Systems Research Engineer Jobs

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 the Team Our Robotics team is focused on unlocking general-purpose robotics and pushing towards AGI-level intelligence in dynamic, real-world settings. Working across the entire model stack, we integrate cutting-edge hardware and software to explore a broad range of robotic form factors. We strive to seamlessly blend high-level AI capabilities with the constraints of physical systems to improve peoples’ lives. About the Role We're seeking talented Robotics Software Engineers to expand our robotics data collection and evaluation program. This highly technical role involves designing, implementing, and optimizing software solutions across diverse robotics hardware. You'll work closely and collaboratively with multidisciplinary teams; including software, hardware, research, and operations - to drive advancements in our robotic systems. This role is based in San Francisco, CA, and requires in-person 4 days a week. In this role, you will: Help develop and grow our data collection labs, owning the entire integration lifecycle, from identifying and sourcing new hardware to collaborating with mechanical and electrical engineers on setup, software integration, and operational deployment. Develop innovative robot control interfaces suited to a variety of morphologies, environments, and tasks. Collaborate closely with research and engineering teams to develop automation tools and machinery that facilitate the evaluation of advanced robotic policies. Lead the design and implementation of data collection, visualization, and quality control processes. You might thrive in this role if you: Have 5+ years of professional software engineering experience developing and shipping production-quality systems in robotics or hardware-integrated environments. Have extensive experience integrating and deploying industrial automation systems, off-the-shelf robotics platforms, or custom hardware into production environments. Bring hands-on experience delivering production-quality software

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. Our team includes creators of popular open-source projects (e.g., Seaborn , Luigi ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We are looking for PhD research interns with strong research experience in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our research team. This internship is well suited to candidates interested in improving existing methods and developing new techniques for large-scale model training, optimization, and inference, extending models to long-context and long-horizon tasks, and improving inference-time efficiency, reliability, and robustness in high-stakes real-world deployments. Preferred Qualifications: Currently pursuing a PhD in computer science, machine learning, or a related field. A demonstrated record of research in reinforcement learning, machine learning, foundation models, or related areas. Experience developing and evaluating large-scale models or machine learning systems. Familiarity with distributed training, lar

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. Our team includes creators of popular open-source projects (e.g., Seaborn , Luigi ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We're building a platform that covers the whole life of an LLM: training it, deploying it, and observing it in production. We already run multi-node training, elastic inference, sandboxes, and distributed volumes, and we control the infrastructure underneath. We’re looking for research depth in post-training to sit alongside our systems and product work. What You'll Do: We are looking for research scientists with a strong track record in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our research team. This role is well suited to candidates interested in improving existing methods and developing new techniques for large-scale model training, optimization, and inference, extending models to long-context and long-horizon tasks, and improving inference-time efficiency, reliability, and robustness in high-stakes real-world deplo

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 is an analysis and research role inside the core product organization. It works closely with the Lead Analyst and Senior Product Marketer to bring credible market, customer, competitor, and product evidence into the decisions Sapience AI makes. The Junior Data Analyst is the evidence and research engine behind the team’s work. You will gather information, evaluate sources, structure data, maintain research systems, identify patterns, and turn complex material into clear first-draft findings. Your work will support market and category strategy, product decisions, positioning, competitive intelligence, pricing and packaging research, launches, and adoption analysis. This is a junior-level professional role with meaningful responsibility. It is intended for someone with at least three years of relevant experience; it is not an internship or a first professional role. You will be expected to independently own well-scoped assignments, ask thoughtful questions, communicate clearly, work with care, and apply sound judgment when distinguishing a useful signal from incomplete or misleading information. Final strategic accountability remains with the Lead

Senior Python / Agentic AI Engineer Capco: Where Innovation Meets Opportunity Capco is a global leader in consulting, uniquely positioned to drive innovation and transformation in the financial services industry. More than consultants, we are strategic partners and problem solvers for some of the largest organizations in Banking, Insurance, Investment Management, Data/Digital Transformation, Regulatory Compliance, and ICT Risk. Our business and technology professionals thrive in a dynamic environment where creativity, collaboration, and cutting-edge solutions are at the heart of everything we do. Experience the Capco difference and explore how you can shape your future with us at capco.com . Capco’s AI Lab The AI Lab is a cross-disciplinary research and engineering centre that translates advanced AI research into robust, deployable solutions for clients and internal products. It runs applied research (ML and agentic/LLM systems, planning, simulation and evaluation), rapid prototyping and production engineering (APIs, CI/CD, observability, data platforms and cost/latency optimisation), and rigorous evaluation and safety work (benchmarks, red-teaming, monitoring, governance and compliance). The lab also supports client engagements and internal innovation by building demonstrators and reference architectures, transferring technology to delivery teams, publishing technical learnings, and maintaining the shared infrastructure, tooling and best practices that enable reproducible, secure and scalable AI deployment. Responsibilities Design and implement scalable, production-grade Python services that leverage LLMs and Agentic architectures. Design and implement robust agent workflows and orchestration patterns to automate complex business processes in regulated enterprise environments. Build and maintain reliable AI-enabled backend systems , including orchestration layers, tool integrations, knowledge a

About the Team OpenAI's research training infrastructure powers how our frontier models are trained and evaluated. The Simulation team sits at the intersection between the agentic harness that powers OpenAI's products and the research infrastructure where GPT-next is trained, ensuring that our model's training environment is as realistic as possible. This team owns the integration layer that connects our production harness capabilities into the training stack. The work is highly cross-functional and high leverage: researchers depend on it to run experiments and evaluations reliably as well as to develop the next generation of harness capabilities. Failures in this surface can materially affect training velocity and correctness. About the Role We're looking for a Principal Software Engineer to lead the architecture and evolution of the Simulation Platform. You'll own a critical interface between research and engineering, building the systems, APIs, and operational patterns that let researchers use agentic coding infrastructure safely and effectively in training environments. This role is ideal for a senior backend or infrastructure engineer with strong technical judgment, product sense for highly technical users, and the ability to drive execution across multiple teams. The highest-leverage work is building robust infrastructure that supports and accelerates research without compromising engineering quality. In this role, you will Design, build, and evolve the integration between the Codex harness that powers OpenAI's products and research training infrastructure used for training GPT-next Build a platform for our LLMs to train and be evaluated in simulated environments that mimic their deployment setting as closely as possible, on every axis: agentic harness, compute substrate, timing, tools, data sources, humans in the loop, and more Own major integration surfaces end-to-end, from architecture and API design through rollout, operations, and long-term maintenance Bu

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 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. Staff+ Software Engineer, Developer Acceleration About the Role Anthropic is seeking talented and experienced Staff+ Software Engineers to join Developer Acceleration within the Developer Productivity organization. The Developer Acceleration team is responsible for the infrastructure that enables thousands of employees to be productive via agents. By joining this team, you'll have a direct impact on the velocity and quality of Anthropic's research and engineering development. You will be defining how AI transforms our work and building the future of agentic productivity. Some things that the team owns: Agent Runtime Platform for provisioning and running agents Knowledge for providing relevant internal context to agents and humans Some of our work is mentioned in When AI Builds itself and Building effective human-agent teams . Responsibilities Define the future of agentic productivity at scale and at the frontier Own the technical strategy and roadmap for your area, translating goals into concrete execution Define what quality means for agent-driven engineering work, and hold that line across the company Stay hands-on: build and ship the runtime platform and tooling Build agent harnesses and experiment with various context management strategies to improve agent performance and correctness Write evals to benchmark agent behaviors Deliver impact by collaborating cross-team Own infrastructure scalability and reliability, and establish operational excellence practices You May Be a Good Fit If You: Have 10+ years building and operating large-scale distributed systems Have 3+ years of experience leading large scale, complex projects or

About the Team The Human Data team at OpenAI is responsible for identifying and mitigating risks in advanced AI systems by designing evaluations, surfacing vulnerabilities, and collaborating closely with researchers to strengthen model reliability and public trust. About the Role As a Research Program Manager, you will lead initiatives that test the safety and robustness of OpenAI’s models through creative experimentation and structured evaluation. You’ll coordinate efforts across research and engineering teams to transform ambiguous risks into concrete research programs and influence future model development and deployment. We’re looking for people who are technically savvy, comfortable with ambiguity, and excited about shaping the future of safe AI. 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 programs that explore unexpected model behaviors and identify failure modes. Translate vague or emergent risk signals into clear priorities and actionable research plans. Design and run creative evaluations, experiments, and red-teaming campaigns. Collaborate with research, product, and deployment teams to integrate findings into model training and deployment cycles. Develop repeatable systems for tracking model performance and understanding emerging behavior patterns. You might thrive in this role if you: Have strong experience in technical program management, with excellent organizational and communication skills. Are familiar with large language models, prompt engineering, or model evaluation techniques. Are comfortable managing fast-paced, high-uncertainty projects and shaping them from the ground up. Are creative and resourceful in devising new methods for testing model behavior and performance. Can effectively coordinate across technical and non-technical stakeholders to drive alignment and execution. About OpenAI OpenAI is an AI resear

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 At Anthropic, we're building AI systems that are safe, beneficial, and transformative. Our mission is to develop AI that benefits humanity, and we believe the most powerful capabilities emerge when we thoughtfully bridge the gap between research breakthroughs and real-world applications. Claude is getting good at research. Given a well-posed problem, it can read the literature, write and run simulations, analyze results, and iterate on a design across physics, materials, chemistry, and engineering. But most of the hardest problems in science and engineering aren't well-posed. They live inside companies and labs with their own data, tools, constraints, and experts, and progress depends on turning all of that into work a model can actually do and check. Our Applied AI, Science & Engineering team takes Claude to the scientists and engineers working on hard problems in energy, materials, hardware design, and other physical-world fields. We build the tools Claude needs to do real research and engineering work. Mostly that means software: the agent harnesses, integrations, evaluations, and working processes that turn a partner's problem into something Claude can make progress on and validate. We find real-world problems and test what we build through partner engagements, then feed our learnings back to our research and product teams. It's early, and the engineers who join now will shape how Claude gets applied to science and engineering. This is an engineering role first. You'll spend real time with domain experts, become the person who knows how to get Claude working in their field, and prototype quickly alongside

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

Ignite your curiosity. Solve the unsolvable. At Leidos, we do more than write code—we decode the unknown. Our San Diego-based research and engineering team takes on some of the nation’s toughest defense challenges using advanced signal processing, ocean remote sensing, and high-performance computing. We’re seeking a Software Engineer / Computer Scientist who enjoys solving complex problems and pushing the limits of performance. In this role, you’ll work alongside a multidisciplinary team of scientists and engineers with expertise in hydrodynamics, physics, acoustics, and signal processing to build impactful software that turns massive, complex data sets into meaningful insight. If you are motivated by innovation, energized by collaboration, and excited to see your work support real-world missions, this could be the right opportunity for you. What You’ll Do Collaborate with scientists and engineers to design, develop, and optimize advanced algorithms for next-generation radar, optical, and infrared sensor systems. Build scalable, high-performance backend systems for scientific computing in distributed environments. Integrate, refactor, and improve scientific codebases to increase efficiency and scalability. Translate and optimize existing code for GPU/CUDA acceleration and parallel or distributed execution. Test, document, maintain, and enhance complex software in Linux/Unix environments. Contribute in a collaborative environment that values technical excellence, creativity, and continuous growth. Required Qualifications Bachelor’s degree in Computer Science, Applied Mathematics, Physics, or a related field with 4+ years of backend software development experience, or a Master’s degree with 2+ years of experience. Equivalent experience may be considered in place of a degree. U.S. citizenship and the ability to obtain a Top Secret clearance; active Top Secret clea

About the Team OpenAI's Human Data Team creates custom data solutions driving groundbreaking research. Our work enhances and evaluates our flagship models and products like ChatGPT, GPT-5, and Sora, and contributes to safety initiatives through collaboration with our Preparedness and Safety Systems teams. About the Role As a Research Program Manager (RPM) in the Human Data team you will partner with research and engineering to design and implement pragmatic solutions for collecting high-quality data. You will be a key interface between our research roadmap, external vendors, AI trainers, and the Human Data engineering team. This role is based in our San Francisco HQ. In this role, you will: Collaborate with Research: Partner with researchers to scope data collection needs, define success metrics, and establish quality measurement frameworks. Design & Execute Data Collection Campaigns: Translate research needs into actionable plans and accelerate execution by leveraging existing tooling and iterating to reach the desired outcome. In many cases, you will need to implement scrappy new solutions while partnering with engineering to design robust/scalable solutions. Unblock Yourself: You must be deeply uncomfortable with the idea of sitting around waiting for external dependencies, and have the technical acumen and drive to figure out how to achieve at least partial success in the interim. Optimize Systems & Processes: Build and optimize dashboards to track campaign performance, leveraging SQL and Python for data analysis and actionable insights. Drive Technical Roadmaps: Collaborate with engineers to enhance data platforms, resolve blockers, and ensure security best practices such as access management. Scale Your Impact : Advise and empower program managers and vendors to drive day-to-day execution so that you can focus on addressing high priority opportunities. You might thrive in this role if you: Are proficient in SQL and Python for data analysis, including q