Systems Research Engineer Intern Jobs

A World-Changing Company Palantir builds the world’s leading software for data-driven decisions and operations. By bringing the right data to the people who need it, our platforms empower our partners to develop lifesaving drugs, forecast supply chain disruptions, locate missing children, and more. The Role As a Security Systems Engineer at Palantir, you are responsible for implementing, designing, and maintaining the physical security systems that ensure the protection of Palantir’s people, assets, intellectual property, and reputation. You lead research and evaluations for the latest technology, security infrastructure, policies, procedures, systems, and applications, looking for easy-to-use capabilities that reduce button clicks and ultimately give time back to the operator. You will be collaborating with technical teams to develop and maintain end-to-end project plans and ensure on-time delivery. Your technical expertise is second only to your integrity and genuine passion for security and technology. As a member of the Global Security and Investigations team, you work with security professionals, architects, lawyers, and software and network engineers to deliver a safe and secure environment for your fellow employees. Palantir is a global company, so you'll lead physical security projects all over the world. You'll work hard to break down barriers between teams by communicating your knowledge and goals effectively across time zones and other physical and virtual barriers. *Please note this role is NOT for cyber security or information security positions* Core Responsibilities Design and oversee the technical aspects of your projects from conception to full deployment. Systems naturally break and need maintenance. You'll work with vendors and end users to find the best solutions for a problem, in the shortest possible time. Leverage both system and software engineering skills in order to address the needs of all teams within Globa

JOB TITLE Senior Data Engineer, Proprietary Research A CAREER WITH POINT72’S PROPRIETARY RESEARCH On our proprietary research team—Market Intelligence—you’ll partner with our Investment Professionals and Compliance team to uncover insights about companies, industries, and the broader economy through deep fundamental research and applying data science and engineering techniques to alternative data sets. You’ll work alongside a talented team with diverse skills, backgrounds, and perspectives. Our industry researchers, product managers, and data scientists and engineers work together to build compliant research products that answer the questions our investment professionals care about most. We look for other bright, motivated, and collaborative people to join our team and grow with us—more than 90% of the leaders in our group were promoted from within. WHAT YOU'LL DO Senior Data Engineers on the Proprietary Research team design and build Data & AI Platform to transform structured and unstructured data into actionable insights. As a member of our Data Engineering team, you will work closely with investment professionals, researchers, and data scientists to design, build, and launch robust end-to-end business solutions including data pipelines, products and tools that help extract the most value out of Point72’s data assets. In this role, you will: • Design and build big data pipelines for ingestion, transformation, trend creation, and delivery of AI-ready data assets. • Develop reusable software components, libraries, and APIs to support experimentation and production deployment. • Implement data quality, lineage, and governance controls to ensure datasets are reliable, auditable, and compliant. • Build scalable, AI-native, cloud-based infrastructure using distributed systems and modern cloud technologies. • Optimize system performance, cost, and reliability through benchmarking and continuous improvement. • Explore emerging Data and AI technologies, run proof of co

Our vision is to transform how the world uses information to enrich life for all. Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing. Our vision is to transform how the world uses information to enrich life for all. Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. As a Smart Systems Engineer at Micron, you will be at the forefront of innovation in our semiconductor manufacturing environment across High Bandwidth Memory (HBM), Component Assembly and Test, and SSD operations . Collaborating with a multi-functional team, you will provide system solutions to improve operational productivity, efficiency, and product quality while driving cost reduction initiatives. Your role will encompass problem analysis, system development and building, vendor management, production testing, know-how training, and commissioning of solutions. You will define software specifications and research, develop, document,

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. As an IT Software Engineer at Micron you will configure, develop, maintain and support Micron and third-party applications. You will create and analyze software specifications, as well as research, design, document, modify, and support software throughout the development life cycle. You must demonstrate the functional and technical skills necessary to switch between development technologies and merge them to provide the best solution. You must have a demonstrated ability to create quality software and participate in quality improvement efforts. You will provide solutions that create business value while at the same time minimizing negative impact resulting from defects, downtime, or performance degradation. Responsibilities: • Develop, maintain, troubleshoot and support software systems for manufacturing semiconductors; these systems schedule, track, manage and report on products produced at World-wide Micron Fabs • Work closely with customers to understand their needs and make suggestions to resolve production and process issues • Collaborate on a world-wide scale in gathering and evaluating customers’ requirements and specifications • Provide incident and problem management for MES systems • Provide technical support to customers as needed for problem identification and resolution • Provide constant update and training to customers and support personnel on applications, tools, uti

About Sapience AI Sapience AI is the collective intelligence platform for professional communities. We sit above the CRMs, AMS platforms, and knowledge bases that organizations already run, and we turn the expertise scattered across them into something every member can search, act on, and share. The intelligence a community needs is already inside it. Most organizations just cannot reach it. Knowledge lives in silos, in legacy systems, in the heads of a few experts, and in fragmented records no one can connect. We change that. Our work is grounded in four commitments: technology elevates people and never replaces them, the best expertise is already inside the community, everything is built on trust, and every deployment is purpose-driven for the organization it serves. Let’s achieve more, together. Where this role sits This role owns how Sapience AI serves intelligence at scale. You build and run the inference infrastructure that turns the models and reasoning behind MINERVA and COGENT into fast, reliable, affordable answers for members. You work where models meet production: serving, scaling, latency, and cost. Every time a member asks the platform something, your systems are what make the answer arrive quickly and hold up under load. You sit close to applied AI, research, and platform engineering, and you make the difference between a model that works in a notebook and one that serves a community reliably. Why this role exists Collective intelligence is only useful if it is fast and dependable. Members will not wait, and communities cannot rely on a platform that buckles under load or costs too much to run. Serving modern AI at scale is a hard systems problem: large models, tight latency budgets, expensive hardware, and demand that spikes. It takes real infrastructure engineering to get right. The AI/ML Infrastructure and Systems Engineer owns that problem. You make inference fast, reliable, and affordable, so the intelligence the platform promises actually reache

About the Team OpenAI's Industrial Compute organization is responsible for planning, delivering, operating, and optimizing the compute infrastructure that powers frontier AI. As OpenAI scales toward becoming an intelligence utility, Industrial Compute coordinates a complex lifecycle spanning infrastructure strategy, capacity planning, provider partnerships, fleet operations, product demand, and financial planning. The organization manages one of the largest and fastest-growing compute footprints in the world, where decisions around capacity allocation, deployment readiness, utilization, reliability, and product demand directly impact product availability, customer experience, and business performance. The Capacity Systems team builds the software platforms, data systems, and automation frameworks that connect these functions into a shared operating model. We transform fragmented planning workflows into scalable systems that enable teams to understand what compute was contracted, delivered, healthy, allocated, and ultimately converted into business and research outcomes. About the Role We are seeking a Capacity Systems Software Engineer to build the platforms and services that power Industrial Compute planning, forecasting, optimization, and operational decision-making. In this role, you will design and develop software systems that connect infrastructure delivery, fleet health, capacity allocation, demand forecasting, deployment readiness, financial planning, and product consumption into a unified system of record. Your work will help OpenAI make better decisions about where compute should be deployed, how capacity should be allocated, and how infrastructure investments translate into business value. You will partner closely with Capacity Planning, Fleet Operations, Infrastructure Engineering, Product, Finance, Supply Chain, and Strategic Sourcing teams to replace spreadsheet-driven workflows with scalable software systems that enable visibility, automation, and dec

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products. THE ROLE As an OS / K8s Systems Engineer at Baseten, you’ll build the automation and systems that turn raw GPU hardware into production-ready compute. From provisioning to orchestration, you’ll own the software layer that makes our infrastructure reproducible, scalable, and reliable across data centers. This is a senior, hands-on role focused on building systems not operating them. You’ll work close to the metal designing OS images, building provisioning pipelines, and automating cluster bring-up from scratch. Your work will define how quickly we can turn new capacity into usable compute. EXAMPLE INITIATIVES Zero-to-cluster automation Build workflows that take new hardware from unprovisioned to fully operational cluster. Provisioning systems Design PXE-based or equivalent systems for imaging and lifecycle management. Reproducible infrastructure — Ensure clusters deploy consistently across data centers. RESPONSIBILITIES Own the end-to-end automation of cluster bring-up and lifecycle management. Build and maintain OS images, provisioning systems, and configuration pipelines. Deploy and operate cluster orchestration platforms (Kubernetes, Slurm, or similar). Design systems for reproducibility across sites and hardware generations. Automate upgrades, rollouts, and failure recovery. Optimize system performance, including GPU utilization and networking. Partner with hardware and network teams to validate and improve system b

About the Team The Platform Systems team at OpenAI operates at the intersection of cutting-edge AI and large-scale distributed systems. We build the engineering and research infrastructure required to train OpenAI’s flagship models on some of the world’s largest, custom-built supercomputers. Our team develops core model training software and works deep in the stack - spanning collective communication, compute efficiency, parallelism strategies, fault tolerance, failure detection, and observability. The systems we build are foundational to OpenAI’s research velocity, enabling reliable, efficient training at frontier scale. We collaborate closely with researchers across the organization, continuously incorporating learnings from across OpenAI into the evolution of our training platform. About the Role As a Software Engineer, Platform Systems, you will design and build distributed systems that provide visibility into large-scale training workloads and help operate them reliably at scale. You’ll work on failure detection, tracing, and observability systems that identify slow or faulty nodes, surface performance bottlenecks, and help engineers understand and optimize massive distributed training jobs. This infrastructure is critical to operating OpenAI’s training stack and is actively evolving to support new use cases and increasingly complex workloads. This role sits at the core of our training infrastructure, blending systems engineering, performance analysis, and large-scale debugging. In This Role, You Will Design and build distributed failure detection, tracing, and profiling systems for large-scale AI training jobs Develop tooling to identify slow, faulty, or misbehaving nodes and provide actionable visibility into system behavior Improve observability, reliability, and performance across OpenAI’s training platform Debug and resolve issues in complex, high-throughput distributed systems Collaborate with systems, infrastructure, and research teams to evolve platform

About the Team The Platform Systems team at OpenAI operates at the intersection of cutting-edge AI and large-scale distributed systems. We build the engineering and research infrastructure required to train OpenAI’s flagship models on some of the world’s largest, custom-built supercomputers. Our team develops core model training software and works deep in the stack - spanning collective communication, compute efficiency, parallelism strategies, fault tolerance, failure detection, and observability. The systems we build are foundational to OpenAI’s research velocity, enabling reliable, efficient training at frontier scale. We collaborate closely with researchers across the organization, continuously incorporating learnings from across OpenAI into the evolution of our training platform. About the Role As a Software Engineer, Platform Systems, you will design and build distributed systems that provide visibility into large-scale training workloads and help operate them reliably at scale. You’ll work on failure detection, tracing, and observability systems that identify slow or faulty nodes, surface performance bottlenecks, and help engineers understand and optimize massive distributed training jobs. This infrastructure is critical to operating OpenAI’s training stack and is actively evolving to support new use cases and increasingly complex workloads. This role sits at the core of our training infrastructure, blending systems engineering, performance analysis, and large-scale debugging. In This Role, You Will Design and build distributed failure detection, tracing, and profiling systems for large-scale AI training jobs Develop tooling to identify slow, faulty, or misbehaving nodes and provide actionable visibility into system behavior Improve observability, reliability, and performance across OpenAI’s training platform Debug and resolve issues in complex, high-throughput distributed systems Collaborate with systems, infrastructure, and research teams to evolve platform

About the Team OpenAI’s Hardware organization develops silicon and system-level solutions designed for the unique demands of advanced AI workloads. The team is responsible for building the next generation of AI-native silicon while working closely with software and research partners to co-design hardware tightly integrated with AI models. In addition to delivering production-grade silicon for OpenAI’s supercomputing infrastructure, the team also creates custom design tools and methodologies that accelerate innovation and enable hardware optimized specifically for AI. About the Role We are looking for an experienced Mechanical Engineer with 7+ years of experience in design of IT hardware from chip/package to system levels. You’ll work alongside experts in thermal, mechanical, electrical, software, and systems engineering to support the design, analysis, and validation of mechanical and thermal systems that ensure the reliability, efficiency, and longevity of mission-critical hardware. This position requires strong analytical skills, hands-on testing experience, and the ability to work in a fast-paced, cross-disciplinary environment. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Lead mechanical design for AI supercomputer product in the data center application Collaborate with the cross functional team to design and optimize thermal solutions for data center hardware, including chips, power modules, and system-level cooling architectures Collaborate with cross-functional teams to integrate thermal management strategies into hardware design, from concept to mass production Design and validate mechanical systems, including chassis, enclosures, cooling systems, and high-power connections, ensuring alignment with performance and reliability standards. Perform 3D modeling, FEA, tolerance analysis, and prototyping, ensuring manufacturability and a

About the Team The Frontier Systems team at OpenAI builds, launches, and supports the largest supercomputers in the world that OpenAI uses for its most cutting edge model training. We take data center designs, turn them into real, working systems and build any software needed for running large-scale frontier model trainings. Our mission is to bring up, stabilize and keep these hyperscale supercomputers reliable and efficient during the training of the frontier models. About the Role As a Software Engineer on the Frontier Systems team focused on power management, you will work on critical infrastructure to support cutting-edge research. With large-scale supercomputers consuming substantial amounts of power, managing this efficiently is key to maximizing computational capacity. This role is critical to ensuring that our cutting-edge research supercomputing infrastructure runs smoothly, while maintaining reliability and grid-level power stability. Our team empowers strong engineers with a high degree of autonomy and ownership, as well as ability to effect change. This role will require a keen focus on system-level comprehensive investigations and the development of automated solutions. We want people who go deep on problems, investigate as thoroughly as possible, and build automation for detection and remediation at scale. In this role, you will: Develop and implement system-level and software-level solutions to optimize power usage in large-scale supercomputers, ensuring efficient and reliable operations. Build automation to monitor power consumption patterns during training workloads and design algorithms to stabilize these fluctuations, preventing issues with grid reliability. Work with researchers and engineers to design tools for real-time monitoring, detection, and remediation of power-related hardware and system faults. Collaborate cross-functionally to translate complex electrical system requirements into code, while driving continuous improvements in power man

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

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

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

Senior: PLN 260,400 - 352,200 Staff: PLN 350,700 - 474,400 Subject to alignment to the responsibilities and duties of the role - we currently have multiple positions available at both Senior and Staff level About Graphcore At Graphcore, we’re building the future of AI compute. We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale. As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem. To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world. We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence Job Summary As a Senior Machine Learning Engineer in the Applied AI team at Graphcore, you will contribute to advancing AI technology by developing and optimising AI models tailored to our specialised hardware. You will work on large scale systems where performance is critical to the success of our projects. Working closely with the Software development and Research teams, you will play a critical role in identifying opportunities to innovate and differentiate Graphcore’s technology. We seek engineers with strong technical skills and an understanding of AI model implementation at scale, eager to make a tangible impact in this rapidly evolving field. The Team The Applied AI team’s role is to be proxies for our customers, we need to understand the latest AI models, applications, and software to ensure that Graphcore’s technology works seamlessly with the AI ecosystem and at scale. We build reference applications, contribute to key software libraries e.g. optimising kernels for efficiency on our hardware, and collaborate with the Research team to develop