Systems Research Engineer Intern Jobs

With a strong security engineering background, you’re looking for a role that gives you the freedom to increase MongoDB’s resonance with customers by strengthening our core database products. You’re passionate about solving hard security engineering problems while putting a strong emphasis on customer experience, leveraging your own significant experience. You enjoy collaborating with different teams to innovate and implement pragmatic solutions. Who We Are The MongoDB Product Security organization is a diverse collection of individuals working together to scale MongoDB’s security, both security of the products themselves and the security features we offer to customers. The team is responsible for the MongoDB Database Server ( Community and Enterprise editions). The MongoDB Product Security organization works with software engineers to design, implement, and operate systems in a manner that protects customer data. It is a multidisciplinary team that covers product, software, cloud, infrastructure, and operational security concerns. The team does the following: Build a developer driven security program where there is tight integration with engineering artifacts, process, and tooling. Use software architecture and coding patterns to reduce the impact of security issues. Be security subject matter experts for our tech stack and products. We are looking to speak to candidates who are based in Dublin for our hybrid working model. Responsibilities You will take ownership, define strategy, and drive improvement for parts of our program such as fuzzing, threat modeling, secrets management, or container security Advocate for and lead complex security projects from inception through completion Drive architecture, patterns, and processes across Server Engineering that make security the easiest path Partner closely with engineering teams to design and implement security controls across our software and systems Research and POC new attacks against our systems. Plan and per

We are looking for an AI Infra engineer to join our growing team. We work with Kubernetes, Slurm, Python, C++, PyTorch, and primarily on AWS. As an AI Infrastructure Engineer, you will be partnering closely with our Inference and Research teams to build, deploy, and optimize our large-scale AI training and inference clusters. Responsibilities Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads Manage and optimize Slurm-based HPC environments for distributed training of large language models Develop robust APIs and orchestration systems for both training pipelines and inference services Implement resource scheduling and job management systems across heterogeneous compute environments Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands Qualifications Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization Experience with deploying and managing distributed training systems at scale Deep understanding of container orchestration and distributed systems architecture High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies) Experience managing GPU clusters and optimizing compute resource utilization Required Skills Expert-level Kubernetes administration and YAML configuration management Proficiency with Slurm job scheduling, resource mana

We are looking for an AI Infra engineer to join our growing team. We work with Kubernetes, Slurm, Python, C++, PyTorch, and primarily on AWS. As an AI Infrastructure Engineer, you will be partnering closely with our Inference and Research teams to build, deploy, and optimize our large-scale AI training and inference clusters Responsibilities Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training workloads Manage and optimize Slurm-based HPC environments for distributed training of large language models Develop robust APIs and orchestration systems for both training pipelines and inference services Implement resource scheduling and job management systems across heterogeneous compute environments Benchmark system performance, diagnose bottlenecks, and implement improvements across both training and inference infrastructure Build monitoring, alerting, and observability solutions tailored to ML workloads running on Kubernetes and Slurm Respond swiftly to system outages and collaborate across teams to maintain high uptime for critical training runs and inference services Optimize cluster utilization and implement autoscaling strategies for dynamic workload demands Qualifications Strong expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management Hands-on experience with Slurm workload management, including job scheduling, resource allocation, and cluster optimization Experience with deploying and managing distributed training systems at scale Deep understanding of container orchestration and distributed systems architecture High level familiarity with LLM architecture and training processes (Multi-Head Attention, Multi/Grouped-Query, distributed training strategies) Experience managing GPU clusters and optimizing compute resource utilization Required Skills Expert-level Kubernetes administration and YAML configuration management Proficiency with Slurm job scheduling, resource manag

Perplexity is looking for an Applied AI Engineer to design, build, and iterate on cutting-edge agents powering our core experience in Perplexity Computer. Working in this mission critical team, you will develop frontier context layer applications - fulfilling the curiosity of millions of users across the globe. Key Responsibilities Apply state-of-the-art ML and LLM techniques to solve problems spanning: Personalization (LLM memory, context summarization, retrieval and ranking); Contextual recommendations and Monetization applications Build frontier agent capabilities on top of Perplexity Computer Build auto research harness for both offline and online techniques, designing experiments and metrics that provide deep insight into quality and impact. Own the entire model lifecycle from research to production: data analysis, modeling, evaluation, offline/online A/B testing, and iterative improvement and build autonomous harness for agent squad to explore different problem spaces. Collaborate cross-functionally with engineers, PMs, data scientists, and designers to ensure our AI drives meaningful product improvements. Stay at the forefront of ML/AI innovation by evaluating and incorporating emerging research and algorithms into the product lifecycle. Preferred Qualifications 5+ years experience building and shipping robust AI products for large-scale, user-facing or data-driven products. Strong software engineering skills (Python, production-quality codebases, collaborative development) and experience using agentic coding tools for large scale parallel developments. In-depth experience with the full AI lifecycle: data analysis, rigorous evaluation, and ongoing monitoring/improvement. Proven collaborator and communicator; excels in high-velocity, cross-functional teams. Curious, driven by end-user/product impact, and passionate about advancing the state of applied ML and AI. BS, MS, or PhD in Computer Science, Engineering, or related field (or equivalent experience). Bonus

About Us Oliv.AI is a SalesTech global startup headquartered in San Francisco, debuting the world's first team of AI Agents for sales. With our recent $5.2M Seed funding, we solve one of the biggest problems for revenue teams: unreliable deal data. Oliv captures Deal Intelligence from every meeting, call, and email—without any rep involvement. The result is a clear, detailed view of every deal, presented in scorecards built on trusted sales methodologies like MEDDICC, BANT, and SPICED. Our AI agents are built for sales teams—sales managers, AEs, and RevOps—handling the work that takes them away from selling. With Oliv AI, sales teams can bring back focus on deals, strategy and conversation. The role This is not a traditional marketing role, and it is not a pure engineering role either. You will decide which accounts matter, build the systems that research them, write the content that reaches them, build the partnerships that amplify it, and turn all of that into qualified pipeline. Two things have to be true about you at once. You are technically sharp enough to build your own workflows, wire up your own APIs, and ship a working system without waiting on engineering or anyone else. Nothing you own should be blocked on a single person, including us. And you are obsessed with distribution — content, syndication, social, and partnerships — because pipeline goes to whoever the buyer already trusts. That means newsletters, communities, podcasts, comparison pages, partner audiences, and the people who influence them. What you will own GTM strategy and experimentation Develop and continuously refine Oliv's ICP, market segments, buyer personas, and account-selection criteria. Translate company goals into specific growth hypotheses and campaign plans. Identify new audiences, buying signals, use cases, and distribution opportunities. Build a structured experimentation roadmap across content, social, syndication, partnerships, outbound, and ABM. Define success metric

About the Team The Privacy Engineering team builds the systems and technical foundations that govern how user data is understood, retained, accessed, and used across OpenAI. We partner with Product, Data, Infrastructure, Security, and Legal to translate policy and trust commitments into durable architecture and enforceable controls. Our work spans data inventory and mapping, classification and lineage, retention and deletion, access governance, purpose and usage controls, auditability, and lifecycle automation. We aim to make policy-aligned data handling the default while giving teams clear, reliable primitives for building and operating products at scale. About the Role We are looking for an experienced Software Engineer to drive the architecture and execution of user data governance across OpenAI. You will define technical direction, build shared platforms and controls, and lead cross-functional programs that make data flows discoverable, policies enforceable, and ownership explicit. This role is well suited to a senior engineer who can move between deep systems design and organization-wide influence, turn ambiguous requirements into pragmatic roadmaps, and operate high-trust systems end to end. This position is based in San Francisco. Relocation assistance is available. In this role, you will: Set the technical strategy and architecture for user data governance across data mapping, classification, lineage, retention, deletion, access, and permitted usage. Design and build shared services, APIs, metadata systems, and policy-enforcement mechanisms that make governance controls consistent, scalable, and auditable. Establish reliable inventories of user data, system ownership, data flows, and policy applicability across products, infrastructure, analytics, and research systems. Partner with Product, Data, Infrastructure, Security, and Legal leaders to define decision rights, translate requirements into controls, and drive adoption across teams. Own governance systems

About the Team DoorDash's MDX team sits within Machine Learning Platform and builds the shared foundation that ML engineers across DoorDash, Wolt, and Deliveroo rely on to build and run ML-powered products. We work across the full ML lifecycle: feature engineering, training, evaluation, deployment, serving, and monitoring. Our mission is to increase the velocity and reliability of ML-driven business impact across the company. A central pillar of that work is agentic infrastructure. We are building systems that let agents accelerate and operate the ML workflow itself, including auto research, where agents explore and iterate on modeling ideas, and self-healing systems that detect, diagnose, and remediate failures in ML pipelines and production models with minimal human intervention. About the Role You will join a small, high-leverage team building the agentic layer of DoorDash's ML infrastructure. You'll design and build the systems that let agents accelerate how ML engineers across DoorDash, Wolt, and Deliveroo develop, ship, and operate models. That includes auto research systems that explore and iterate on modeling ideas, and agentic developer support that helps ML engineers move through training, evaluation, deployment, and debugging faster. You'll own well-scoped components end to end, from design through production, working across agent orchestration, tooling and integrations with our ML platform, evaluation of agent behavior, and observability. This role is ideal for an engineer with a strong backend foundation who is excited to apply AI agents to real engineering workflows, learns quickly, and wants to grow into technical leadership in a fast-moving area where model capabilities, tooling, and best practices change month to month. You're excited about this opportunity because you will… Build agentic systems that increase the velocity of ML development across DoorDash, Wolt, and Deliveroo, helping ML engineers get from idea to production model faster. Design an

ABOUT CUBIST Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources. WHAT YOU'LL DO The Quant Developer will be responsible for building and maintaining the research infrastructure and backtesting frameworks that support quantitative research, working closely with researchers to translate research ideas into robust, scalable, and maintainable research systems. The role spans multiple areas, including system design, data pipeline development, and the development of research and backtesting frameworks. WHAT'S REQUIRED Background in Electrical Engineering, Computer Science, or a related field, with 3+ years of software development experience as a software engineer. Strong programming ability in C++ and Python. Solid command of CI/CD practices and tooling, with the ability to design and maintain automated testing and build pipelines for research and backtesting environments. Practical knowledge and hands-on experience with distributed computing and distributed job scheduling / task assignment. Excellent analytical and engineering problem-solving skills. Demonstrated ability to work independently on large datasets and large codebases. A strong team player who works closely with researchers to translate research ideas into stable, maintainable research systems. Willingness to take ownership of your work, able to operate both independently and within a small team, while remaining careful, detail-oriented, and capable of performing well under pressure. Strong English communication skills. Commitment to the highest ethical standards. We take care of our people We invest in our people, their careers, their health, and their well-being. When you work here, we provid

About the Team This team builds and operates the systems that enable OpenAI researchers to run reliable, scalable, and efficient research workflows. The team sits close to research and works across infrastructure, systems, and automation to make sure researchers have the tools and environments they need to move quickly. The work spans software engineering, infrastructure, systems administration, cluster operations, and reliability engineering. As OpenAI’s infrastructure evolves from bespoke bare-metal systems toward more standard, scalable platforms, the team needs engineers who can understand how systems work end-to-end and build the right abstractions without reinventing the wheel. About the Role As a Software Engineer on this team, you will build and operate the infrastructure that supports frontier research and critical research-facing systems. You will work on systems that sit close to the metal, but the role is not limited to classic operations or sysadmin work. We are looking for someone who can reason about networking, bootstrapping, Kubernetes, scalability, automation, and reliability - while also writing software to make these systems better over time. This role is a strong fit for an independent, high-ownership engineer who enjoys reliability-heavy infrastructure work but still wants to build. You do not need to come in as a kernel expert or highly algorithmic optimization engineer, but you should be deeply curious about infrastructure, comfortable debugging complex systems, and excited to support researchers doing novel work. We expect you to: Build and operate reliable infrastructure for research workloads and research-facing services. Support and improve systems across data infrastructure, processing, crawl and ingest, caching, search, observability, and clusterwide services. Improve cluster bootstrapping, provisioning, automation, and deployment workflows. Debug issues across networking, compute, storage, orchestration, and service reliability layers.

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 Research Productivity organization builds the infrastructure and systems that accelerate research across Anthropic, including the tools that help our research process get faster and more effective over time. We're looking for an experienced software engineer to join us as we scale this infrastructure at a moment when both demand and scope are growing extremely quickly. This team operates like a startup within a startup. You'll be a strong fit if you like thinking from first principles, iterating fast on infrastructure, and tackling reliability and scalability challenges head-on as the products built on top of your systems evolve underneath you. You'll independently scope complex, multi-month projects, drive cross-org alignment through ambiguous problem spaces, and make the architectural decisions that shape how the infrastructure behind our research tooling gets built. You'll partner directly with research teams to understand their workflows, anticipate how their requirements will change, and design and scale infrastructure that can keep pace. Responsibilities Design, build, and scale infrastructure and systems that support rapidly increasing usage, where requirements and workload continue to evolve as the products built on top of them evolve Independently scope and lead complex, multi-month engineering projects, from an ambiguous starting point through to a production system Drive cross-organizational alignment on technical direction, working through ambiguous problem spaces with multiple stakeholders and teams Make architectural decisions that shape the foundation of research infrastructure and tooling acr

We anticipate the application window for this opening will close on - 10 Oct 2026 Careers that change lives start here. Medtronic is a global leader in healthcare technology with a Mission to alleviate pain, restore health, and extend life. Our 95,000 employees work across more than 150 countries to put patients first — developing innovative medical technologies that improve the lives of 72+ million patients each year. Your unique talents will help shape the future of healthcare while building a career grounded in purpose, growth, and impact. A Day in the Life The Neuromodulation Research & Development organization develops therapies and technologies that address chronic pain and other neurological conditions. Within the Interventional Pain portfolio, teams focus on minimally invasive therapies used to treat musculoskeletal pathologies, including vertebral compression fractures, nerve pain, metastatic bone tumors, and benign bone tumors. This role supports the development of system-level verification and validation strategies for new products and enhancements across mechanical, electrical, and software components. This position is based in Santa Clara, California, and follows an onsite work model. No travel is required for this role. As a Research & Development Test Engineer, you will lead and execute verification and validation activities that support the development of interventional pain therapies and associated medical device systems. You will collaborate with cross-functional partners to define test strategies, evaluate product performance, manage technical risks, and generate evidence needed to support product development and regulatory requirements. Primary Responsibiliti

Staff Software Engineer - AI Research Infrastructure P-1215 At Databricks, we are obsessed with enabling data teams to solve the world’s toughest problems, from security threat detection to cancer drug development. We do this by building and running the world’s best data and AI platform so our customers can focus on the high-value challenges that are central to their own missions. The Databricks AI Research organization enables companies to develop AI models and agents using their own data, with technologies ranging from post-training open source LLMs to developing advanced multi-agent architectures. Databricks AI does so by producing novel science and putting it into production. Databricks AI is committed to the belief that a company’s AI models and agents are just as valuable as any other core IP, and that high-quality AI should be available to all. Job Description As a Staff Software Engineer, AI Research Infrastructure, you will be developing and running the research stack that powers Databricks AI Research. You will design and build services that schedule, orchestrate, and observe large‑scale training and inference experiment workloads across thousands of GPUs, improve our dev tooling and ensure that researchers can iterate quickly without sacrificing reliability, efficiency, or security. You’ll partner closely with research scientists, ML engineers, and platform teams to turn experimental workloads into robust, repeatable pipelines, and to push the limits of what our infrastructure can support. The Impact you will have As a Staff Software Engineer on the AI Research Infra Team at Databricks, you will: Design and implement infrastructure that supports large‑scale experiments, data processing, and model training (e.g., HPC clusters, GPU fleets, or cloud‑based systems) Enable researchers to go from idea to large‑scale experiment in minutes, not days, by building powerful abstractions for job submission, scheduling, and monitoring. Create tooling that improv

Staff Software Engineer - AI Research Infrastructure P-1215 At Databricks, we are obsessed with enabling data teams to solve the world’s toughest problems, from security threat detection to cancer drug development. We do this by building and running the world’s best data and AI platform so our customers can focus on the high-value challenges that are central to their own missions. The Databricks AI Research organization enables companies to develop AI models and agents using their own data, with technologies ranging from post-training open source LLMs to developing advanced multi-agent architectures. Databricks AI does so by producing novel science and putting it into production. Databricks AI is committed to the belief that a company’s AI models and agents are just as valuable as any other core IP, and that high-quality AI should be available to all. Job Description As a Staff Software Engineer, AI Research Infrastructure, you will be developing and running the research stack that powers Databricks AI Research. You will design and build services that schedule, orchestrate, and observe large‑scale training and inference experiment workloads across thousands of GPUs, improve our dev tooling and ensure that researchers can iterate quickly without sacrificing reliability, efficiency, or security. You’ll partner closely with research scientists, ML engineers, and platform teams to turn experimental workloads into robust, repeatable pipelines, and to push the limits of what our infrastructure can support. The Impact you will have As a Staff Software Engineer on the AI Research Infra Team at Databricks, you will: Design and implement infrastructure that supports large‑scale experiments, data processing, and model training (e.g., HPC clusters, GPU fleets, or cloud‑based systems) Enable researchers to go from idea to large‑scale experiment in minutes, not days, by building powerful abstractions for job submission, scheduling, and monitoring. Create tooling that improv

Sendbird is building AI agents for customer experience. Our platform already powers billions of conversations every month across chat, voice, video, and messaging APIs. We are now using that foundation to build agents that understand customer context, reason over business data, and take reliable action in production. We are looking for a Machine Learning Engineer to research, build, and productionize new capabilities for those agents. This role sits at the intersection of agent product development, applied AI research, and production engineering. You will work on systems that enterprise customers depend on every day, not demos or isolated prototypes. About Sendbird and delight.ai Sendbird has spent more than a decade building communication infrastructure for in-app chat, voice, video, and messaging APIs. More than 4,000 brands use our platform, including DoorDash, Match Group, Noom, Yahoo Sports, and Rakuten. Our systems support more than 7 billion messages every month. In 2024, we made a strategic shift toward AI-first customer experience. In 2025, we launched our enterprise AI agent product, delight.ai. Delight.ai helps businesses deliver customer support and engagement that is faster, more contextual, and more personal. Unlike simple FAQ bots, our agents are built to remember customer context, use tools, retrieve relevant knowledge, connect across channels, and handle real customer workflows with accuracy and control. The Role As a Machine Learning Engineer, you will design, build, evaluate, and ship new capabilities for our AI agents. You will work across agent architecture, retrieval, memory, planning, tool use, workflow automation, voice, evaluation, data pipelines, model adaptation, inference, and production integration. This is a hands-on engineering role for someone who can turn AI research and product ideas into reliable customer-facing features. Some problems will require training, fine-tuning, or adapting models. Others will require better retrieval, bet

Hardware & Software Integration Engineer (Associate or Mid-Level) Company: The Boeing Company Boeing Defense, Space and Security (BDS) is looking for an Associate or Mid-Level Hardware & Software Integration Engineer (Level 2 or 3) to join our exclusive team at the Starfire Optical Range on Kirtland AFB in Albuquerque, New Mexico. We are seeking a highly skilled individual with experience in software design for hardware integration, and digital signal processing. This position will support the hardware, firmware, and software development of electro-optical systems. This is a unique opportunity to contribute to advancements in technology using cutting-edge equipment at a historically significant site. This position supports the R&D mission of the Air Force Research Laboratory Optics Division in their advancement of Space Domain Awareness capabilities. The site houses telescope research facilities ranging in size from 0.5m up to 3.5m with advanced optical systems. You will work as part of a multidisciplinary team supporting the development, optimization, and maintenance of embedded electro-mechanical pointing and tracking control systems. Starfire Optical Range is a world class research facility with recognized achievements, enabled by the quality, enthusiasm, and dedication of our entire team. Our team consists of engineers and technicians in all disciplines required to plan, design, integrate, and test complex optical, electronic, and quantum systems to achieve research objectives. Position Responsibilities: Support hardware and software research development projects Work in a team environment to develop specifications of firmware and software architectures Re