As a Technical Program Manager for Infrastructure, you'll work across multiple infrastructure domains to coordinate complex programs that have broad organisational impact. You'll be solving novel scaling challenges at the frontier of what's possible, all while maintaining the security and reliability our mission demands.
\nDeveloper Productivity & Tooling
\nInfrastructure Reliability & Operations
\nCross-functional Coordination
\nYou'll be a good fit if you have 5+ years of technical program management experience, with a track record of successfully delivering complex infrastructure programs in ML/AI systems or large-scale distributed systems. You'll also need a deep technical understanding of infrastructure systems, strong stakeholder management skills, and the ability to navigate competing priorities-confirming data-driven technical decisions.
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\nIn this role, you will participate in the full software lifecycle , from design and implementation to deployment and long-term operation , and will collaborate with engineering teams to evolve release automation, improve tooling, and reduce manual steps in how we build and ship Temporal.
\nKey responsibilities include designing, building, and maintaining tools and systems that support release automation and deployment workflows, writing clean, reliable, and concurrent code that supports distributed systems, collaborating with cross-functional teams to understand and improve release quality and developer productivity, documenting technical designs, deployment practices, and operational procedures, and participating in small-team design reviews and contributing practical engineering solutions.
\nAs a Senior Software Engineer, you will have the opportunity to explore new ways to use Temporal to power the release and deployment lifecycle, deepen your understanding of Temporal's architecture and service interactions, and experiment with new automation patterns, testing strategies, and workflow designs that increase release confidence.
\nTo be successful in this role, you will need strong coding ability, especially in languages used at Temporal (e.g., Go, Java, or similar), a solid understanding of concurrency, distributed systems, and multi-threaded programming, experience contributing to backend systems, tooling, infrastructure, or developer workflows, a track record of solving moderately complex problems with reliable, maintainable solutions, and the ability to collaborate effectively in a remote, fast-paced environment.
\nAdditionally, you will have familiarity with release automation concepts, CI/CD pipelines, build tools, or deployment orchestration, experience with cloud environments (AWS, GCP) and container tooling, and exposure to distributed systems orchestration, observability tooling, or platform engineering.
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\nAnthropic's Accelerator Capacity Engineering (ACE) team manages one of the largest and fastest-growing accelerator fleets in the industry. As an engineer on ACE, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on.
\nWhat This Team Owns
\nThe team’s work spans three functional areas: data infrastructure, fleet observability, and compute efficiency. Depending on your background and interests, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap:
\nData Infrastructure
\nCollecting, normalizing, and serving the fleet-wide data that powers everything else. This means building pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, normalizing billing and usage data across cloud providers, and maintaining the BigQuery layer that the rest of the org queries against.
\nFleet Observability
\nMaking the state of the accelerator fleet legible and actionable in real time. This means building cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and driving systemic improvements to scheduling and fragmentation.
\nCompute Efficiency
\nMeasuring and improving how effectively every major workload uses the hardware it’s running on. This means instrumenting utilization metrics across training, inference, and eval systems, building benchmarking infrastructure, establishing per-config baselines, and collaborating directly with system-owning teams to close efficiency gaps.
\nWhat You’ll Do
\nYou May Be a Good Fit If You Have
\nStrong Candidates May Also Have
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\nAs a key member of our engineering team, you will own and operate Figma's core observability stack, including vendor platforms such as Datadog, ensuring high availability, strong data quality, and effective signal-to-noise across metrics, logs, and traces.
\nYou will define and drive the technical strategy for instrumentation standards, observability libraries, agents, and operators used to monitor internal and external facing services. You will also explore and implement innovative, AI-driven approaches to anomaly detection, root cause analysis, signal correlation, and operational automation.
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\nYou will partner with infrastructure, product engineering, finance, and security teams to improve visibility into system health and cost efficiency at scale.
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\nYou will coach and mentor engineers through career development, performance feedback, and technical leadership, fostering a culture of ownership, collaboration, and high-quality execution.
\nWe are looking for someone with 4+ years of experience leading infrastructure, observability, or platform engineering teams, with a track record of delivering highly reliable production systems.
\nYou should have deep hands-on experience with modern observability platforms (e.g., Datadog, OpenTelemetry) across metrics, logs, and distributed tracing.
\nYou should have a strong understanding of distributed systems, instrumentation best practices, SLO design, and incident response workflows.
\nExperience driving cost transparency and accountability initiatives, including cost attribution, budgeting, forecasting, and alerting in cloud environments is also required.
\nPreferred skills include experience designing or evolving company-wide observability standards, shared libraries, and agent/operator-based integrations, background in cost optimization for infrastructure or observability tooling, including vendor negotiations and usage modeling, and experience applying AI or machine learning techniques to anomaly detection, root cause analysis, or operational automation.
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The quality of Claude's next generation depends on the quality of the data we train it on , and the systems you build are what make that data possible. You'll own product surfaces end-to-end , from backend services and APIs to the web UIs that researchers, external vendors, and thousands of data labelers use every day.\\n\\nYou don't need a background in ML research. What matters is that you can take an ambiguous, high-stakes problem and ship a polished, reliable product against it, fast. This team moves very quickly. 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We've contributed to every Claude model, with significant impact on the autonomy and coding capabilities of our most advanced models.\\n\\nOur work spans teaching models to use computers effectively, advancing code generation through RL, pioneering fundamental RL research for large language models, and building the scalable training methodologies behind our frontier production models.\\n\\nThe RL org is organized around four goals: solving the science of long-horizon tasks and continual learning, scaling RL data and environments to be comprehensive and diverse, automating software engineering end-to-end, and training the frontier production model.\\n\\nOur engineering teams build the environments, evaluation systems, data pipelines, and tooling that make all of this possible , from realistic agentic training environments and scalable code data generation to human data collection platforms and production training operations.\\n\\n### Responsibilities\\n\\n Build and extend web platforms for RL environment creation, management, and quality review , including environment configuration, versioning, and validation workflows\\n Develop vendor-facing interfaces and tooling that let external partners create, submit, and iterate on training environments with minimal friction\\n Design and implement platforms for human data collection at scale, including labeling workflows, quality assurance systems, and feedback mechanisms that surface reward signal integrity issues early\\n Build evaluation dashboards and observability UIs that give researchers real-time insight into environment quality, training run health, and reward hacking\\n Create backend services and APIs that connect environment authoring tools, data collection systems, and RL training infrastructure\\n Build and expand scalable code data generation pipelines, producing diverse programming tasks with robust reward signals across languages and difficulty levels\\n Develop onboarding automation and documentation tooling so new vendors and internal users ramp up in hours, not weeks\\n Partner closely with RL researchers, data operations, and vendor management to translate ambiguous requirements into well-scoped, well-designed products\\n\\n### Requirements\\n\\n Strong software engineering fundamentals and real full-stack range , you're comfortable owning a surface from database schema to frontend\\n Proficient in Python and a modern web stack (React, TypeScript, or similar)\\n Track record of shipping systems that solved a hard problem, not just shipped on time , e.g. you built the thing that made your team 10x faster, or the internal tool nobody thought was possible\\n Operate with high agency: you identify what needs to be done and drive it forward without waiting for a ticket\\n Found yourself wondering "why isn't this moving faster?" in previous roles , and then have done something about it\\n Care about UX and can build interfaces that are intuitive for both technical researchers and non-technical labelers\\n Communicate clearly with researchers, operations teams, and engineers, and can turn vague asks into well-scoped work\\n Thrive in a fast-moving environment where priorities shift, Claude is your pair programmer, and the next problem is often one nobody has solved before\\n Care about Anthropic's mission to build safe, beneficial AI and want your work to contribute directly to it\\n\\n### Nice to Have\\n\\n Built data collection, labeling, or annotation platforms , ideally ones that had to scale across many vendors or many task types\\n Background building multi-tenant platforms with role-based access, audit trails, and vendor management workflows\\n Experience with cloud infrastructure (GCP or AWS), Docker, and CI/CD pipelines\\n Familiarity with LLM training, fine-tuning, or evaluation workflows\\n Experience with async Python (Trio, asyncio) or high-throughput API design\\n Background in dashboards, monitoring, or observability tooling\\n Experience working directly with external vendors or partners on technical integrations\\n A background that isn't a straight line , e.g. math or physics into SWE, competitive programming, research into engineering, or a side project that outgrew its scope\\n\\n### Representative Projects\\n\\n Building a unified platform for human data collection that integrates labeling workflows, vendor management, and QA for complex agentic tasks\\n Developing vendor onboarding automation that handles Docker registry access, API token management, and environment validation\\n Creating evaluation and observability dashboards that catch reward hacks, measure environment difficulty, and give real-time feedback during production training\\n Building environment quality review workflows that let researchers browse, grade, and provide feedback on training environments\\n Developing automated environment quality pipelines that validate correctness and difficulty calibration before environments hit production training\\n* Building internal tools for browsing and analyzing training run results, environment statistics, and data collection progress
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\nYou should have an aptitude for debugging and an appetite for real-time response, rapid resolution and root-causing complex issues on electromechanical systems. If you are passionate about ground-breaking technology, contributing to the national security mission, interacting alongside professionals that span a wide-range of disciplines, and providing best-in-class product operations oversight, Anduril is interested in speaking with you.
\nYour duties will include sustaining Anduril's Air Defence deployments by combining an understanding of our customers' missions with deep knowledge of our products and integrations. You will also triage, diagnose and conduct root cause analysis of product incidents; drive post-mortem actions including providing status visibility through resolution.
\nAdditionally, you will collect, organise and analyse system failure data to define trends, drive proactive sustainment processes and support resource allocation. You will consistently assess and ratchet up the quality of the fleet's observability and telemetry in partnership with product teams.
\nYou will communicate and coordinate technical and non-technical efforts across multiple business, engineering and sustainment functions, influencing decision making and driving action to maximise capability availability for end users.
\nYou will support Anduril's global customers through proactive communications and detail-oriented execution. You will perform maintenance on Anduril hardware through use of Linux command line function.
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\nYou'll build deployment pipelines and internal tooling that let IT Engineering ship like a product team. You'll define SLOs for corporate services, build the monitoring to know when we're missing them, and run on-call for the things you deploy. You'll partner with our network and AV engineers as their infrastructure counterpart , automating physical-world systems, building the telemetry that tells us an office is degraded before someone files a ticket. The scope is broad and the team is deliberately small, which means you'll need depth across cloud, CI, and observability, strong judgment about where to invest, and a bias toward infrastructure-as-code over heroic manual fixes.
\nResponsibilities ---------------
\nRequirements ------------
\nStrong candidates may also -------------------------------
\nTechnical Skills ----------------
\nExperience Level: senior Employment Type: full-time Workplace Type: remote Category: Engineering Industry: Technology Salary Range: $275,000-$325,000 USD Required Skills:
\nPreferred Skills:
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\nResponsibilities:
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\nLLM & AI Pipeline Engineering - Design, build, and maintain production-grade LLM integration pipelines , including retrieval-augmented generation (RAG), prompt engineering, output parsing, and chain orchestration.
\nDevelop and operate AI features within Jeeves's core financial products: spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation.
\nImplement structured output validation, fallback handling, and confidence scoring to ensure AI decisions meet reliability standards for financial use cases.
\nEvaluate and integrate AI frameworks and tools (LangChain, LlamaIndex, OpenAI API, Anthropic API, HuggingFace, vector databases) and advocate for the right tool for the job.
\nEstablish prompt versioning and evaluation practices to ensure AI outputs remain accurate and consistent as models and data evolve.
\nRetrieval & Vector Search - Design and maintain vector search pipelines using databases such as Pinecone, Weaviate, or pgvector to power semantic search and RAG-based features.
\nBuild document ingestion and chunking pipelines for Jeeves's financial data , processing invoices, receipts, policy documents, and transaction records.
\nOptimize retrieval quality through embedding model selection, chunk strategy, metadata filtering, and re-ranking techniques.
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\nBuild and maintain model serving endpoints with appropriate latency SLOs, input validation, and output monitoring.
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\nCollaboration & Growth - Partner with Product, Backend Engineering, and Data Science to define the AI roadmap and translate requirements into reliable systems.
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\nPay Transparency - North America
\nCOMPENSATION AND BENEFITS
\nThe ranges listed below are what EA in good faith expects to pay applicants for this role in these locations at the time of this posting. If you reside in a different location, a recruiter will advise on the applicable range and benefits. Pay offered will be determined based on a number of relevant business and candidate factors (e.g. education, qualifications, certifications, experience, skills, geographic location, or business needs).
\nPAY RANGES
\n• British Columbia (depending on location e.g. Vancouver vs. Victoria) $130,800 - $183,000 CAD
\nPay is just one part of the overall compensation at EA.
\nFor Canada, we offer a package of benefits including vacation (3 weeks per year to start), 10 days per year of sick time, paid top-up to EI/QPIP benefits up to 100% of base salary when you welcome a new child (12 weeks for maternity, and 4 weeks for parental/adoption leave), extended health/dental/vision coverage, life insurance, disability insurance, retirement plan to regular full-time employees. Certain roles may also be eligible for bonus and equity.
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\nNot all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application.
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\nLocation
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\nEmployment Type
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\nDepartment
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\nCompensation
\nThe base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.
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\nThis role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.
\nAbout the Team Full Stack engineers within the Fleet Scheduling team are dedicated to building intuitive and scalable interfaces that empower researchers to efficiently manage AI workloads across some of the largest supercomputers in the world. Our focus is on developing robust, high-performance systems that provide real-time insights, resource tracking, and seamless interaction with complex infrastructure. We aim to optimize resource allocation, minimize operational overhead, and create user-friendly tools that enhance researcher productivity and system transparency.
\nAbout the Role You will design, develop, and operate web-based systems that provide a powerful and intuitive interface to OpenAI’s supercomputing clusters. You will collaborate closely with researcher, product and infrastructure teams to deliver scalable solutions that enable seamless monitoring, job scheduling, and resource management. This is an opportunity to work at the cutting edge of AI infrastructure, designing tools that scale to exascale workloads while maintaining usability and performance.
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\nAbout OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
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