Systems Administrator in San Francisco

Who We Are HP IQ is HP’s new AI innovation lab. Combining startup agility with HP’s global scale, we’re building intelligent technologies that redefine how the world works, creates, and collaborates. We’re assembling a diverse, world-class team—engineers, designers, researchers, and product minds—focused on creating an intelligent ecosystem across HP’s portfolio. Together, we’re developing intuitive, adaptive solutions that spark creativity, boost productivity, and make collaboration seamless. We create breakthrough solutions that make complex tasks feel effortless, teamwork more natural, and ideas more impactful—always with a human-centric mindset. By embedding AI advancements into every HP product and service, we’re expanding what’s possible for individuals, organisations, and the future of work. Join us as we reinvent work, so people everywhere can do their best work. About The Role As a Software Engineering Intern on the Systems team at HP IQ, you’ll work on low-level software that sits close to the hardware and helps power intelligent experiences across our products. This role is ideal for students who enjoy understanding how complex systems work under the hood. You’ll have the opportunity to work across multiple layers of the software stack, investigate performance bottlenecks, optimize system behavior, and build software that interacts closely with hardware and system resources. We’re looking for engineers who are curious about more than whether something works — you want to understand how it works, why it performs the way it does, and how to make it better. What You Might Do Build and optimize low-level systems software using languages such as C and C++. Investigate performance bottlenecks and improve the speed, efficiency, and reliability of existing systems. Work on data processing and sensor pipelines that connect software with underlying hardware. Analyze and improve memory usage, resource management, and system performance. Work across multiple lay

Location: San Francisco, CA (Remote/Hybrid Available) What is Verse? The race to AI has become the race to power. Every breakthrough in artificial intelligence depends on one thing: access to electricity. But across the country, aging grid infrastructure and years-long interconnection queues are slowing the deployment of the data centers that will power the next generation of innovation. Solving this challenge isn't just about energy—it's about unlocking the future of AI. At Verse, we're building the energy intelligence platform for the AI economy. Our software helps the world's largest energy consumers achieve faster, cheaper, and cleaner power by combining real-time control of energy assets with complete visibility into their energy portfolio. Backed by Bessemer Venture Partners, GV, Coatue, and NVIDIA, and built by pioneers in grid-scale batteries, energy markets, and enterprise software, we're redefining how the world's most ambitious organizations access and manage energy. The Role As a Software Engineer focusing on Distributed Systems at Verse, you will work in collaboration with some of the brightest industry experts in the field building cloud-native applications that scale to trillions of data points collected from electricity markets globally. You will be a part of a dynamic, robust team primarily supporting the backend needs of our Aria software product spanning hundreds of data sources, sinks, services, and jobs. Your expertise will not only have a direct impact on product decisions, but you also be well-positioned to drive the development and trajectory of our entire platform and infrastructure and influence important architectural decisions that affect the whole organization. Key Responsibilities Foster a culture and mindset of well-designed systems, test-driven software, and transparent communication with a high caliber of mutual respect and consideration for stakeholders Read and write a lot of Go, Python, and Protobuf Build, test, debug, maint

About Scale Scale’s mission is to develop reliable AI systems for the world’s most important decisions. As the leading AI data foundry, we provide the high-quality data and full-stack technologies that power the world’s most advanced models — fueling breakthroughs in generative AI, defense, and autonomous vehicles. We partner with leading enterprises and governments to bring AI into production that performs when it matters most, combining rigorous evaluation with full-stack deployment so our customers can build AI they can trust. About the Team Applied Intelligence Systems (AIS) is part of the Scale Generative AI Platform (SGP), focused on pushing the frontier of what agentic applications can do across diverse enterprise and government use cases. We build the infrastructure and tooling that power agentic AI in production, paired with applied ML research, design, and evaluation to ensure these systems perform reliably at the scale our customers demand. AIS spans multiple workstreams — agent evaluation and oversight, orchestration and tool-use infrastructure, model and systems optimization, and applied research on new agent capabilities — and this role is not scoped to any single one of them. We’re growing fast, with increasing traction across both commercial and public sector customers, and we’re just getting started — this team will define what dependable, production-grade agentic AI looks like. About the Role As a Staff Machine Learning Research Engineer, you will operate across the full breadth of AIS’s technical needs — wherever the hardest ML problem in agentic AI happens to be that quarter. This could mean training and fine-tuning models, designing evaluation and observability systems, building improvement loops from production data, prototyping novel agent architectures, or designing internal systems and tooling that boost productivity across teams. You’re not tied to one team’s roadmap; you’re expected to move to where the technical leverage is highest, and t

Scale’s ML platform (RLXF) team builds our internal distributed framework for large language model training and inference. The platform has been powering MLEs, researchers, data scientists and operators for fast and automatic training and evaluation of LLM's, as well as evaluation of data quality. Scale is uniquely positioned at the heart of the field of AI as an indispensable provider of training and evaluation data and end-to-end solutions for the ML lifecycle. You will work closely across Scale’s ML teams and researchers to build the foundation platform that supports all our ML research and development. You will be building and optimizing the platform to enable our next generation of LLM training, inference and data curation. If you are excited about shaping the future AI via fundamental innovations, we would love to hear from you! You will: Build, profile and optimize our training and inference framework Collaborate with ML teams to accelerate their research and development and enable them to develop the next generation of models and data curation Research and integrate state-of-the-art technologies to optimize our ML system Ideally you’d have: Strong excitement about system optimization Experience with multi-node LLM training and inference Experience with developing large-scale distributed ML systems Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc. Strong written and verbal communication skills and the ability to operate in a cross functional team environment Nice to haves: Demonstrated expertise in post-training methods &/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the positi

Scale's LLM post-training platform team builds our internal distributed framework for large language model training. The platform powers MLEs, researchers, data scientists, and operators for fast and automatic training and evaluation of LLMs. It also serves as the underlying training framework for the data quality evaluation pipeline. Scale is uniquely positioned at the heart of the field of AI as an indispensable provider of training and evaluation data and end-to-end solutions for the ML lifecycle. You will work closely with Scale’s ML teams and researchers to build the foundation platform which supports all our ML research and development works. You will be building and optimizing the platform to enable our next generation LLM training, inference and data curation. If you are excited about shaping the future AI via fundamental innovations, we would love to hear from you! You will: Build, profile and optimize our training and inference framework. Collaborate with ML and research teams to accelerate their research and development, and enable them to develop the next generation of models and data curation. Research and integrate state-of-the-art technologies to optimize our ML system. Ideally you’d have: Passionate about system optimization Experience with multi-node LLM training and inference Experience with developing large-scale distributed ML systems Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc. Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc. Strong written and verbal communication skills to operate in a cross functional team environment. Nice to haves: Demonstrated expertise in post-training methods and/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc. Compensation packages at Scale for eligible roles include base salary, equity,

Scale GP is Scale's enterprise Generative AI platform—APIs and infrastructure for knowledge retrieval, inference, evaluation, and intelligent automation. We power mission-critical workflows for leading enterprises, helping teams turn complex data and models into reliable, production-ready AI systems. We're building a new AI Enablement team to create the next generation of agent-powered tools that ground AI in real operational workflows. Our goal: help internal teams demystify their own workflows, then deploy agentic systems that reason over data, take action, and deliver measurable outcomes. We don't build in a vacuum. You'll use our own platform to solve real business problems internally—then selectively commercialize that same stack for customers. What we run on is what we sell. This is a 0→1 team. We're looking for a sharp, product-minded engineer who thrives in ambiguity, moves fast, and loves building systems from scratch alongside customers and cross-functional partners. You'll work closely with product, forward-deployed engineers, data scientists, and applied AI teams to turn real-world problems into scalable production solutions. If you like shipping fast, owning outcomes, and working across the stack—from polished frontends to distributed backends to LLM integrations—this role is for you. What You’ll Do Own full-stack features and projects end-to-end — from design through production deployment — within a larger product area Sample surfaces - Accounting Agents, Finance Copilots, GTM Agents, Agentic Experimentation Platforms Develop reliable backend services in Typescript/Python, work with distributed systems, data pipelines, and AI/ML infrastructure Integrate LLMs, vector databases, and agentic frameworks to power intelligent workflows Ship quickly through tight experimentation loops while maintaining high quality and reliability Adapt across the stack and learn new tools as needed to solve real problems end-to-end Ideal Experience 3+ years of full-tim

Scale's mission is to develop reliable AI systems for the world's most important decisions. We provide the high-quality data that powers the world's AI models, and we help enterprises and governments build, deploy, and oversee AI applications that create real impact. As Communications Manager, Corporate and Product, you will help shape how Scale shows up when it comes to our work building enterprise AI, spanning company narrative, product launches, partner communications, media presence, and executive visibility. This role is centered on translating complex enterprise AI deployments and partnerships into clear, compelling narratives that resonate with business and vertical industry audiences. A core focus of the role is pitching and positioning Scale's enterprise wins, product launches, and customer partnerships, including how organizations across industries are using Scale's Generative AI Platform to build, deploy, and oversee AI applications that create real impact. You will also work closely with communications counterparts at partner companies to align messaging and coordinate joint announcements. You will help tell the story of how Scale's enterprise business powers mission-critical AI programs for the world's most consequential organizations, from Fortune 500 companies to leaders across every major industry. In this role, you will partner closely with enterprise product leaders and go-to-market teams, as well as teams across communications, social, and marketing. You will play a key role in strengthening Scale's reputation with enterprise customers, industry analysts, and the broader technology ecosystem. This position reports directly to the Head of Corporate & Product Communications. You Will Support corporate and product communications initiatives with a strong emphasis on Scale's enterprise business, including customer and partnership announcements, product launches, executive visibility, and industry positioning. Help translate co

Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with leading enterprises and government organizations to accelerate their AI initiatives through our data annotation platform, generative AI solutions, and enterprise AI capabilities. About the General Agents Team The General Agents team, part of Scale’s Enterprise organization, builds robust general agents for customer use cases and applications. The team sits at the intersection of frontier agent development and real-world deployment, translating state-of-the-art reasoning and agentic capabilities into reliable, production-grade systems that drive real economic value. Our agents are scalable systems built around recurring enterprise problem domains, with a strong emphasis on generalization, extensibility, and deployment across many customers. About the Role As a Senior/Staff Machine Learning Engineer (MLE) on the General Agents team, you’ll play a critical role in designing, building, and deploying production-ready AI agents that solve high-impact enterprise problems. You will work across the full agent lifecycle—from model and system design to evaluation, deployment, and iteration—bridging cutting-edge agentic techniques with the constraints and requirements of real customer environments. You will: Design and implement end-to-end agent systems that combine LLM reasoning, tool use, memory, and control logic to solve recurring enterprise use cases. Build scalable, reliable agent architectures that can be deployed across many customers with varying data, tools, and constraints. Develop evaluation frameworks, datasets, environments, and metrics to measure agent performance, reliability, and business impact in production settings. Collaborate closely with product managers, customers, data annotators, and other engineering teams to translate enterprise requirements into robust agent designs. Productionize frontier agent techniques (e.g.,

About the Team The Storage teams build and operate online stateful systems and abstractions that are reliable, efficient, secure and easy to use for DoorDash Engineering. The teams are responsible for understanding Product Engineering’s evolving needs and developing platform and infrastructure capabilities to serve them. The team currently supports CockroachDB, Cassandra, Kafka and Redis as well as data abstraction services to reduce the complexity of interacting with storage systems for Product Engineers. About the Role The Storage team is building and operating a high-performance, scalable, and reliable data abstraction layer that optimizes both efficiency and reliability. Our goal is to create a platform that manages itself and fades into the background—empowering engineers to focus on delivering product experiences our customers love. This role is available across two teams within Storage, each solving unique and high-impact challenges: One team is building the orchestration layer for DoorDash’s storage platform—unifying lifecycle management, operations, and self-serve APIs for databases and streaming systems, turning complex, stateful infrastructure into reliable, developer-friendly services used across the company. One team builds and operates the distributed data platform powering DoorDash's largest stateful workloads -- including Cassandra, which backs critical product surfaces across DoorDash, Wolt, and Roo. You'll design high-throughput data abstractions, smart clients, and platform services that make distributed data reliable and easy to work with at multi-petabyte, multi-million-QPS scale, with opportunities to go deep on distributed systems internals and contribute to the open-source Cassandra ecosystem. If you're passionate about distributed systems, developer experience, and building foundational infrastructure at scale, we'd love to hear from you. You must be located in San Francisco, Sunnyvale, Seattle, or the New York Metro Area for this hybrid pos

At Scale, we develop reliable AI systems for the world’s most important decisions. Scale’s product marketing team is responsible for developing and executing strategies that drive awareness and engagement for Scale’s offerings amongst our core audiences. We take a data-driven approach to understand our customers’ needs and challenges, ensuring that their voices are reflected in product development and messaging. We partner closely with product, engineering, research, sales, comms, and the broader marketing team to create a cohesive customer experience across all our channels. We aim to provide valuable insights and resources that help our customers execute on their AI transformation journey. This role will focus on developing and optimizing vertical-specific messaging and content for Scale’s applications business to ensure our messaging resonates with core buyers across verticals. The ideal candidate combines strategic thinking with hands-on execution. You will: Develop clear, compelling messaging for enterprise offerings tailored to key personas within target verticals Create marketing assets, including slide decks, videos, blog posts, and one-pagers, that effectively communicate our value propositions to vertical leaders and support our sales team’s pursuits Lead positioning and sales enablement efforts to drive awareness and engagement for Scale’s offerings. Drive cross-functional marketing programs around target verticals, in conjunction with product, engineering, sales, and growth marketing to create a cohesive customer experience and contribute to pipeline targets Collaborate with field marketing and events teams to develop vertical-specific event strategies, content, and experiences that drive engagement within customer and target accounts Drive customer marketing efforts, creating both strategy and tactics to maximize the value Scale and customers get from our shared success, including case studies, testimonials, visual assets and event particip

About the Team The Storage organization builds and operates the online stateful systems and abstractions that DoorDash Engineering depends on: reliable, efficient, secure, and easy to use. Within Storage, the Distributed Caching team owns every caching offering at DoorDash end to end, including ElastiCache (Redis/Valkey), Boulder (our KVRocks-based key-value store for high-QPS feature serving), Entity Cache (read Bill Shen’s engineering blog post, “ High-Performance Proxy Cache for DoorDash Services ”), and the Distributed Lock Service, plus the smart clients (asgard-redis, valkey-go) that sit in front of them. These systems back critical product surfaces across DoorDash, Wolt, and Deliveroo: the team runs roughly 400 ElastiCache clusters serving hundreds of millions of GET requests per second in aggregate, and Boulder, our offline-to-online feature store, serves billions of feature lookups per second at peak. About the Role The team owns provisioning of clusters and the smart clients that sit in front of them, baking in sensible defaults so that other engineering teams get a turnkey caching solution instead of having to run their own. You'll help drive Boulder's evolution to scale further, improve cost efficiency, enhance performance, and support real-time updates; re-platform the Distributed Lock Service onto a strongly consistent backend; and build the self-serve tooling and recommendation engine that let customers describe a workload (QPS, TTL, payload size, latency profile) and get the right backend without talking to a human. You'll go deep on cache invalidation, replication, sharding, compaction, and failover, while shipping the guardrails, automation, and observability that keep this scale operable by a small team. You must be located in San Francisco, Seattle, or the New York Metro Area for this hybrid position. You will report to the Engineering Manager on the Distributed Caching team within the Storage organization. You’re excited about this opportunity b

About Snorkel At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data. We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes since 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler! In September 2026 we raised a $350 million Series E at a $3.5 billion valuation , and we are scaling our engineering and research teams to meet demand. The role Frontier AI data is expensive to make and hard to measure. Every task we deliver is tested against the strongest models, often through many long-running agent rollouts. Your job is to make that process faster, cheaper, and more rigorous with ML and AI You will be one of the early members of ML & Research Engineering at Snorkel. You will study how frontier-grade data is generated and evaluated, form hypotheses, validate them against real production data, and ship the winners at scale. You will shape the discipline's direction, its standards, and the team that grows around it. What you'll work on Efficient agentic evals. Cut the cost of long-horizon agent evaluation with adaptive sampling, statistically grounded early stopping, model cascades, caching, and cheap-first gating. AI model routing. Route every eval and judge call to the cheapest model that clears the quality bar, with fallback, monitoring, and cost attribution. Fine-tuned small models. Fine-tune and serve open-weight models (LoRA and other

At Scale, our mission is to develop reliable AI systems for the world's most important decisions. For 10 years, Scale has provided the high-quality data and full-stack technologies that power the world's leading models, and has helped enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. Public Sector engineers build the core product including the systems required to ingest and process federal datasets that support real-time decision-making in contested environments. As a New Grad Software Engineer on this team, you will own meaningful, mission-facing work from day one: shipping features, sitting with the government stakeholders who use them, and iterating fast. Example Projects Build multi-layered guardrails that keep agents safe and predictable in high-stakes federal environments Optimize data retrieval for agents, including RAG pipelines over large, heterogeneous federal datasets Build orchestration for fleets of asynchronous agents running long-horizon tasks Develop systems that automatically alert users to deviations and anomalies in incoming data Create interfaces that illustrate how an agent reached a decision, so operators can audit and trust its output Develop data pipelines and ML infrastructure that make previously siloed government data sources accessible to agents Build evaluation infrastructure that measures model reliability against mission requirements Ship full-stack tooling that lets analysts query, visualize, and explore mission data Deploy and harden applications into secure, air-gapped, and cloud-native government environments Requirements A graduation date in Fall 2026 or Spring 2027 with a Bachelor's degree (or equivalent) in a relevant field (Computer Science, EECS, Computer Engineering, Statistics) Product engineering expe

About Scale AI At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. Scale Frontier Data is the organization behind the training and evaluation data that frontier labs depend on. We build the systems, tooling, and expert workflows that turn hard human expertise into signals that models can learn from, across reasoning, coding, agentic tool use, and domain expertise. Reinforcement learning environments are now the center of gravity for that work: the difference between a model that demos well and a model that reliably completes long-horizon work is almost always the quality of the environments and reward signals it was trained against. Responsibilities As a Staff Software Engineer, RL Environments, you'll own the technical foundation for how Scale builds, runs, verifies, and delivers RL environments at scale. An RL environment is a real piece of software: a containerized world with real dependencies, real state, real tools, and a grader that has to be correct even when the agent is creative about breaking it. Building one is a full-stack engineering problem. Building thousands of them reproducibly, cheaply, with trustworthy reward signals and throughput measured in millions of rollouts is a systems problem that very few people have solved. You'll work on both. You'll design the platform: sandboxed execution, environment packaging and versioning, rollout orchestration, trajectory capture, verifier frameworks, and the authoring surfaces that let engineers and domain experts produce environments without reinventing infrastructure each time. And you'll go deep on the environments themselves by instrumenting real applications, designing task suites that expose specific capability gaps, and building graders that

At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. Scale Frontier Data is the organization behind the training and evaluation data that frontier labs depend on. We build the systems, tooling, and expert workflows that turn hard human expertise into signals that models can learn from, across reasoning, coding, agentic tool use, and domain expertise. About our Customer Platform team: Our Customer Platform Team plays a pivotal role in integrating our platform with external systems and ensuring seamless, reliable connectivity for both internal users and customers. As the leader of this team, you’ll drive the strategy, architecture, and development of our connectivity solutions, focusing on API integration, distributed systems, and a robust data platform. Your role will be crucial in maintaining and enhancing our platform’s ability to meet the needs of both our internal and external stakeholders. Responsibilities: Own large areas within our product Comfortable working cross functionally, whether that be internal or external customers Build features end-to-end: front-end, back-end, system design, debugging and testing Deliver experiments at a high velocity and level of quality to engage our customers Work across the entire product lifecycle from conceptualization through production Influence the culture, values, and processes of a growing engineering team Inspire and mentor less experienced engineers Collaborating with cross-functional teams to define, design, and ship new product features and experiences. Requirements: At least 7-10 years of relevant experience is preferred Track record of shipping high-quality products and features at scale Desire to work in a very fast-paced environment Abil