Systems Research Engineer Intern Jobs

About Hark Hark is an artificial intelligence company building advanced, personalized intelligence. One that is proactive, multimodal, and capable of interacting with the world through speech, text, vision, and persistent memory. We're pairing that intelligence with next-generation hardware to create a universal interface between humans and machines. While today's AI largely operates through chat boxes and decade-old devices, Hark is focused on what comes next: agentic systems that interact naturally with people and the real world. To get there, we're developing multimodal models and next-generation AI hardware together - designed from the ground up as a single, unified interface for a new era of intelligent systems. About the Role As an Embedded AI Engineer, you will work closely with the AI research team to bring AI to Hark’s next-gen hardware. You will be responsible for the full AI stack on the device, including data ingestion, model development, optimization, and deployment on embedded devices. You should have deep understanding of the constraints of an embedded system (compute, memory, power etc) and leverage your expertise in both embedded system software development and AI model deployment to deliver production-ready ML solutions on hardware Responsibilities Build data collection and ingestion pipelines for an embedded system including various sensors, at scale Work closely with model teams to co-design model architectures that meets the required latency, memory, power, and bandwidth Work with platform vendors to bring up toolchains, SDKs and new accelerator to ensure efficient model deployment and optimization Integrate ML inference into embedded firmware written in C, C++, or Rust Profile and optimize memory usage, power consumption, and real-time performance Evaluate and select silicon platforms (GPUs, NPUs etc.) for Hark’s next gen on-device and edge deployment of a wide range of models Requirements 5 years of experience in machine learning

About the Team We’re hiring Software Engineers to join our broader Infrastructure organization, which supports multiple high-impact teams. Depending on your interests and experience, you could work on one of several focus areas—including Core Distributed Systems, Reliability Engineering, Observability, Developer Productivity or Cloud Infrastructure. About the Role All teams are deeply collaborative, work on mission-critical services, and are responsible for building distributed, scalable infrastructure to bring OpenAI’s technology to the world through products like ChatGPT and the OpenAI API. You’ll work closely with stakeholders to understand infrastructure, data and compute needs, setting the technical strategy that supports cutting-edge research and product development. This is a critical role for someone who is passionate about solving complex engineering problems at scale, ensuring their performance, scalability and reliability Team Focus Areas Distributed Systems: Owning and building important, highly scalable, available, performant, and reliable distributed systems (and their building blocks) to power the entire stack at OpenAI Systems Engineering: Work across layers of the stack—debugging system bottlenecks, evolving core infrastructure, and solving novel problems in performance and scalability. Reliability Engineering: Build scalable, fault-tolerant systems and lead efforts around service health, incident response, and resilience. Observability: Design and maintain observability tooling (metrics, logs, tracing) to give teams visibility into production systems at scale. Developer Productivity: Create tools, environments, and workflows that help engineers ship high-quality software faster and more safely. Cloud Infrastructure: Own the cloud-native infrastructure (compute, networking, storage) that underpins all services and research workloads. Databases: Building high performance, distributed database systems that power all of OpenAI's product stack. In this

About Us YipitData is the leading market research and analytics firm for the disruptive economy and most recently raised $475M from The Carlyle Group at a valuation of over $1B. Every day, our proprietary technology analyzes billions of alternative data points to uncover actionable insights across sectors like software, AI, cloud, e-commerce, ridesharing, and payments. Our data and research teams transform raw data into strategic intelligence, delivering accurate, timely, and deeply contextualized analysis that our customers — ranging from the world's top investment funds to Fortune 500 companies — depend on to drive high-stakes decisions. We operate globally with offices in the US (NYC, Austin, Miami, Mountain View), APAC (Hong Kong, Shanghai, Beijing, Guangzhou, Singapore), and India. Our award-winning, people-centric culture — recognized by Inc. as a Best Workplace for three consecutive years — emphasizes transparency, ownership, and continuous mastery. What It's Like to Work at YipitData YipitData isn't a place for coasting — it's a launchpad for ambitious, impact-driven professionals. From day one, you'll take the lead on meaningful work, accelerate your growth, and gain exposure that shapes careers. Ownership That Matters: You'll lead high-impact projects with real business outcomes Rapid Growth: We compress years of learning into months Merit Over Titles: Trust and responsibility are earned through execution, not tenure Velocity with Purpose: We move fast, support each other, and aim high — always with purpose and intention About The Role We're looking for a seasoned, high-output engineer who can operate within our product portfolio — from our Public Investor platform and Signals application to our AI-powered customer insights agent. You'll execute on well-defined work, but the expectation is that you go well beyond that — proactively identifying opportunities, improving systems, and driving outcomes that move the business forward. You'll be responsibl

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 We're looking for experienced engineers to build and scale the database infrastructure that powers Claude's products and Anthropic's research. As a Software Engineer on the Databases team, you'll architect and operate the systems that let millions of users interact with Claude while also supporting frontier AI research workloads. You'll help set the database strategy for Anthropic: designing systems that handle billions of API requests, building storage that runs cleanly across GCP, AWS, and a range of deployment models, and creating the reliable data layer that lets research move fast. The Databases team spans three areas: the core database platform (data plane and control plane), data movement (migrations, backfill, and change data capture), and caching. We're hiring across all three. Key Responsibilities Drive the technical direction for database solutions used across Product and Research Design and implement database solutions that scale to support millions of users across Claude's product ecosystem Build and scale database systems through 100x+ growth while maintaining reliability and performance Build the database platform that lets Anthropic engineers ship without thinking about databases or scaling. Architect data storage solutions that operate across GCP, AWS, first-party deployments, third-party deployments, and other environments Develop database infrastructure that serves both product and research workloads with different performance characteristics Build data movement infrastructure (migration tooling, backfill, and change data capture pipelines) that safely consolidates and moves data across the organizati

About the Team The DoorDash Research Fellowship is a 3-month program (extendable to 6 months) looking for Summer and Fall 2026 cohorts, for researchers and engineers who want to work on the hardest applied ML and AI problems in local commerce. Fellows are given the resources, autonomy, and access to real-world operational data needed to pursue ambitious research directions — with the goal of producing work that influences both the field and how DoorDash operates at scale. This program is modeled on the best external research fellowships: fellows are treated as independent researchers, not as junior employees on a product team. You pick the problem (within a set of priority areas), you own the direction, and you publish or ship the outcome. You’re excited about this opportunity because you will receive… Dedicated compute allocation sized to the research agenda — GPU clusters for training and inference budgets for experimentation Full access to DoorDash's research infrastructure — our internal RL stack, training and evaluation pipelines, RL environments built on real operational systems, agent evaluation harnesses, and the tooling our own research teams use day-to-day. Fellows are first-class users, not sandboxed visitors. Access to DoorDash operational data — real-world datasets spanning logistics, merchant operations, consumer behavior, and marketplace dynamics, under appropriate data governance Research mentorship from senior researchers and engineering leaders at DoorDash, plus a named research sponsor for each fellow who meets with you weekly and is accountable for unblocking your work Speaker series featuring leading researchers and practitioners from academia and industry — faculty from top ML programs, research leads from frontier AI labs, and senior operators from across tech. Fellows get dedicated 1:1 time with speakers when possible. A cohort of fellows working alongside you — a small, tight-knit group of researchers tackling different problems but sharing

About the Team The Codex Core Agent team builds the kernel of Codex. We own making the agent better, accelerating research, and making those improvements real in production for our users. That means working across the systems that make Codex actually function as an agent in the real world: the production performance envelope around tokens, latency, reliability, cost, and capacity; the core execution loop and interfaces that turn models into useful behavior; the shared infrastructure that enables other teams to build on Codex; and the feedback loops that turn real-world usage into better models and better agent behavior over time. About the Role We’re looking for engineers to build the infrastructure that powers Codex agents in production. This role focuses on the systems that let models safely execute code, interact with tools, complete long-running tasks, and operate reliably and efficiently at scale. You’ll design and operate the infrastructure behind sandboxed execution, orchestration, stateful workflows, app-server and SDK boundaries, and model rollouts. You’ll work at the intersection of distributed systems, developer tooling, and AI, building primitives that make Codex faster, safer, more reliable, and easier for the rest of the organization to build on. What You’ll Do Design and build execution environments for AI agents, including sandboxing, isolation, and reproducibility. Develop systems for agent orchestration across multi-step, tool-using workflows. Build infrastructure for running, testing, and debugging code generated by models. Create state and memory systems that allow agents to persist context across long-running tasks. Optimize tokens, latency, reliability, and cost across Codex’s production fleet. Support model rollouts, capacity planning, and the core tradeoffs between quality, speed, and economics to manage a fleet of frontier agents at scale. Build shared platform capabilities that unblock product teams, partner teams, and open source Codex. Yo

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 is seeking Software Engineers to join our Observability team within the Infrastructure organization. The Observability team owns the monitoring and telemetry infrastructure that every engineer and researcher at Anthropic depends on, from metrics and logging pipelines to distributed tracing, profiling, error analytics, alerting, and the dashboards and query interfaces that make it all actionable. By joining this team, you'll have a direct impact on the reliability and operational excellence of Anthropic's research and product systems. As Anthropic scales its infrastructure across large GPU, TPU, and Trainium clusters, the volume and complexity of operational data is growing by orders of magnitude, and an increasing share of the most challenging problems live below the application layer. We're building next-generation observability systems, including high-throughput telemetry pipelines, fleet-wide continuous profiling, eBPF-based tracing and network visibility, and agentic diagnostic tools, so engineers can detect, diagnose, and resolve issues in minutes rather than hours, even when the answer lies in the kernel, the network stack, or an accelerator rather than on a dashboard. Key responsibilities Design and build scalable telemetry ingest and storage pipelines for metrics, logs, traces, and error data across Anthropic's multi-cluster infrastructure Build observability solutions that give engineers deep, low-overhead visibility into system behavior across the fleet Own and evolve core observability platforms, leading migrations and architectural improvements that improve reliability, reduce cost, and scale with

The goal of a Staff Machine Learning Engineer at Scale is to lead the design and deployment of agentic AI systems that operate in real-world, mission-critical government environments. On the Public Sector team, you’ll work at the intersection of agentic ML, systems engineering, and applied research, building foundational infrastructure that enables AI systems to reason, plan, and act reliably at national scale. Our Public Sector ML Team partners directly with U.S. defense and intelligence agencies to deploy AI into classified and regulated environments. Through flagship programs like Donovan and Thunderforge , we are advancing the next generation of agentic AI for geospatial reasoning, planning, and decision support. Staff Machine Learning Engineers play a central role in setting technical direction, owning core architectures, and translating ambitious ideas into production systems trusted by government operators. You will: Lead the architecture and implementation of agentic AI systems, with a focus on long-horizon reasoning, orchestration, and system-level reliability. Build and scale agents that perform complex geospatial reasoning, including interpreting, generating, and reasoning over maps and spatial data. Design and improve retrieval systems across large collections of static and semi-structured documents, enabling agents to surface high-signal context efficiently. Fine-tune and evaluate embedding models to improve recall and precision for mission-critical datasets. Design memory systems that allow agents to persist state, operate over long contexts, and learn from prior interactions. Own and evolve shared agentic infrastructure and core libraries, enabling reuse across teams, products, and Public Sector contracts. Define evaluation strategies for agentic systems, including robustness testing, failure-mode analysis, and regression testing in production environments. Partner closely with engineering managers, product leaders, and researchers to scope high-impac

About the Team The Storage Infrastructure team builds and operates the storage foundation behind OpenAI’s most demanding workloads. We work directly with research to design storage systems for rapidly evolving experiments, while also powering production at scale. We own the platform end to end: backend systems, user-facing services and APIs, and the control planes that manage how data is placed, moved, and retained over time. Our stack spans cloud and in-house object stores across very different workload profiles, from GPU-attached systems to dedicated storage hardware. We also build the federation layer that unifies these backends behind a simple interface and routes each workload to the right storage solution. About the Role You will help build the storage platform that powers OpenAI’s research and production systems. This is a hands-on infrastructure role for engineers who want to work on deeply technical systems at scale and own them in production. You’ll work across object storage, cross-region data movement, lifecycle management, and the federation layer that provides a unified interface across multiple backends. Much of our stack runs on Kubernetes, and we primarily build services in Rust. In this role, you will: Build and operate storage services that underpin OpenAI’s research infrastructure Develop object storage systems across cloud and in-house environments Build systems for cross-region data movement, replication, and recovery Design lifecycle management capabilities that keep data durable, available, and cost-effective Evolve the federation layer that unifies multiple backend systems behind a simple interface Improve performance, reliability, and operational excellence across the platform Collaborate closely with researchers and infrastructure teams to support rapidly evolving workloads You might thrive in this role if you: Have experience building or operating distributed systems in production Have worked on storage infrastructure, object stores, dist

About the Team Data Platform at OpenAI owns the foundational data stack powering critical product, research, and analytics workflows. We operate some of the largest Spark compute fleets in production; design, and build data lakes and metadata systems on Iceberg and Delta with a vision toward exabyte-scale architecture; run high throughput streaming platforms on Kafka and Flink; provide orchestration with Airflow; and support ML feature engineering tooling such as Chronon. Our mission is to deliver reliable, secure, and efficient data access at scale and accelerate intelligent, AI assisted data workflows. Join us to build and operate these core platforms that underpin OpenAI products, research, and analytics. We’re not just scaling infrastructure – we’re redefining how people interact with data. Our vision includes intelligent interfaces and AI-assisted workflows that make working with data faster, more reliable, and more intuitive. About the Role This role focuses on building and operating data infrastructure that supports massive compute fleets and storage systems, designed for high performance and scalability. You’ll help design, build, and operate the next generation of data infrastructure at OpenAI. You will scale and harden big data compute and storage platforms, build and support high-throughput streaming systems, build and operate low latency data ingestions, enable secure and governed data access for ML and analytics, and design for reliability and performance at extreme scale. You will take full lifecycle ownership: architecture, implementation, production operations, and on-call participation. You’ve supported Spark, Kafka, Flink, Airflow, Trino, or Iceberg as platforms. You’re well-versed in infrastructure tooling like Terraform, experienced in debugging large-scale distributed systems, and excited about solving data infrastructure problems in the AI space. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per wee

At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most. As a Machine Learning Engineer on our team, you will be at the forefront of developing high-quality, production-ready models that play a central role in our decision-making processes. Your contributions will be instrumental in shaping our financial landscape. If you have a strong interest in machine learning and enjoy challenging work, Affirm is the place for you! What you'll do Use Affirm’s proprietary and other third party data to develop machine learning models that predict the likelihood of default and make an approval or decline decision to achieve business objectives Partner with platform and product engineering teams to build model training, decisioning, and monitoring systems Research ground breaking solutions and develop prototypes that drive the future of credit decisioning at Affirm Implement and scale data pipelines, new features, and algorithms that are essential to our production models Collaborate with the engineering, credit, and product teams to define requirements for new products What we look for 2+ years of experience as a machine learning engineer or PhD in a relevant field Proficiency in machine learning with experience in areas such as Generalized Linear Models, Gradient Boosting, Deep Learning, and Probabilistic Calibration. Domain knowledge in credit risk is a plus Strong engineering skills in Python and data manipulation skills like SQL Experience using large scale distributed systems like Spark or Ray Experience using open source projects and software such as scikit-learn, pandas, NumPy, XGBoost, Kubeflow Experience developing machine learning models at scale from inception to business impact Excellent written and oral communication skills and the capability to drive cross-functional requirements with product and engineering teams The ability to

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 As Anthropic's models grow more capable, so does the interest of people who want to attack or misuse them. The Detection and Response (D&R) team spots those threats and responds to incidents across every layer of Anthropic's technology stack. The team is early-stage, so you'll help build our detection and response capabilities from the ground up and work closely with our security and research teams. Key responsibilities Lead incident response across a range of domains, from external attacks to insider threats, spanning all layers of Anthropic's technology stack Build and deploy tooling, including tools that use large language models, to improve how we detect and respond to threats Create and tune detections, playbooks, and workflows to catch and respond to incidents quickly Review incident response metrics and procedures, and drive improvements Work across security and engineering teams Take part in an on-call rotation Minimum qualifications Experience handling security incidents and investigating anomalies as part of a team Working knowledge of EDR, SIEM, SOAR, or similar security tooling A solid understanding of cloud environments and operations Experience working with engineering teams in a SaaS environment Proficiency with a scripting language such as Python and query languages such as SQL Strong communication and collaboration skills Able to lead projects with little guidance Able to pick up new languages and technologies quickly Preferred qualifications 7+ years of software engineering experience, or 7+ years of detection engineering, incident response, or threat hunting experience Security operations or inves

At Curai, we believe that access to high-quality healthcare is a fundamental human right, not a privilege. Our mission is to radically transform healthcare delivery by harnessing the power of artificial intelligence and clinical expertise to make care more affordable, accessible, and effective for everyone. Patients interact with advanced AI systems at every step of their care and follow-up. Licensed physicians review each case with the patient for the clinical decision in our integrated virtual clinic. We ground our approach in rigorous research, continuous learning, and a deep commitment to clinical integrity. We focus on making a real impact: improving health outcomes, expanding access to care, and setting new standards for what trustworthy, patient-centered healthcare is. Read some of our latest publications. About the Senior Software Engineer, Agentic AI role We are hiring a Full-Stack Senior Software Engineer across a range of seniority levels to push the frontier of agentic / applied AI in healthcare. As a member of the technical staff on the artificial intelligence engineering team, you will design, build, and ship ML and LLM systems (agentic AI workflows) that directly shape how clinicians and patients interact with our platform. You will work end-to-end: framing the problem, exploring data, training and evaluating models, productionizing inference, and measuring real-world clinical and product impact. The bar is high and the surface area is large; we are looking for engineers who want significant ownership and are excited to operate where research meets production. What You'll Do Lead the technical execution of complex AI initiatives, owning the design and delivery of solutions within a product or technical domain while partnering with senior engineers on broader architectural direction. <span style="

Work Flexibility: Onsite What You Will Do: This role is on the Electrical Engineering team at Stryker Medical division's Acute Care business unit. We primarily work with patient handling and patient care equipment within the hospital space such as stretchers, hospital beds, and support surfaces. This role falls under the team addressing the high-acuity market, with a focus on products that often come into direct contact with patients. It includes gaining a deep understanding of customer needs through research and partnership with upstream marketing and through direct interactions with customers. Working with a diverse team, you will design, develop, modify, evaluate, and verify electrical components and sub-systems for medical devices. The role includes the full development process from research to product launch. Design and develop electrical circuits and subsystems for medical devices. Coordinate engineering changes and complete documentation in accordance with quality management system requirements. Review and modify C/C&#43;&#43; code to support product functionality and performance Apply electrical system and circuit test methods to evaluate product designs. Build and support prototypes and conduct bench testing activities. Collaborate with manufacturing, sourcing, and cross-functional teams to support ongoing production activities. Analyze and test designs for electromagnetic compatibility, reliability, safety, manufacturability, and testability. Support

About us Graphcore is one of the world's leading innovators in Artificial Intelligence compute. It is developing hardware, software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry. As part of the SoftBank Group, Graphcore is a member of an elite family of companies responsible for some of the world's most transformative technologies. Together, they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone. Graphcore's teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore brings together deep expertise to solve complex problems and deliver meaningful progress in AI compute. Job Summary Reporting to the Head of Data & Transformation, the Staff Data Engineer – Agentic Applications is a senior individual contributor responsible for designing, building and operating Python-based web applications, agentic workflows and supporting data services for Graphcore’s business teams. The role combines strong data engineering foundations with hands-on application development, using AI agents, trusted data and business system integrations to improve decision-making and automate operational workflows. Depending on the use case, you will either deliver applications end to end, using AI-assisted coding tools to build and maintain the front end, or own the backend and enable business colleagues to create and look after their own front ends. In both approaches, you will help establish clear ownership, secure interfaces and appropriate engineering controls so applications remain reliable, maintainable and supportable. Working closely with engineers, analysts and business stakeholders, you will take solutions from initial requir