Systems Research Engineer Intern Jobs

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 and publish novel ideas in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications. If you're excited about advancing the next gen

About the Team The Fleet team builds core components to enable productive research from small to state of the art scale across OpenAI, with the goal of accelerating progress towards AGI. We frequently collaborate with other teams to speed up the development of new state-of-the-art capabilities. About the Role As we scale up with more researchers and engineers joining OpenAI, we seek a pragmatic and passionate engineer with a strong focus on the development experience for both engineers and scientists. In this role, you will be responsible for building and maintaining systems that allow our research + engineering organization to iteratively develop, test, and deploy new features reliably, with high velocity, and with a frictionless and fast development cycle. You will help oversee and drive to the vision of how we should build, test and deploy software. You will drive the design of our continuous integration pipelines, testing infrastructure, training and support around our build system. Our current environment relies heavily on Python, Rust, and C++, which you will take ownership of and strive to transform into a state of the art development experience for research. Ultimately, your role will be to provide the necessary tools and metrics to support our fast-paced culture and ensure a stable, scalable platform for growth, while also fostering a seamless and low friction experience for OpenAI’s research. This role is based in San Francisco, CA. For a San Francisco role, we use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. You might thrive in this role if you: Have supported large monorepo development and deployment before Are a proficient Python programmer working in large monorepos Are proficient with Docker and Kubernetes Experienced in CI/CD About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boun

About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible. At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI. Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here . At Pinterest Labs , you'll work on tackling new challenges in machine learning and multi-modal large language models along with a world-class team of research scientists, and machine learning engineers. You'll conduct research that can be applied across Pinterest engineering teams and engage in external collaborations and mentoring, while also performing research in any of the following areas: natural language processing (NLP) and reasoning capability, computer vision for multi-modality, graph neural network, inclusive and responsible AI, reinforcement learning, user modeling, and recommender systems. What you’ll do: Contribute to cutting-edge research in machine learning and LLM that can be applied to Pinterest problems, especially search agent, recommendation agent, reason and planning agent Collect, analyze, and synthesize findings from data

Role: Application Reliability Engineer Location: Gurgaon Who we are Graviton Research Capital is a privately funded quantitative trading firm striving for excellence in financial markets research. We trade across a multitude of asset classes and trading venues using a diverse range of concepts, from time series analysis and stochastic models to machine learning and statistical inference. We analyse terabytes of data to identify pricing anomalies and drive innovation in financial markets. Key Responsibilities and Deliverables The ideal candidate will possess a strong background in technical support, with a passion for problem-solving and a commitment to excellence. As an Application Reliability Engineer, you will be responsible for: Monitor production services and respond quickly to alerts, incidents, and outages to ensure smooth operation and minimal downtime. Monitor trading systems and infrastructure., Triage issues across trading support services, databases, and infra; escalate and coordinate with the right owners, and drive root-cause analysis and ensure fixes are implemented for long-term stability. Serve as the first line of defense for trading operations. Proactively identify, address recurring issues, and build automation to reduce manual intervention. Improve observability by enhancing monitoring, logging, and alerting systems. Develop and maintain operational runbooks and SLO/SLA metrics. Eligibility and Required Skills Possess a degree in a highly analytical field, such as Engineering, or Computer Science 2-5 years of experience in Python, Shell/Bash scripting. Experience with Linux and shell/bash online tools. Hands-on experience with databases (SQL, NoSQL) Strong problem-solving and analytical skills Excellent communication skills Ability to remain calm and analytical under production pressure Good to have: Familiarity with monitoring/alerting stacks (Prometheus, Grafana, ELK, etc.) Familiarity with distributed messaging (Kafka) and caching systems (Red

About the Role: We are looking for a Trade Operations Engineer to join our operations team at NK Securities. This is a hands-on role where you'll help make sure our trading systems are ready every day. You’ll work closely with our tech and trading teams to keep things running smoothly. This is a great opportunity for someone who wants to learn, grow, and take on more responsibility over time. You will be trained by experienced team members and have the chance to lead the operations team in the future. What You’ll Do: Perform daily system checks and ensure everything is running fine before markets open Monitor trading systems and raise alerts if anything looks off Write and improve scripts (mostly in Bash and Python) to reduce manual work Coordinate with tech and trading teams for any issues or updates Work on frontend tools if needed (basic web frontend knowledge is a plus) Take ownership of tasks and make sure nothing is missed What We’re Looking For: Good knowledge of Bash and Python Basic understanding of frontend (HTML/JS/CSS or React is a bonus) Willingness to learn and work hard Responsible and reliable – our systems need to be up and running on time Someone who likes solving problems and automating boring stuff Freshers or candidates with 0–2 years of experience are welcome Why Join Us: Work in a fast-paced and exciting HFT environment Learn directly from experienced developers and traders Opportunity to grow into a leadership role in the ops team Flat team structure – your work will be noticed and appreciated Competitive salary and good learning curve

Description: Graviton is a privately funded quantitative trading firm striving for excellence in financial markets research. We are seeking a Software Engineer for our team in Gurgaon. Our Core Technology team has some of the best programmers in India working on cutting edge technologies to build a super fast and robust trading infrastructure handling millions of dollars worth of trading transactions every day. Requirements Contribute to all layers of backend systems including databases, APIs and applications Design, build and maintain applications for business requirements Architect scalable and reliable applications Write clean and modular code, following good coding standards and practices Troubleshoot and debug applications Be involved in the entire application lifecycle Collaborate with multidisciplinary team of front-end developers, engineers and system administrators Devise innovative solutions to address new and complicated challenges Build reusable code and libraries for future use Take lead on projects, as needed Work in a high paced competitive environment Qualifications The ideal candidate will have: Engineering degree in Computer Science (preferred) or any other discipline from a Tier 1 college. 3 to 4 years of relevant experience in designing and improving information systems Proficiency in Python programming Extensive experience working with relational databases and handling large datasets Good Understanding of object oriented and asynchronous programming Familiarity with front-end languages such as HTML, JavaScript and CSS Knowledge of Linux systems and bash scripting Good communication and interpersonal skills Willingness to continuously learn and improve Ability to work in fast paced environment under pressure and manage multiple high priority projects Good to have: Experience in Financial services space/domain Experience in and understanding of system design decisions Experience in leading small teams or projects Benefits: Our open and casua

About the Team pAGI Infra team builds and operates the systems that make large-scale model training and evaluation reliable, efficient, and easy to run. Our work spans distributed training infrastructure, inference and grading platforms, compute scheduling, and research tooling. We partner closely with researchers and engineering teams to turn new research needs into dependable infrastructure, improve GPU efficiency, and shorten the path from an experiment to a validated model. About the Role We’re looking for an AI Systems Engineer to help scale the infrastructure behind our training and evaluation workflows. You’ll own projects from identifying bottlenecks and designing solutions through deployment and operation. The work combines distributed systems engineering, performance optimization, and close collaboration with researchers. You might build a shared grading service, improve resource allocation across workloads, or bring a new training stack into production — directly improving how quickly and reliably research moves forward. In this role, you will: Build and operate infrastructure for large-scale training and evaluation, improving reliability, throughput, and resource efficiency. Develop shared inference and grading platforms with automated capacity management, health monitoring, and visibility into performance. Improve compute scheduling and resource allocation to reduce idle GPU time and help workloads recover quickly from failures. Diagnose bottlenecks across training, inference, and orchestration, and work across teams to improve end-to-end performance. Build self-service tools, automated validation, and observability that help researchers launch experiments, diagnose issues, and compare results with less manual intervention. You might thrive in this role if you: Are excited about the potential of personal AGI and want to build the infrastructure that enables it. Have strong software engineering fundamentals and experience building or operating large-scal

As a Senior Software Engineer at Coder, you’ll build and evolve the systems behind our agentic development experience. You’ll work across the agent harness, integrations, and workflows that connect agents with real development environments. You’ll stay hands-on, solve complex technical problems, and work closely with Product, Design, and other engineers to ship reliable agentic experiences. What you’ll do here Design and build production systems in Go, with work across React and TypeScript where needed. Improve agent execution, tool use, context management, streaming, and long-running workflows. Extend our provider-agnostic architecture as models and capabilities change. Build reliable integrations between agents, workspaces, tools, and developer infrastructure. Own projects from implementation through rollout and iteration. Contribute to design reviews, code reviews, and technical discussions. Partner with Product and Design to turn agent capabilities into useful developer experiences. Improve the reliability, performance, and operability of agentic systems. What we’re looking for Strong experience building and operating production software systems. Hands-on experience with Go. Experience with React and TypeScript. Experience building systems around LLMs or agentic workflows. Familiarity with model APIs, tool calling, context management, or agent loops. Good understanding of distributed systems and production reliability. Working knowledge of AWS. Strong problem-solving skills and comfort working through technical ambiguity. Someone who contributes beyond their own code through reviews, collaboration, and knowledge sharing. Bonus tacos if you have Experience building coding agents, developer tools, or cloud development environments. Experience with MCP, agent tools, or multi-agent systems. Experience with remote execution, sandboxing, or isolated compute. Experience building integrations across multiple model providers. Experience with AWS, Kubernetes, Docker, or L

As a Senior Software Engineer on Coder’s Engineering team, you’ll build and evolve the systems behind our agentic development experience. You’ll work across the agent harness, integrations, and workflows that connect agents with real development environments. You’ll stay hands-on, solve complex technical problems, and work closely with Product, Design, and other engineers to ship reliable agentic experiences. What you’ll do here Design and build production systems in Go, with work across React and TypeScript where needed. Improve agent execution, tool use, context management, streaming, and long-running workflows. Extend our provider-agnostic architecture as models and capabilities change. Build reliable integrations between agents, workspaces, tools, and developer infrastructure. Own projects from implementation through rollout and iteration. Contribute to design reviews, code reviews, and technical discussions. Partner with Product and Design to turn agent capabilities into useful developer experiences. Improve the reliability, performance, and operability of agentic systems. What we’re looking for Strong experience building and operating production software systems. Hands-on experience with Go. Experience with React and TypeScript. Experience building systems around LLMs or agentic workflows. Familiarity with model APIs, tool calling, context management, or agent loops. Good understanding of distributed systems and production reliability. Working knowledge of AWS. Strong problem-solving skills and comfort working through technical ambiguity. Someone who contributes beyond their own code through reviews, collaboration, and knowledge sharing. Bonus tacos if you have Experience building coding agents, developer tools, or cloud development environments. Experience with MCP, agent tools, or multi-agent systems. Experience with remote execution, sandboxing, or isolated compute. Experience building integrations across multiple model providers. Experience with AWS, Kuber

As a Staff Software Engineer on Coder’s Agentic Engineering team, you’ll shape the systems behind our agentic development experience. You’ll work across the agent harness, integrations, and workflows that connect agents with real development environments. You’ll stay hands-on while setting the team's technical direction. You’ll lead complex work, make sound architectural decisions, and help other engineers do their best work. What you’ll do here Set technical direction across Coder’s agent harness, integrations, and workflows. Design and build production systems in Go, with work across React and TypeScript where needed. Evolve agent execution, tool use, context management, streaming, and long-running workflows. Extend our provider-agnostic architecture as models and capabilities change. Lead complex projects from early ambiguity through production. Raise the engineering bar through design reviews, code reviews, and technical mentorship. Partner with Product and Design on clear, useful agent experiences. Improve the reliability, performance, and operability of agentic systems. What we’re looking for Deep experience building and operating production software systems. Strong hands-on experience with Go. Experience with React and TypeScript. Hands-on experience building systems around LLMs and agentic workflows. Experience with model APIs, tool calling, context management, or agent loops. Strong distributed systems knowledge. Working knowledge of AWS. A track record of setting technical direction without formal authority. Strong architectural judgment and comfort working through ambiguity. Someone who makes the engineers around them better. Our tech stack Backend: Go, Postgres Frontend: TypeScript, React Infrastructure: AWS, Kubernetes Observability: Prometheus, Grafana CI/CD: GitHub Actions Bonus tacos if you have (Tacos? If you need an ice-breaker, ask how we say thanks by giving tacos!) Experience building coding agents, developer tools, or cloud development environm

As a Senior Backend Engineer on Coder's Enterprise Experience team, you'll build the systems that help large organizations run Coder in production with confidence. You'll improve how Coder scales, how it's upgraded, and how reliably it performs in regulated, air-gapped, and enterprise environments. You'll work on a genuinely cross-functional team of backend, platform, and QA engineers who own the end-to-end experience for Coder operators. From designing new features to evolving Coder's architecture, you'll partner across engineering and product to solve complex problems and ship software that operators trust. What you'll do here Design and build new features end to end, from technical design through production rollout. Design and implement backend architecture changes that support Coder's long-term scalability goals. Investigate and resolve scalability bottlenecks under production-like load, from database access patterns to concurrency handling in coderd. Improve database migration safety and upgrade reliability through schema compatibility, background migrations, and safe rollback strategies. Own the backend side of issues surfaced by Coder operators and administrators. Document the design, implementation, and operational tradeoffs of the systems you build. Participate in code reviews, RFC-style design discussions, and on-call rotations for the services you own. What we're looking for 5+ years of professional software engineering experience, including significant production experience with Go. Deep understanding of Go's concurrency model, including goroutines, channels, the sync package, and debugging race conditions under real-world load. Experience designing and operating relational databases in production, including schema design, migrations, and transactions. Strong verbal and written communication skills. Exceptional debugging and troubleshooting skills, with the persistence to drive complex problems to resolution. A self-motivated, analytical engineer who enj

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic's Environments organization builds and maintains the infrastructure that improves Claude’s capabilities through reinforcement learning. That includes the frameworks researchers use to build environments and the infrastructure responsible for running them. The team's mission is to productionize research. You'll embed with research teams, get up to speed on how they work, and design the frameworks and APIs that let them move faster, building systems the team can understand, own, and maintain themselves. Scope also includes keeping production RL runs healthy, maintainable, monitored, and easy to triage. You'll be a strong fit if you have deep expertise in Python, a refined sense of taste for API and framework design, and good intuition for how complex systems fail, especially silently. It's a bonus if you've built and operated a stateful distributed system, such as a workflow engine, actor framework, or durable-execution runtime, where correctness depends on getting shared state and recovery right. You should be comfortable diving into messy research code, finding the abstractions that matter, and improving them incrementally while researchers continue to build on your work. You should also be comfortable using AI tools to accelerate your own development, but have an impulse towards deep verification. Key responsibilities Design widely used APIs, frameworks, and abstractions that other engineers and researchers build on, making correct usage the default and ruling out entire classes of errors structurally Own the platform layers that sit beneath every environment, including the agent runtime Build the

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Every time Claude runs code, uses a computer, or works as an agent, it does so inside a sandbox. Our Sandboxing team builds the isolation layer behind that work. The same systems run the code Claude writes for users, the environments we train and evaluate models in, and the agents our customers deploy. These workloads are untrusted by design, and there are a lot of them. Sandboxes need to start in milliseconds, hold up under adversarial code, and scale to a very large number of concurrent sessions. You will work at the boundary between the Linux kernel, virtualization, and the container ecosystem. You will partner with research, product, and security teams to make sure that safe and fast do not trade off against each other. Key responsibilities Design, build, and operate the sandboxing infrastructure that runs untrusted, model-generated code and agent workloads at scale Improve sandbox startup time, density, and throughput, including snapshot and restore, checkpointing, and warm pools Strengthen isolation across compute, filesystem, and network, including egress controls and per-session credential handling Investigate and fix performance, reliability, and security issues at the kernel, hypervisor, and container runtime level Build the monitoring, tracing, and debugging tools that show where time and resources go inside a sandbox Work with researchers to support reinforcement learning environments and evaluations that need fast, reproducible, isolated execution Work with product and platform engineers to expose sandboxes as a dependable, well-designed capability for Claude products and customers Partner with security tea

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role: The Encodings Infra team maintains the libraries that engineers and researchers across Anthropic use to encode text and multimodal data into a form that Claude can consume. It also determines Claude’s prompt shape: how a user’s turn is represented to the model, how Claude calls tools and receives tool results, and so on. As a Software Engineer on this team, you'll own the design and maintenance of these libraries—keeping their APIs intuitive, their performance sharp, and their abstractions solid enough that most of the org never has to think about encodings or prompt structures at all. You’ll have the satisfaction of knowing that your work enabled Claude to learn new ways of understanding the world. This role is unusually broad: your work will touch systems across the codebase, from pretraining to finetuning to the API, and you'll collaborate closely with both researchers and engineers to make sure new encoding ideas can move quickly from experiment to production. Responsibilities: Maintain and improve the encoding libraries used by engineers and researchers across Anthropic Run experiments to determine the optimal way to feed structured data into Claude without confusing it Design data structures and abstractions that shield most of the organization from the details of how encoded data works while enabling “power users” Adapt the encoding libraries to support new research directions as they emerge, and make sure that we can ship these research ideas to production Optimize encoding performance across the systems that depend on these libraries You may be a good fit if you: Have 5+ years of software engineering experienc

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. Performance Engineer, Inference Engine About the Role Anthropic's inference engine is the software between the accelerator kernels and the routing layer. It manages the entire token path in between: batching requests, laying the model out across chips, managing memory for weights and activations, coordinating every forward pass, and managing model state across requests. Built in-house, it runs on all of our accelerator platforms, serving Claude to millions of users and running our research workloads. You will work on building and optimizing this system at Anthropic scale: improving throughput, cost, reliability, and latency across all accelerator and cloud platforms. You are intimately familiar with the hardware and bandwidth numbers (FLOPs, HBM, PCIe, RDMA, network links, etc.) and can model a problem quickly: where the time and bytes go, and what sets the bound. The role is deeply technical and high-impact, and suits engineers who enjoy working across accelerator programming, high-performance systems that seamlessly coordinate between host and device, and large-scale distributed systems. Familiarity with the transformer architecture is a plus. Some example recurring themes: Keep device utilization high. Accelerators should never be waiting due to other overheads. Reuse instead of recompute. Keep model state cached and reuse it whenever that is cheaper than computing it again. Measure, model, then change. We build the observability to see where the gaps are, model the impact of potential improvements, deploy them, and go around again, with Claude speeding up every turn of that loop. Tokens you can trust. Ensuring model quality matter