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Ornn is looking for a Fleet Operations Engineer to build, operate, and secure GPU production infrastructure.

Ornn is looking for a Software Engineer to build reliable full-stack compute trading platform features and infrastructure.

You will own the reliability, performance, and cost efficiency of the PostgreSQL data platform behind our flagship CMS — primarily Amazon Aurora PostgreSQL — along with the infrastructure and automation around it. This is a reliability engineering role,.

Location: Москва GigaCode -  AI-продукт для разработчиков. Мы создаём AI-агентов, которые помогают писать, анализировать, тестировать и сопровождать код. Ищем технически сильного Backend Team Lead (Java), который возглавит команду backend-разработки GigaCode. Обязанности ### Задачи: * управлять командой backend-разработчиков: планирование, развитие, найм. * проектировать архитектуру и развивать backend GigaCode * разрабатывать новые продуктовые возможности и API для CLI, Desktop и IDE-плагинов * развивать интеграции с LLM, агентные сценарии и взаимодействие клиентских приложений сbackend * обеспечивать производительность, отказоустойчивость и масштабируемость продукта * принимать технические решения, участвовать в разработке и code review. Требования ### Мы ожидаем: * опыт backend-разработки от 5 лет, управление командой — от 2 лет * глубокая экспертиза в Java, Spring Boot * опыт проектирования распределённых и высоконагруженных систем * знание PostgreSQL, Redis, Kafka, Docker, Kubernetes * понимание микросервисной архитектуры, observability и CI/CD * умение самостоятельно принимать технические решения и доводить их до production. **Будет плюсом:** * опыт разработки на Rust и JavaScript / TypeScrip * опыт разработки AI-продуктов, LLM-интеграций и агентных систем * Знакомство с MCP, OpenTelemetry и архитектурой AI coding assistants. Условия ### Мы предлагаем: * годовой бонус и ежегодный пересмотр зарплаты * расширенный ДМС с первого дня, страхование жизни, корпоративная пенсионная программа * льготная ипотека для сотрудников, подписка Прайм с возможностью шеринга (совместного использования) на трёх близких, скидки от партнеров и сервисов группы компаний * корпоративный университет Сбера, внутренняя образовательная платформа, участие в IT-конференциях * офис с видом на набережную, зонами отдыха и спортзалом * 90 дней удаленной работы из любого региона РФ * реферальная программа (бонус до 100 000 рублей).

Location: San Francisco; Remote (United States); Seattle; New York City Perplexity serves hundreds of millions of queries a month. Every answer, every Perplexity Computer task, and every enterprise deployment runs on production infrastructure owned by the Cloud Infrastructure team: a fleet of Kubernetes clusters across a multi-account AWS estate, private networks that reach hyperscalers and the neoclouds serving our models, and the storage underneath our user data and agentic workloads. We're looking for SREs who treat reliability as a software problem. You'll own compute, networking, and storage at scale and keep production stable while everything built on top of it is changing fast. RESPONSIBILITIES - Own production at scale. Run the compute, networking, and storage foundation behind Perplexity's consumer, Computer, API, and enterprise products, and own its availability, latency, and cost. - Run our Kubernetes fleet. Operate and upgrade the EKS clusters that host web services, search, datastores, and secure sandboxes. Tune scheduling, autoscaling, node lifecycle, DNS, and service networking so clusters stay healthy under bursty, fast-growing load. - Engineer the network. Design and operate VPC architectures, Transit Gateway and PrivateLink connectivity, load balancing, DNS, and egress across accounts, regions, and cloud providers. - Keep data durable and fast. Own the storage layer (EBS, S3, DynamoDB, and the managed databases and caches our services depend on), including performance, durability, backups, and restores that have actually been tested. - Manage everything as code. Run infrastructure through Terraform and CI/CD. Build reusable modules and guardrails, and make every change reviewable, repeatable, and safe to roll back. - Prepare for failures. Define SLOs, build alerts that someone can act on, design for zone and regional failure, and run game days and disaster-recovery exercises that prove recovery works. - Lead incident response. Be on the critical path when things break. Drive incidents to resolution, write blameless postmortems, and land the fixes so the same failure never pages anyone twice. - Scale ahead of demand. Own capacity planning, quotas, reservations, and cost efficiency so product launches and traffic spikes never wait on infrastructure. - Automate away the toil. Build tooling, increasingly agent-driven, that turns provisioning, upgrades, and remediation into self-serve, hands-off workflows for every engineering team. QUALIFICATIONS We expect you to have real depth in most of these: - Deep AWS expertise. You know VPC, IAM, EC2, EKS, ELB, Route 53, S3, EBS, etc. well past the console, including their limits, failure modes, and the bill that comes with them. - Production Kubernetes at scale: cluster upgrades, CNI, CoreDNS, autoscalers like Karpenter, and debugging a misbehaving node at 3 a.m. Writing Helm charts alone doesn't count. - Terraform mastery. You've designed module structures, managed state across many accounts, and run large refactors and imports without taking production down. - Strong networking fundamentals (TCP/IP, DNS, TLS, load balancing, routing). - You write real code in Python, Go, or Rust for automation, tooling, and controllers, and you'd rather automate a runbook than follow it. - Fluency with observability tools like Datadog, Prometheus, or Grafana, and the judgment to turn metrics, logs, and traces into SLOs and alerts that matter. - A track record of leading production incidents and turning root-cause analysis into durable fixes. - You own problems end-to-end and do well when the path forward isn't laid out for you. ADDITIONAL EXPERIENCE WE VALUE - Multi-region active-active or failover architectures, and disaster-recovery programs with real recovery targets. - Proxies, service meshes, and kernel-level networking: Envoy, Istio, Cilium, or eBPF. - GitOps and progressive delivery: Argo CD, Flux, or similar. - Enterprise and compliance environments: single-tenant or BYOC deployments, KMS/BYOK, SOC 2 or similar controls. - Running stateful systems in production: Postgres, Redis, or search engines like Vespa or Elasticsearch. - Multi-cloud or neocloud operations (GCP, CoreWeave, or similar). - FinOps: cost attribution, Savings Plans and reservations, and making infrastructure cheaper without making it fragile.

Location: San Francisco; Remote (United States); Seattle; New York City Perplexity serves hundreds of millions of queries a month. Every answer, every Perplexity Computer task, and every enterprise deployment runs on production infrastructure owned by the Cloud Infrastructure team: a fleet of Kubernetes clusters across a multi-account AWS estate, private networks that reach hyperscalers and the neoclouds serving our models, and the storage underneath our user data and agentic workloads. We're looking for SREs who treat reliability as a software problem. You'll own compute, networking, and storage at scale and keep production stable while everything built on top of it is changing fast. RESPONSIBILITIES - Own production at scale. Run the compute, networking, and storage foundation behind Perplexity's consumer, Computer, API, and enterprise products, and own its availability, latency, and cost. - Run our Kubernetes fleet. Operate and upgrade the EKS clusters that host web services, search, datastores, and secure sandboxes. Tune scheduling, autoscaling, node lifecycle, DNS, and service networking so clusters stay healthy under bursty, fast-growing load. - Engineer the network. Design and operate VPC architectures, Transit Gateway and PrivateLink connectivity, load balancing, DNS, and egress across accounts, regions, and cloud providers. - Keep data durable and fast. Own the storage layer (EBS, S3, DynamoDB, and the managed databases and caches our services depend on), including performance, durability, backups, and restores that have actually been tested. - Manage everything as code. Run infrastructure through Terraform and CI/CD. Build reusable modules and guardrails, and make every change reviewable, repeatable, and safe to roll back. - Prepare for failures. Define SLOs, build alerts that someone can act on, design for zone and regional failure, and run game days and disaster-recovery exercises that prove recovery works. - Lead incident response. Be on the critical path when things break. Drive incidents to resolution, write blameless postmortems, and land the fixes so the same failure never pages anyone twice. - Scale ahead of demand. Own capacity planning, quotas, reservations, and cost efficiency so product launches and traffic spikes never wait on infrastructure. - Automate away the toil. Build tooling, increasingly agent-driven, that turns provisioning, upgrades, and remediation into self-serve, hands-off workflows for every engineering team. QUALIFICATIONS We expect you to have real depth in most of these: - Deep AWS expertise. You know VPC, IAM, EC2, EKS, ELB, Route 53, S3, EBS, etc. well past the console, including their limits, failure modes, and the bill that comes with them. - Production Kubernetes at scale: cluster upgrades, CNI, CoreDNS, autoscalers like Karpenter, and debugging a misbehaving node at 3 a.m. Writing Helm charts alone doesn't count. - Terraform mastery. You've designed module structures, managed state across many accounts, and run large refactors and imports without taking production down. - Strong networking fundamentals (TCP/IP, DNS, TLS, load balancing, routing). - You write real code in Python, Go, or Rust for automation, tooling, and controllers, and you'd rather automate a runbook than follow it. - Fluency with observability tools like Datadog, Prometheus, or Grafana, and the judgment to turn metrics, logs, and traces into SLOs and alerts that matter. - A track record of leading production incidents and turning root-cause analysis into durable fixes. - You own problems end-to-end and do well when the path forward isn't laid out for you. ADDITIONAL EXPERIENCE WE VALUE - Multi-region active-active or failover architectures, and disaster-recovery programs with real recovery targets. - Proxies, service meshes, and kernel-level networking: Envoy, Istio, Cilium, or eBPF. - GitOps and progressive delivery: Argo CD, Flux, or similar. - Enterprise and compliance environments: single-tenant or BYOC deployments, KMS/BYOK, SOC 2 or similar controls. - Running stateful systems in production: Postgres, Redis, or search engines like Vespa or Elasticsearch. - Multi-cloud or neocloud operations (GCP, CoreWeave, or similar). - FinOps: cost attribution, Savings Plans and reservations, and making infrastructure cheaper without making it fragile.

Location: San Francisco; Remote (United States); Toronto; Seattle; New York City Perplexity serves hundreds of millions of queries a month, and every one of them fans out into multiple AI inference requests running in real time. Behind that sits a large GPU fleet spread across several cloud providers. Today, our inference engineers and researchers build models while also managing networking, securing capacity, and operating the underlying GPU clusters, responsibilities we want a dedicated platform team to own. Your job is to take ownership of that infrastructure and hide its complexity behind a unified, self-serve platform for running training and inference workloads. RESPONSIBILITIES - Build a self-serve compute platform. Design and own the systems that let inference engineers and researchers launch training jobs and operate inference services without managing GPU provisioning, cluster configuration, or provider-specific infrastructure. - Operate the GPU fleet. Own provisioning, lifecycle management, reliability, and capacity integration across providers, giving teams a consistent way to use compute regardless of where it runs. - Solve for GPU scarcity. Build the scheduling and placement logic that finds available capacity across providers, packs it efficiently, and gets the right workload onto the right hardware under real constraints. - Support two very different workloads. Keep long-running distributed training jobs healthy while simultaneously guaranteeing the availability and latency of production inference services on the same fleet. - Own the Kubernetes for GPU orchestration. Write the operators and CRDs, and manage many clusters across providers so the platform behaves the same everywhere we run. - Make failure boring. Build the fault tolerance, autoscaling, and observability that keep the fleet utilized and let workloads survive node loss, provider hiccups, and capacity shifts without human intervention. - Set technical direction across teams. Partner with inference and cloud infrastructure engineers to turn operational constraints into a coherent platform architecture and roadmap. QUALIFICATIONS We expect you to have real depth in most of these: - Deep Kubernetes experience — custom operators, CRDs, and multi-cluster federation, not just running kubectl apply. - You've managed GPU clusters at scale: NVIDIA hardware, CUDA, and the networking that makes them fast (InfiniBand or RoCE). - You've orchestrated compute across multiple clouds (CoreWeave, AWS, GCP, or similar) and understand how different each one really is. - Strong distributed systems fundamentals: scheduling, resource allocation, and fault tolerance under load. - You write infrastructure and systems-level code in Go, Rust or C++. - You've supported both long-running training jobs and high-availability inference services, and you know why they pull infrastructure in opposite directions. - You own problems end-to-end and do well when the path forward isn't laid out for you. ADDITIONAL EXPERIENCE WE VALUE - Inference serving stacks: vLLM, SGLang, or TensorRT-LLM. - Slurm or other HPC schedulers. - GPU kernel work in CUDA or Triton — not required, but notable. - High-speed interconnects: InfiniBand, RoCE, or RDMA in production. - Observability for ML workloads: Prometheus, Grafana, or Weights & Biases.

Location: San Francisco; Remote (United States); Toronto; Seattle; New York City Perplexity serves hundreds of millions of queries a month, and every one of them fans out into multiple AI inference requests running in real time. Behind that sits a large GPU fleet spread across several cloud providers. Today, our inference engineers and researchers build models while also managing networking, securing capacity, and operating the underlying GPU clusters, responsibilities we want a dedicated platform team to own. Your job is to take ownership of that infrastructure and hide its complexity behind a unified, self-serve platform for running training and inference workloads. RESPONSIBILITIES - Build a self-serve compute platform. Design and own the systems that let inference engineers and researchers launch training jobs and operate inference services without managing GPU provisioning, cluster configuration, or provider-specific infrastructure. - Operate the GPU fleet. Own provisioning, lifecycle management, reliability, and capacity integration across providers, giving teams a consistent way to use compute regardless of where it runs. - Solve for GPU scarcity. Build the scheduling and placement logic that finds available capacity across providers, packs it efficiently, and gets the right workload onto the right hardware under real constraints. - Support two very different workloads. Keep long-running distributed training jobs healthy while simultaneously guaranteeing the availability and latency of production inference services on the same fleet. - Own the Kubernetes for GPU orchestration. Write the operators and CRDs, and manage many clusters across providers so the platform behaves the same everywhere we run. - Make failure boring. Build the fault tolerance, autoscaling, and observability that keep the fleet utilized and let workloads survive node loss, provider hiccups, and capacity shifts without human intervention. - Set technical direction across teams. Partner with inference and cloud infrastructure engineers to turn operational constraints into a coherent platform architecture and roadmap. QUALIFICATIONS We expect you to have real depth in most of these: - Deep Kubernetes experience — custom operators, CRDs, and multi-cluster federation, not just running kubectl apply. - You've managed GPU clusters at scale: NVIDIA hardware, CUDA, and the networking that makes them fast (InfiniBand or RoCE). - You've orchestrated compute across multiple clouds (CoreWeave, AWS, GCP, or similar) and understand how different each one really is. - Strong distributed systems fundamentals: scheduling, resource allocation, and fault tolerance under load. - You write infrastructure and systems-level code in Go, Rust or C++. - You've supported both long-running training jobs and high-availability inference services, and you know why they pull infrastructure in opposite directions. - You own problems end-to-end and do well when the path forward isn't laid out for you. ADDITIONAL EXPERIENCE WE VALUE - Inference serving stacks: vLLM, SGLang, or TensorRT-LLM. - Slurm or other HPC schedulers. - GPU kernel work in CUDA or Triton — not required, but notable. - High-speed interconnects: InfiniBand, RoCE, or RDMA in production. - Observability for ML workloads: Prometheus, Grafana, or Weights & Biases.

Location: US, Remote Workplace: Remote The Luxor team has built a range of solutions for compute power, including a mining pool, custom firmware, an ASIC brokerage desk, Hashrate derivatives and compute to power CI and agents. We service clients across the globe. Our client base is growing rapidly and as such we are looking to expand our platform engineering team. We're looking for a Senior Platform Security Engineer with strong expertise in security and compliance. You'll join Luxor AI's Platform team and own the entire area of security and compliance within the team's scope, contributing to building and running production infrastructure. This role is ideal for someone who has experienced how security and compliance operate within a large organization and now seeks the autonomy to evolve that function at a fast-growing startup. It will suit an individual energized by the importance of the data we protect and the reliability we provide to our clients. This role will report directly to the Chief Technology Officer. This is a fully remote role based in U.S.A. with seldom travel if needed. RESPONSIBILITIES - Serve as a day-to-day security advisor for the team you support, advocating security requirements as an engineering team member during sprint planning, architecture reviews, process discussions, product direction, design decisions, and while work is in flight - Build and maintain centralized automated security testing and enforcement in CI/CD, including static and dynamic application security testing (SAST/DAST), software composition analysis (SCA), secrets scanning, and infrastructure-as-code (IaC) scanning - Manage centralized cloud monitoring tooling for vulnerability and configuration management, and integrate ongoing patching and hardening into engineering operations - Continually audit application environments and development processes to ensure compliance with internal and external requirements, and alignment with security standard methodologies - Implement and maintain cloud detection and response monitoring, and act as an incident CIRT responder - Document controls, configurations, and decisions to build durable institutional knowledge - Enforce policies and detection using device management tooling (e.g. FleetMDM) REQUIREMENTS - Bachelor's degree in Computer Science, Information Technology, Cybersecurity, or equivalent practical experience - 8+ years of experience in DevOps, software engineering, or security engineering - Proficiency with CI/CD platforms (e.g. GitHub Actions), infrastructure as code tools (e.g.,Terraform), and configuration management (e.g. Ansible) - Proficiency in at least two of the following languages: Go, JavaScript, Rust, Python - Hands-on cloud security experience in GCP, Alibaba, AWS, OVH, Hetzner and/or Azure, including identity and access management, containerization, network security controls, encryption, logging, and monitoring - Harden access and controls (e.g. IAM Policy) - Hands-on experience with Kubernetes GitOps (e.g. Flux, ArgoCD) - Demonstrated experience implementing automated application and infrastructure security testing, secrets management, and policy-as-code enforcement - Familiarity with penetration testing applications or experience conducting penetration testing engagements - Strong scripting skills in Python, or Bash for automation and integration - Experience supporting third-party attestations and audits (e.g., SOC 2 Type 2, ISO 27001), including preparation, implementation, and ongoing evidence collection - Operated and maintained Zero Trust network infrastructures using Tailscale and Cloudflare WARP, providing direct technical support to users while managing access policies and troubleshooting remote connectivity issues. - Deployed, configured, and administered SIEM (Security Information and Event Management). - Strong communication and governance skills, with the ability to translate technical risk into business terms for technical and non-technical audiences, and to advocate for security without slowing down delivery At Luxor we believe that meaningful contributions can be made by anyone. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, or disability status. Luxor welcomes all! Luxor does not accept unsolicited headhunter and agency resumes. Luxor will not pay any third-party agency or company that does not have a signed agreement with Luxor. HOW WE USE AI IN OUR HIRING PROCESS We use AI tools to support certain parts of our hiring process, and we believe in being transparent about how they’re used. During your application, AI may assist in reviewing CVs, qualifications, and assignments by providing additional insights, but it does not make decisions, every application is evaluated at each step by a member of our hiring team. We also use AI to transcribe interviews so our team can stay focused on the conversation rather than note-taking. Any voice recordings are automatically deleted within 15 days. In addition, AI helps us keep candidates informed about their application status and helps us understand how our process is working so we can improve it over time. In all cases, AI is used to support efficiency and consistency, not to replace human judgment. If you have any questions about how we use these tools or your data, or if you would prefer to opt out of any of these features, please include them in the dedicated section of the application form, and we’ll follow up accordingly.

KORPA is looking for an experienced Flutter developer to build and improve our mobile app for iOS and Android on a project basis. - 10+ years of сommercial software development experience.

Location: San Francisco Perplexity is seeking an Engineering Manager to lead and grow our highly driven Agent Engine team. The Agent Engine team designs and builds powerful agentic experiences including Perplexity Computer https://www.perplexity.ai/computer, the Comet ecosystem, our Agent API, and more. In short, we build frontier intelligence systems for the world's most discerning users. As the engineering manager supporting the Agent Engine team, you'll bring AI expertise, sharp product intuition, and strong leadership and collaboration skills to advance the frontier of what agents can accomplish for our millions of devoted users. You'll lead, grow, and support a team in solving many open problems in AI, including: - Designing frontier agent harnesses and systems to perform high-stakes, mission-critical work for our users; - Leveraging action and decision models that determine, based on complex multimodal states, how to accomplish user-specified objectives; - Providing consistently excellent experiences across local execution, headless cloud, custom executors, and other environments through sound system design and flexible abstractions; - Developing permission architectures, payload classifiers, and other methods to implement secure-by-design agentic capabilities; - Designing optimal data representations and modes of interaction between agents and their environments; - and much, much more. RESPONSIBILITIES - Provide technical leadership across multiple layers of a rapidly growing product in the AI agents space. - Develop and leverage cutting-edge AI systems and related technologies to advance the capability frontier and scale those capabilities for a rapidly growing userbase. - Exercise sharp technical & product intuition to guide the team’s system architectures and product roadmaps. - Drive product reliability, code quality, AI evaluation, testing, and maintenance for the broader team. - Oversee hiring, onboarding, and mentorship for a rapidly growing team. Develop rigorous interview pipelines and work closely with recruiting to source candidates. - Interface with the Perplexity co-founders to deliver strategic objectives that redefine what’s possible in the AI industry. QUALIFICATIONS - Strong foundational familiarity with the AI systems stack. - Proficiency in Python (bonus points for TypeScript, Go, and/or Rust). - Domain knowledge in at least one of the following areas: - Harness engineering - Agent Skills and plugin architectures - Model training and reinforcement learning - Browser technologies (CDP, Playwright, extension development, etc.) - Strong technical intuition and taste for user experience excellence. - Strong organizational skills for managing and delivering parallel technical projects; ability to guide highly-opinionated teams in making sound tradeoffs and prioritization decisions is critical. - Experience managing engineering teams, including recruiting, growing, and retaining high-caliber talent. - 8+ years of engineering experience, with at least 3 of those years as an engineering manager.

Location: San Francisco; New York Workplace: Hybrid ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE Storage decides how fast agent sandboxes start and how safely they share data. Agents need a filesystem that thousands of sandboxes mount at the same time, with strict isolation and the consistency agents need. Agent context is mostly small files, so the access patterns look very different from storage built for large files. The Agent Infra team builds Agent Drive, the distributed filesystem that agent sandboxes mount over FUSE, and the storage underneath it. The work uses tiered caching, replication, content-addressed data, and Linux filesystem internals. In September 2026, Baseten acquired Blaxel https://www.baseten.co/blog/blaxel-is-joining-baseten-to-build-the-future-of-agentic-cloud/, whose sandboxes and Agent Drive filesystem are the foundation for Baseten Sandboxes https://docs.baseten.co/sandboxes. We are building a cloud, not just using one, so storage is designed for many tenants from day one. The team is small and ownership is big: we ship in weeks and months, not multi-year programs. If you care about page cache coherency, FUSE, replication protocols, and content-addressable storage, we'd love to talk. EXAMPLE INITIATIVES - Blaxel is joining Baseten to build the future of agentic infrastructure https://www.baseten.co/blog/blaxel-is-joining-baseten-to-build-the-future-of-agentic-cloud/ - Baseten Sandboxes documentation https://docs.baseten.co/sandboxes RESPONSIBILITIES - Design and implement core Agent Drive features: metadata replication, volume placement, garbage collection, and FUSE client behavior - Solve multi-attach correctness: cache coherency across many FUSE mounts of the same drive, dentry and inode invalidation, write ordering, and crash consistency - Optimize tail latency on read and write paths, including page cache strategy, writeback, prefetch, and metadata RPC batching - Build tenant isolation into the storage layer, including quota enforcement, blast-radius containment, and noisy-neighbor mitigation - Own the storage side of sandbox startup and resume performance - Build durable block storage for sandboxes alongside other Baseten storage teams - Build observability into every layer, drive storage incidents end to end, and turn each one into a permanent fix - Contribute upstream to the open-source storage projects we build on REQUIREMENTS - Strong systems programming background in Go, Rust, and/or C/C++, with comfort reading kernel code - Deep understanding of Linux filesystem and storage internals: VFS, page cache, dentry and inode caches, FUSE, the block I/O stack, fsync semantics, and crash consistency - Hands-on experience building distributed storage systems, content-addressable storage, or large-scale caching infrastructure - Experience profiling I/O, memory, and RPC bottlenecks and optimizing tail latency in production - Ability to debug correctness bugs that only show up under concurrency or partial failure - You move fast and like owning a problem end to end on a small team NICE TO HAVE - Experience with a distributed filesystem or object store such as JuiceFS, CephFS, or S3-compatible storage, or contributions to those projects - Knowledge of consensus and replication protocols (Raft, leader leases, quorum reads) - Experience with snapshot, clone, and copy-on-write filesystem features - Experience operating multi-tenant storage with hard isolation requirements - Familiarity with Linux performance tooling such as eBPF, io_uring, perf, ftrace, and blktrace BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Location: San Francisco; New York Workplace: Hybrid ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE Every agent on Baseten Sandboxes runs in its own microVM. Agents call models, reach tools and APIs, expose services, and more and more often talk to each other. Networking built into today's cloud providers was not designed for very large numbers of isolated machines that suspend, resume, and exchange data constantly. The Agent Infra team builds its own networking stack for sandboxes, designed from the start for that scale. It runs in user space, with VPP (FD.io http://FD.io) as its main data plane technology, and is built from virtualized network functions (VNF/NFV): carrier-grade, low-latency packet processing designed for very large numbers of agents. In September 2026, Baseten acquired Blaxel https://www.baseten.co/blog/blaxel-is-joining-baseten-to-build-the-future-of-agentic-cloud/, whose sandboxes, storage, and networking are the foundation for Baseten Sandboxes https://docs.baseten.co/sandboxes. You'll build multi-tenant virtual private networking that connects each customer's agents and services privately and securely, using modern overlay technologies. We are building a cloud, not just using one. We want engineers who expose the network as a programmable API and write the code, not operators who push router and switch configuration. The team is small and ownership is big: we ship in weeks and months, not multi-year programs. This role is separate from Baseten's GPU-to-GPU networking. Here you'll own how sandboxes connect to each other, to Baseten services, to customer networks, and to the internet. If you've built user-space data planes, virtualized network functions, or multi-tenant cloud networking and want to design one for agents, we'd love to talk. EXAMPLE INITIATIVES - Blaxel is joining Baseten to build the future of agentic infrastructure https://www.baseten.co/blog/blaxel-is-joining-baseten-to-build-the-future-of-agentic-cloud/ - Securing the open frontier with NVIDIA OpenShell and Blaxel sandboxes https://www.baseten.co/blog/announcing-carbon/ - Baseten Sandboxes documentation https://docs.baseten.co/sandboxes RESPONSIBILITIES - Design and build the user-space, VPP-based data plane that connects microVM sandboxes to each other, to Baseten services, and to the internet - Build multi-tenant virtual private networking and overlays (for example EVPN, GENEVE, VXLAN) across regions - Expose the network as a programmable API that the rest of the platform calls - Cut latency and raise connection scale for agent-to-agent and agent-to-service traffic - Build routing that sends each request to the nearest region and keeps connections working as sandboxes suspend and resume - Build secure ingress and egress for sandboxes, including firewalling, static egress IPs, and custom domains with managed TLS - Build L7 proxies for request inspection, filtering, secret injection, and auditing - Own per-sandbox addressing, including IPv6, and network isolation between tenants - Build observability for the packet path across guest, host, and network, and debug production issues end to end - Work closely with the compute, storage, GPU infrastructure, and datacenter teams REQUIREMENTS - Experience with user-space packet processing (DPDK, VPP, or similar) or virtualized network functions (VNF/NFV) - Experience building multi-tenant networking in production (VXLAN, GENEVE, VRF) - Experience building a software-defined network, overlay network, or packet-processing data plane in production - You write production code: strong systems programming in Rust, Go, and/or C/C++. Network architects are welcome if they build, not only configure - Experience building cloud networking as the provider, not only consuming a cloud provider's network - Solid grounding in TCP/IP and routing - Experience building or operating proxies, load balancers, or L4/L7 gateways at scale - Experience debugging latency and throughput problems with packet captures, tracing, and profiling - You move fast and like owning a problem end to end on a small team NICE TO HAVE - Experience with VPP / FD.io http://FD.io, our main networking technology - Experience with GENEVE or VXLAN overlays and EVPN - Linux kernel networking: eBPF/XDP, netfilter or nftables, network namespaces - Familiarity with virtual machine networking: virtio-net, vhost, TAP devices, or networking for Firecracker or Cloud Hypervisor - Experience running IPv6 at scale - Background in telecom user-plane or 5G core networking, or in low-latency trading networks - Experience with multi-region traffic routing, anycast, or BGP - Experience with TLS termination, DNS, and egress security controls BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Location: San Francisco; New York Workplace: Hybrid ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE Every agent on Baseten Sandboxes gets its own microVM, where it writes and runs code, calls tools, and keeps state across long tasks. Agents pause and resume constantly, so the machine underneath has to start, stop, snapshot, fork, and resume fast, and keep every tenant isolated while it does. General-purpose virtualization was not built for that workload. The Agent Infra team builds that layer. Sandboxes run in microVMs with custom Linux kernels on bare-metal servers we operate. You'll own how sandboxes start, stop, snapshot, fork, and resume, and how they perform on each host's CPU and memory, down to the hardware. You'll also own the guest and host side of each sandbox: its identity, how it attaches to different storage classes for performance or capabilities, and the virtual devices that connect it securely to the network. In September 2026, Baseten acquired Blaxel https://www.baseten.co/blog/blaxel-is-joining-baseten-to-build-the-future-of-agentic-cloud/, whose microVM sandboxes are the foundation for Baseten Sandboxes https://docs.baseten.co/sandboxes. We are building a cloud, not just using one. That means designing for many tenants from day one and thinking through what each decision means all the way down to the datacenter. The team is small and ownership is big: we ship in weeks and months, not multi-year programs. If you've worked deep in KVM and Firecracker, Cloud Hypervisor, or QEMU and want to build the computers AI agents run on, we'd love to talk. EXAMPLE INITIATIVES - Blaxel is joining Baseten to build the future of agentic infrastructure https://www.baseten.co/blog/blaxel-is-joining-baseten-to-build-the-future-of-agentic-cloud/ - Securing the open frontier with NVIDIA OpenShell and Blaxel sandboxes https://www.baseten.co/blog/announcing-carbon/ - Baseten Sandboxes documentation https://docs.baseten.co/sandboxes RESPONSIBILITIES - Own the microVM lifecycle for agent sandboxes: start, stop, suspend, resume, snapshot, and fork - Build and tune the custom guest kernels sandboxes run on - Tune host CPU and memory configuration so sandboxes run fast and safely on each type of hardware we operate - Manage host-level resources and sandbox density on each machine - Own startup and resume performance end to end, from VM boot or snapshot restore to the first command - Own the guest and host side of each sandbox: its identity, how it attaches to storage, and the virtual devices that connect it to the network - Design isolation for multi-tenant workloads where the hypervisor is the security boundary - Debug virtualization and isolation issues at scale, from userspace through the kernel to the hardware - Contribute architecture decisions and code back to the open-source projects we build on REQUIREMENTS - Hands-on experience with KVM, plus Firecracker, Cloud Hypervisor, or QEMU - Deep understanding of Linux internals: namespaces, cgroups, memory management, and the boot path - Experience building cloud infrastructure as the provider (multi-tenant compute that customers run on), not only running workloads on a cloud provider - Experience building multi-tenant infrastructure where isolation is a security boundary - Strong systems programming background in Rust and/or Go; C/C++ also welcome - Experience debugging performance and correctness problems across userspace and kernel boundaries - You move fast and like owning a problem end to end on a small team NICE TO HAVE - Contributions to KVM, Firecracker, Cloud Hypervisor, QEMU, rust-vmm, or the Linux kernel - Experience with Xen - Working knowledge of container runtimes and sandboxing (containerd, runc, gVisor, Kata Containers) - Experience with VM snapshot and restore, fast VM resume, or building and tuning custom Linux kernels - Experience running virtualization on bare metal and tuning for specific CPU and memory architectures - Experience building a sandbox, serverless, or function runtime product - Familiarity with Linux performance tooling such as eBPF, perf, and ftrace BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Location: In-Office About Us At Cloudflare, we are on a mission to help build a better Internet. Today the company runs one of the world’s largest networks that powers millions of websites and other Internet properties for customers ranging from individual bloggers to SMBs to Fortune 500 companies. Cloudflare protects and accelerates any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare all have web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Cloudflare was named to Entrepreneur Magazine’s Top Company Cultures list and ranked among the World’s Most Innovative Companies by Fast Company. At Cloudflare, we’re not looking for people who wait for a polished roadmap; we’re looking for the builders who see the cracks in the Internet that everyone else has simply learned to live with. We value candidates who have the instinct to spot a "normalized" problem and the AI-native curiosity to create a solution using the latest tools. Our culture is built on iteration, leveraging AI to ship faster today to make it better tomorrow, while ensuring that every improvement, no matter how small, is shared across the team to lift everyone up. If you’re the type of person who values curiosity over bureaucracy, and that AI is a partner in solving tough problems to keep the Internet moving forward, you’ll fit right in. Available Locations: Austin, TX | New York City, NY | San Francisco, CA About AI Gateway AI inference is becoming core infrastructure. Every serious application will need access to many models across many providers, with reliability, observability, security, cost control, and routing built in from the start. AI Gateway is Cloudflare's bet that this layer belongs at the network edge: close to users, close to compute, and simple enough that a developer can adopt it with one API change. Developers using AI Gateway need answers to two questions. Is the model I'm calling working the way you say it does? And which model should I be using at all? You'll lead the models team that answers both. You'll start with model operations. That's the work behind every model in the catalog: provider integrations, capability testing, pricing and metering, capacity, security reviews, docs, health, and deprecations. Sometimes a launch needs to go live within minutes of a lab's announcement. Sometimes the right call is to hold it. Cloudflare is an infrastructure company at heart, so reliability and accuracy come first; speed comes from preparation and automation. Then you'll bring model intelligence online. That means turning what we learn from Gateway traffic into decisions for customers: capability maps, per-task rankings, auto-routing profiles that pick the right model for each request, evals and regression detection, and market intelligence developers can trust. This is a people-management role reporting to the AI Gateway Engineering Manager. You'll hire, develop, and lead the team, and you'll stay hands-on: writing code, building agent-driven workflows, and getting into the details of a failed launch or a suspicious eval SLO. Expect to manage people, projects, model stock, and operations all at once. About the Role Build and lead the models team: Hire, coach, and grow the people who run the catalog today and build its intelligence over time. Give people real ownership, give direct feedback, and grow leads who can run their own areas. Run model launches end-to-end: Scope integrations, coordinate testing and evals, confirm pricing, metering, capacity, and security, and get docs and announcements ready. Prepare day-zero launches in advance so they can ship in minutes. Keep the catalog true: Make sure what we publish (capabilities, limits, versions, prices) matches what developers actually get. Catch silent regressions, such as broken tool calling, truncated streams, quantization changes, or usage and pricing mismatches, before customers do. Build model intelligence: As it comes online, own the evals, capability maps, and per-task rankings behind our auto-routing profiles and model recommendations. Make sure they measure what they claim to, and connect regressions to routing decisions so customers who chose "auto" aren't the ones who discover a problem. Treat the catalog and intelligence as products: Have a point of view on which models belong in the catalog, which routing profiles to ship, and what market intelligence to publish. Base it on usage, quality, reliability, and cost data. Turn what you learn into roadmap input. Run a lightweight operating cadence: Keep one source of truth for what's in flight, who owns it, what's blocked, and what ships next. Keep Engineering, Product, Partnerships, and other stakeholders aligned and informed without status meetings. Automate relentlessly: Turn repeatable steps into scripts, tools, and agent workflows: endpoint validation, eval runs, catalog and pricing reconciliation, regression detection, and provider follow-ups. Manual effort per launch should fall as the catalog grows. Responsibilities Day-Zero & Quality Launches: Ensure new models launch on day zero with tested capabilities, accurate pricing, docs, and a clear call on routing eligibility. Catalog Reliability & Health: Ensure the catalog reflects reality, and proactively identify when a model or provider is underperforming before customers notice. Auto-Routing & Optimization: Deliver auto-routing that measurably beats a fixed model choice on cost or quality for users. Developer Trust & Methodology: Build and maintain trusted rankings and recommendations grounded in solid methodology. Partner & Stakeholder Management: Set clear expectations for providers, while keeping Engineering, Product, Partnerships, and other stakeholders aligned. Team Leadership & Growth: Provide clear ownership and direction so the team knows what's next, who owns it, and how to grow into bigger roles. Operational Scalability: Continually drive down the human effort required for each launch, eval, and routing update through tools and automation. Desirable Skills, Knowledge, and Experience People Leadership: Experience hiring, managing, and growing engineers or operators, giving direct feedback, delegating real ownership, and expanding scope without becoming a bottleneck. Technical & Shipping Capabilities: Proven track record of taking technical launches or integrations from ambiguity to production across teams and partners. Hands-on ability to write TypeScript, Python, or Rust, read API docs, inspect endpoints, and debug using terminal tools (logs, cURL). Model Ecosystem Expertise: Deep familiarity with new models, labs, and inference providers. Hands-on understanding of streaming, tool calling, structured outputs, rate limits, token pricing, and provider-specific model behavior variations. Product & SRE Mindset: Data-driven product instincts to advocate for adding, modifying, or retiring catalog items. SRE approach to defining, measuring, and alerting on service health, managing incidents calmly, and fixing root causes. Agent Workflows & Detail-Oriented Execution: Daily experience building with agents—scoping work, verifying outputs, and integrating deterministic checks or human judgment appropriately. Meticulous attention to detail regarding model IDs, context limits, per-token prices, and response fields. Bonus Qualifications (Nice to Have): Experience at an inference provider, model lab, model router, eval platform, or API company. Background in evals, ranking/recommendation systems, observability, metering, or usage-based billing. Familiarity with Cloudflare Workers, AI Gateway, Workers AI, or Replicate. Relevant domain backgrounds in engineering management, SRE, partner engineering, evals/applied ML, technical program management, or solutions engineering. Compensation Compensation may be adjusted depending on work location. For Bay Area based hires: Estimated annual salary of $230,000 - $317,000 For New York City, New Jersey, Washington, Washington DC, and California (excluding Bay Area) based hires: Estimated annual salary of $275,000 - $303,000 Equity This role is eligible to participate in Cloudflare’s equity plan. Benefits Cloudflare offers a complete package of benefits and programs to support you and your family.  Our benefits programs can help you pay health care expenses, support caregiving, build capital for the future and make life a little easier and fun!  The below is a description of our benefits for employees in the United States, and benefits may vary for employees based outside the U.S. Health & Welfare Benefits Medical/Rx Insurance Dental Insurance Vision Insurance Flexible Spending Accounts Commuter Spending Accounts Fertility & Family Forming Benefits On-demand mental health support and Employee Assistance Program Global Travel Medical Insurance Financial Benefits Short and Long Term Disability Insurance Life & Accident Insurance 401(k) Retirement Savings Plan Employee Stock Participation Plan Time Off Flexible paid time off covering vacation and sick leave Leave programs, including parental, pregnancy health, medical, and bereavement leave What Makes Cloudflare Special? We’re not just a highly ambitious, large-scale technology company. We’re a highly ambitious, large-scale technology company with a soul. Fundamental to our mission to help build a better Internet is protecting the free and open Internet. Project Galileo : Since 2014, we've equipped more than 2,400 journalism and civil society organizations in 111 countries with powerful tools to defend themselves against attacks that would otherwise censor their work, technology already used by Cloudflare’s enterprise customers--at no cost. Athenian Project : In 2017, we created the Athenian Project to ensure that state and local governments have the highest level of protection and reliability for free, so that their constituents have access to election information and voter registration. Since the project, we've provided services to more than 425 local government election websites in 33 states. 1.1.1.1 : We released 1.1.1.1 to help fix the foundation of the Internet by building a faster, more secure and privacy-centric public DNS resolver. This is available publicly for everyone to use - it is the first consumer-focused service Cloudflare has ever released. Here’s the deal - we don’t store client IP addresses never, ever. We will continue to abide by our privacy commitment and ensure that no user data is sold to advertisers or used to target consumers. Sound like something you’d like to be a part of? We’d love to hear from you! Please note that applicants who progress to the offer stage of the interview process may be asked to attend an in-person interview within one of the Cloudflare Offices or Cloudflare Hubs.  More details about this will be available at that stage of the interview process. This position may require access to information protected under U.S. export control laws, including the U.S. Export Administration Regulations. Please note that any offer of employment may be conditioned on your authorization to receive software or technology controlled under these U.S. export laws without sponsorship for an export license. Cloudflare is proud to be an equal opportunity employer.  We are committed to providing equal employment opportunity for all people and place great value in both diversity and inclusiveness.  All qualified applicants will be considered for employment without regard to their, or any other person's, perceived or actual race, color, religion, sex, gender, gender identity, gender expression, sexual orientation, national origin, ancestry, citizenship, age, physical or mental disability, medical condition, family care status, or any other basis protected by law. We are an AA/Veterans/Disabled Employer. Cloudflare provides reasonable accommodations to qualified individuals with disabilities.  Please tell us if you require a reasonable accommodation to apply for a job. Examples of reasonable accommodations include, but are not limited to, changing the application process, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment.  If you require a reasonable accommodation to apply for a job, please contact us via e-mail at hr@cloudflare.com or via mail at 101 Townsend St. San Francisco, CA 94107.

Location: Multiple Countries Nitka Technologies develops software for customers in the US and Europe and brings together about 300 professionals from Eastern Europe, North and South America, Armenia, Georgia and Kazakhstan. We are looking for an experienced Low-Latency Trading Systems Developer to join a long term US project focused on developing and supporting high-performance systems in a financial technology environment. We offer 100%  remote, full-time work. Responsibilities Design, develop, and maintain low-latency trading system components with a strong focus on performance and reliability. Analyze and optimize system performance to minimize latency and improve efficiency. Develop and support high-performance, real-time system components. Requirements Professional experience developing low-latency or other performance-critical systems. Strong understanding of system performance, concurrency, and optimization techniques. Strong programming skills in C++, Java, C#, Rust, C, or other languages commonly used for high-performance systems. Experience working with high-throughput, real-time systems. Working conditions: Remote work with no time trackers, flexible working hours Full-time (8 hours/day) Attractive USD compensation Paid vacation, holidays, sick leaves

Location: Remote - US Founded in 2012, Bugcrowd is the preemptive security platform that unifies exposure discovery and assessment, offensive testing, and intelligence shaped by AI and human insight to help organizations avoid, discover, and validate real-world risk. Bugcrowd helps security teams move faster by identifying the exposures that matter most so they can act first and stay ahead of attackers. By combining the power of humans and AI, teams can preempt attack paths and prevent breaches. Based in San Francisco and New Hampshire, Bugcrowd is supported by General Catalyst, Rally Ventures, Costanoa Ventures, and others. Visit www.bugcrowd.com . Job Summary The Bugcrowd RL and Reasoning Team focuses on pushing the boundaries of autonomous cybersecurity by building authentic, verifiable reinforcement learning environments for world-leading foundational AI companies. As a Reinforcement Learning Engineer specializing in Reinforcement Learning from Verifiable Rewards (RLVR), you will design and scale automated verification pipelines that transform real-world software vulnerabilities into deterministic reward functions. In this role, you will bridge the gap between low-level security analysis and modern LLM reasoning models, engineering environments where AI agents learn to discover, exploit, and remediate software vulnerabilities with mathematical certainty. Instead of relying on subjective human feedback, your work directly powers the rigorous, verifiable reward signals that teach next-generation frontier AI models how to master complex cybersecurity domain logic. You will work at the intersection of fuzzing, dynamic program analysis, system exploitation, and scalable ML infrastructure to shape the safety and offensive/defensive capabilities of future artificial intelligence. Essential Duties and Responsibilities Design, build, and deploy high-throughput RLVR (Reinforcement Learning from Verifiable Rewards) environments that evaluate LLM action sequences against deterministic execution outcomes. Develop automated test harnesses, sandboxes, and verification engines that convert complex vulnerability research (e.g., memory corruption, web security, logic bugs) into binary pass/fail reward signals. Integrate Bugcrowd’s Mayhem automated analysis platform and real-world vulnerability feeds into continuous, scalable RL environment generation pipelines. Architect safe, isolated, and highly reproducible execution environments (using Docker, BuildKit, or Nix) capable of running thousands of simultaneous agent-driven exploitation and patching trajectories. Collaborate directly with researchers at frontier AI labs including Anthropic, OpenAI, and Cohere to define standard benchmark formats, observation spaces, and verifiable evaluation metrics for cybersecurity tasks. Implement precise telemetry, ground-truth verification algorithms, and trajectory logging to analyze agent reasoning paths and prevent reward hacking or false positives. Build low-level instrumentation and debugging tools to monitor memory states, process executions, and network behaviors during agent interaction cycles. Optimize infrastructure performance and environment reset latency to support massive-scale parallel sampling and distributed RL training workflows. Benchmark and evaluate frontier AI model performance across diverse offensive and defensive security challenges, such as automated fuzzing, exploit payload generation, and patch validation. Education, Experience, Knowledge, Skills, and Abilities Understanding of RL training workflows used by modern LLM systems, specifically execution-based feedback or Reinforcement Learning from Verifiable Rewards (RLVR). Proficiency developing applications in Python and low-level systems programming in C, with Rust experience being a strong plus. Solid understanding of software vulnerabilities, binary exploitation, fuzzing methodologies, or program analysis. Experience with DevOps pipelines (e.g., GitHub Actions), reproducible builds (Docker, BuildKit, Nix), and comfort working with Linux systems and low-level debugging. Experience working with or building benchmark environments (e.g., CTFs, SWE-bench, security challenges, or execution sandboxes). Preferred Experience Experience designing custom reward functions, ground-truth verifiers, or automated grading engines for AI safety and reasoning models. Background in low-level program analysis tools, sanitizers (e.g., ASan/MSan), compiler instrumentation, or automated exploit generation tools. Proven track record of participating in or developing competitive cybersecurity benchmarks, CTFs, or open-source AI evaluation frameworks. Working Conditions and Physical Requirements The ideal candidate must be able to complete all physical requirements of the job with or without reasonable accommodation. Sitting and / or standing - Must be able to remain in a stationary position 50% of the time Carrying and / or lifting - Must be able to carry / move laptop as needed throughout the work day. Environment - remote, work-from-home 100% of the time. Pay Range Disclosure At Bugcrowd, we strive for fairness, equality and to create an environment that allows our people to perform at their very best. Our compensation philosophy is to foster a collaborative community that rewards, attracts and retains the best possible talent. The provided salary details are based on US national averages and we retain the flexibility to tailor to the needs of the business. The national estimate for the current base range for the position of $176,400 - $242,550. This position may also be eligible to participate in a discretionary bonus program or commission plan, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance. Culture At Bugcrowd, we understand that diversity in the workplace is vital to a company’s success and growth. We strive to make sure that people are included and have a sense of being part of making Bugcrowd not only a great product but a great place to work. We regularly hear from both customers and researchers that Bugcrowd feels like a family, and we strive to maintain that internally as well. Our team consists of a broad range of people: musicians, adventure sports junkies, nature lovers, parents, cereal enthusiasts, night owls, cyclists, artists—you get the point. At Bugcrowd, we are solving security threats and vulnerabilities that are relevant to everyone, therefore we believe solving these problems takes all kinds of backgrounds. We value the perspectives and experiences people from underrepresented backgrounds bring. Disclaimer This position has access to highly confidential, sensitive information relating to the technologies of Bugcrowd. It is essential that the applicant possess the requisite integrity to maintain the information in the strictest confidence. The company is authorized to obtain background checks for employment purposes under state and federal law. Background checks will be conducted for positions that involve access to confidential or proprietary information (including trade secrets). Background checks may include Social Security verification, prior employment verification, personal and professional references, educational verification, and criminal history. Applicants with conviction histories will not be excluded from consideration to the extent required by law. Any personal data you submit in connection with your application will be processed in compliance with Bugcrowd's Privacy Policy, which you may review here: https://www.bugcrowd.com/privacy . Equal Employment Opportunity: Bugcrowd is EOE, Disability/Age Employer. Individuals seeking employment at Bugcrowd are considered without regards to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, or sexual orientation. Bugcrowd is committed to the full inclusion of all qualified individuals. In keeping with our commitment, Bugcrowd will take the steps to assure that people with disabilities are provided reasonable accommodations. Accordingly, if reasonable accommodation is required to fully participate in the job application or interview process, to perform the essential functions of the position, and/or to receive all other benefits and privileges of employment, please contact HR at ADA at bugcrowd.com. Apply at: https://www.bugcrowd.com/about/careers/

Location: San Francisco Perplexity is seeking energetic engineers to join our highly driven Agent Engine team. The Agent Engine team designs and builds powerful agentic experiences including Perplexity Computer https://www.perplexity.ai/computer, the Comet ecosystem, our Agent API, and more. In short, we build frontier intelligence systems for the world's most discerning users. As an engineer on our Agent Engine team, you'll bring AI expertise, sharp product intuition, and a tinkerer's mindset to advance the frontier of what agents can accomplish for our millions of devoted users. You'll work across engineering and applied research to solve many open problems in AI, including: - Designing frontier agent harnesses and systems to perform high-stakes, mission-critical work for our users; - Leveraging action and decision models that determine, based on complex multimodal states, how to accomplish user-specified objectives; - Providing consistently excellent experiences across local execution, headless cloud, custom executors, and other environments through sound system design and flexible abstractions; - Developing permission architectures, payload classifiers, and other methods to implement secure-by-design agentic capabilities; - Designing optimal data representations and modes of interaction between agents and their environments; - and much, much more. QUALIFICATIONS - Strong foundational familiarity with the AI systems stack. - Proficiency in Python (bonus points for TypeScript, Go, and/or Rust). - Significant experience in at least one of the following areas: - Harness engineering - Agent Skills and plugin architectures - Model training and reinforcement learning - Browser technologies (CDP, Playwright, extension development, etc.) - Strong technical intuition and taste for user experience excellence. - Comfortable working with a small, fast-moving team; bias toward ownership. - A passion for shipping products that surprise and delight.