requirements, set expectations, and deliver machine learning-driven solutions that improve user engagement Your Background: 8+ years of industry experience building production Machine Learning systems MSc or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, or a related field Experience with deep learning technologies for recommendation systems, including TensorFlow, PyTorch, or similar frameworks Proficiency in building and deploying full-stack machine le

Forward was founded in 2013 by four Stanford Ph.D.s, building the industry's first network digital twin: a mathematically accurate model of the production network. It's the foundation for autonomous networking, giving engineers and AI agents the ability to know the impact of every change before it touches production. That founding instinct still defines how we work. We're accurate and evidence-driven, relentless about clarity, and we'd rather be certain than comfortable, building a groundbreaking platform that transforms how teams run and secure networks across every major cloud and vendor environment. Global leaders like Goldman Sachs, PayPal, S&P Global, IBM, and Dell trust Forward, alongside fast-growing enterprises and government agencies, realizing an average of $14.2 million in annual benefits, according to IDC. Backed by top-tier investors, including A. Capital, Andreessen Horowitz, Goldman Sachs, MSD Partners, Omega Venture Partners, Section 32, and Threshold Ventures, and headquartered in Santa Clara, we're most proud of our team: curious people who'd rather build what doesn't exist than accept how things have always been done. Forward Networks is looking for a Systems (Sales) Engineer Do want to create a category and help build a special company? Do you want to help sell a platform that solves real networking problems? Join a company that has been in market 5+ years and has some of the top Federal agencies and F500/Global 2000 already buying and referenceable. If you have 5-10 years of wildly successful experience as a Sales Engineer selling to large enterprise accounts..you may be the one! We are building a special team and hope you consider us if you want to have the experience of changing the networking world as we know it. Responsibilities: Serve as the primary technical resource for the sales organization working with large enterprise accounts. Work with the sales team as the product advocate and key technical adviser Drive and manage the te

About the Role As a Systems Research Engineer specialized in GPU Programming, you will play a crucial role in developing and optimizing GPU-accelerated kernels and algorithms for ML/AI applications. Working closely with the modeling and algorithm team, you will co-design GPU kernels and model architecture to enhance the performance and efficiency of our AI systems. Collaborating with the hardware and software teams, you will contribute to the co-design of efficient GPU architectures and programming models, leveraging your expertise in GPU programming and parallel computing. Your research skills will be vital in staying up-to-date with the latest advancements in GPU programming techniques, ensuring that our AI infrastructure remains at the forefront of innovation. Requirements Strong background in GPU programming and parallel computing, such as CUDA and/or Triton. Knowledge of ML/AI applications and models Knowledge of performance profiling and optimization tools for GPU programming Excellent problem-solving and analytical skills Bachelor's, Master's, or Ph.D. degree in Computer Science, Electrical Engineering, or equivalent practical experiences Responsibilities Optimize and fine-tune GPU code to achieve better performance and scalability Collaborate with cross-functional teams to integrate GPU-accelerated solutions into existing software systems Stay up-to-date with the latest advancements in GPU programming techniques and technologies About Together AI Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month. Compensation We offer competi

About the Role We're looking for a Hardware Systems Engineer to own system-level architecture across electrical, mechanical, firmware, sensor, and compute subsystems – decomposing platform requirements into verifiable specs for power distribution, motion control, embedded compute, sensor suites and communication buses (CAN, Ethernet, etc.). You'll drive requirements traceability, interface control documents (ICDs), trade studies, and verification and validation (V&V) plans that ensure subsystems built by separate EE, ME, firmware, and perception teams function as one safe, reliable delivery robot. You’re excited about this opportunity because you will… Own system-level architecture across electrical, mechanical, firmware, sensor, and compute subsystems for autonomous delivery platforms. Decompose platform requirements into verifiable specs for power distribution, motion control, embedded compute, and sensor suites. Drive requirements traceability, ICDs, trade studies, and V&V plans across EE, ME, firmware, and perception workstreams. Define fault-tolerant architectures (FMEA, fault trees, redundancy) governing system behavior during component failures. Lead cross-functional root-cause debugging across hardware, firmware, and software boundaries, conducting design and safety reviews. Translate business and regulatory requirements into engineering specs in collaboration with Product, Safety, Legal, and Operations. We’re excited about you because… B.S., M.S., or Ph.D. in EE, ME, Computer Science, Systems Engineering, Robotics, or a related field 8+ years of experience building, testing, and validating mechatronics or sensor-equipped hardware in robotics, automotive, or aerospace. Strong expertise in embedded systems architecture, vehicle mechanicals, electronics, and autonomous perception systems. Demonstrated ability to perform cross-boundary debugging across hardware, firmware, and software layers. Ability to evaluate complex trade-offs, identify failure mode

NVIDIA is looking for an outstanding passionate and talented Senior SW & System Architect to join our SW Architecture group. The position includes researching new technologies with focus on architecture definition of groundbreaking technologies in different domains - networking, security, virtualization and orchestration. NVIDIA Architecture group consists of world-class architects responsible for designing the next generation state-of-the-art architecture for our DPUs & NICs technologies. You will play a key role defining the future of cloud solution stack, from HW to Application level, including orchestration, provisioning, network programmability and SDN. You will be working with various teams around the world including SW architects, R&D, product, solution architects and external customers. This position offers a unique and exceptional opportunity to have real impact in a dynamic, technology-focused company shaping the future of data-centers technologies. What you will be doing: Lead architecture for cloud-networking including orchestration, provisioning and security solutions Design state-of-the-art system architecture for DPUs & NICs technologies Build end-to-end solutions from application level to HW Responsible for writing effective, clear and reliable architecture specification Evaluate new technologies and innovate & rapidly develop POC prototypes that can then be developed into full-fledged products/solutions Work closely with different NVIDIA teams around the world including sw & hw architects, R&D, product, solution architects, application and field engineers and more Work with high profile customers on advanced and future technologies and solutions What we need to see: B.Sc/M.Sc/Phd degree in Computer Scie

About the Team The Interpretability team studies internal representations of deep learning models. We are interested in using representations to understand model behavior, and in engineering models to have more understandable representations. We are particularly interested in applying our understanding to ensure the safety of powerful AI systems. Our working style is collaborative and curiosity-driven. About the Role OpenAI is seeking a researcher passionate about understanding deep networks, with a strong background in engineering, quantitative reasoning, and the research process. You will develop and carry out a research plan in mechanistic interpretability, in close collaboration with a highly motivated team. You will play a critical role in helping OpenAI ensure future models remain safe even as they grow in capability. This will make a significant impact on our goal of building and deploying safe AGI. In this role, you will: Develop and publish research on techniques for understanding representations of deep networks. Engineer infrastructure for studying model internals at scale. Collaborate across teams to work on projects that OpenAI is uniquely suited to pursue. Guide research directions toward demonstrable usefulness and/or long-term scalability. You might thrive in this role if you: Are excited about OpenAI’s mission of ensuring AGI benefits all of humanity, and are aligned with OpenAI’s charter . Show enthusiasm for long-term AI safety, and have thought deeply about technical paths to safe AGI. Bring experience in the field of AI safety, mechanistic interpretability, or spiritually related disciplines. Hold a Ph.D. or have research experience in computer science, machine learning, or a related field. Thrive in environments involving large-scale AI systems, and are excited to make use of OpenAI’s unique resources in this area. Possess 2+ years of research engineering experience and proficiency in Python or similar languages. Are deeply curious. About OpenA

EnCharge AI is a leader in advanced AI hardware and software systems for edge-to-cloud computing. EnCharge’s robust and scalable next-generation in-memory computing technology provides orders-of-magnitude higher compute efficiency and density compared to today’s best-in-class solutions. The high-performance architecture is coupled with seamless software integration and will enable the immense potential of AI to be accessible in power, energy, and space constrained applications. EnCharge AI launched in 2022 and is led by veteran technologists with backgrounds in semiconductor design and AI systems. Senior Emulation Engineer Location: India - Remote Job Description: At EnCharge AI, we are building the next generation of AI compute silicon — purpose-built for high-performance, low-power, and scalable AI inference. As an Emulation Engineer, you will play a critical role in validating complex AI accelerator architectures on emulation platforms before tape-out. This position is ideal for someone passionate about bridging the gap between hardware and software in fast-paced, deep tech environments. Responsibilities: • Set up and maintain Siemens Veloce emulation and prototyping platforms • Adapt SoC designs for Emulation and Prototyping • Develop and debug emulation testbenches and system-level environments • Support pre-silicon validation, power/performance analysis, and early software bring-up. Participate in silicon bring-up and validation. • Collaborate with design and verification teams to isolate design issues and accelerate debug. • Optimize performance of the emulation workloads and reduce turnaround time. • Work with firmware/software teams to enable use of emulators for OS and driver testing. Required Background: • BS/MS/Ph.D. in EE, CS, or related field with 7+ years of SoC design experience. • Experience with emulation platforms (Veloce, Palladium, or ZeBu) and FPGA-based prototyping systems (proFPGA, HAPS, or Protium) • Experience with emula

Be the visionary Perception Architect designing the sensory "nervous system" for our next generation of autonomous products. Evolve our end-to-end perception architecture, serving as the essential liaison between Sensing teams and the core ML system. Drive architectural evolution toward next-generation sensing and compute platforms by influencing Hardware-Software Co-Design. In this role, you will: System Synthesis: Define the end-to-end perception architecture—assess new sensing (LiDAR, Radar, Camera) to the final fused world model. Hardware-Software Co-Design: Partner with Compute and Sensing teams to influence the design, ensuring our algorithms have the "computational oxygen" they need. Algorithmic Strategy: Lead the transition from current pipelines to AI innovation pipelines, while maintaining the safety and interpretability required for edge deployment. Future-Proofing: Evaluate emerging technologies and determine their viability for the 3-5 year roadmap. Performance Budgeting: Collaborate on rigorous metrics for latency and accuracy across diverse edge-computing environments. Lead cross functional teams: to create a viable plan to move to new platforms and unlock further technical capabilities for Perception systems. Coordinate projects across partner teams to make sure work is aligned with the overall org direction. Qualifications Education: Ph.D. or MS in Computer Science, Robotics, Electrical Engineering, or a related field with a focus on Computer Vision . Experience: 8+ years in autonomous systems with at least 2 years in an architectural role , shipping and evolving perception systems Deep Domain Expertise: Mastery of systems focused on Computer Vision, De

Overview: The Data Acquisition team within the Foundations organization at OpenAI is responsible for all aspects of data collection to support our model training operations. Our team manages web crawling and GPTBot services and works closely with Data Processing, Architecture, and Scaling teams. We are looking for a skilled Full-Stack Engineer to join our Data Acquisition team to build and optimize the interfaces and tools that power our data infrastructure. Responsibilities: Develop and maintain full-stack applications that support data acquisition, including internal tools and dashboards. Collaborate closely with cross-functional teams, including Data Processing, Architecture, and Scaling, to ensure seamless data ingestion and workflow management. Design and implement APIs to facilitate data interactions between internal services and external data sources. Enhance user experience by developing intuitive web-based interfaces for managing and monitoring data pipelines. Optimize backend services for performance, scalability, and security in a distributed computing environment. Work with legal and compliance teams to ensure our data acquisition processes adhere to privacy regulations and best practices. Deploy and maintain infrastructure using Kubernetes and Infrastructure-as-Code (IaC) methodologies. Analyze system performance, conduct experiments, and improve data workflows to maximize efficiency. Qualifications: BS/MS/PhD in Computer Science or a related field. 4+ years of industry experience in full-stack development. Proficiency in frontend frameworks (React, Vue, or similar) and backend technologies such as Python, Node.js, or Go. Strong expertise in RESTful APIs, GraphQL, and database design (SQL and NoSQL). Experience building data-intensive applications that handle large-scale datasets. Familiarity with cloud platforms (AWS, GCP, or Azure) and container orchestration (Kubernetes, Docker). Prior experience with web crawling and large-scale data processing is a