Software Engineer- GPU Fabric Observability
Bangun sistem observabilitas GPU untuk deteksi masalah real-time
Sebagai Lead Software Engineer di Baseten, kamu akan membangun sistem observabilitas dan analisis penyebab masalah untuk GPU fabrics. Sistem ini akan mengumpulkan dan mengkorelasi sinyal tinggi dari berbagai sumber untuk memberikan diagnosis yang dapat diambil langkahnya saat insiden terjadi. Kamu akan bekerja dengan teknologi seperti NVIDIA, RDMA, dan GPUDirect.
Kenapa Menarik?
Bergabung dengan Baseten untuk mengembangkan platform AI yang digunakan oleh perusahaan-perusahaan terkemuka.
Tanggung Jawab Utama
- Mengembangkan mesin telemetri real-time untuk pengumpulan, pengurangan, penyimpanan, dan query telemetri GPU
- Membangun kolektor, probe, dan korelasi yang memahami topologi untuk mendeteksi masalah latensi, drops, dan congestion
- Mengaitkan perilaku jaringan dengan operasi RDMA, GPUDirect, dan transfer KV cache
- Mengidentifikasi penyebab masalah dari fabric, host, NIC, GPU, atau scheduler
- Mengembangkan sistem observabilitas GPU untuk Baseten
Persyaratan
- Pengalaman dalam sistem terdistribusi dan observabilitas
- Pemahaman mendalam tentang teknologi GPU dan jaringan
- Kemampuan untuk bekerja dengan telemetri tinggi dan korelasi real-time
- Pengalaman dalam pengembangan perangkat lunak untuk infrastruktur GPU
Skills Wajib
Keywords
Lihat Deskripsi Asli dari Ashby Job Boards
Deskripsi asli dari Ashby Job Boards
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 to ship AI products. THE ROLE Baseten is building its own GPU infrastructure for large-scale inference. As we move into large scale, high-density NVIDIA systems, the hardest failures are intermittent, cross-layer, and difficult to prove: RoCE congestion, InfiniBand stalls, ECN/DCQCN mis-tuning, bad optics, RNIC issues, host kernel stalls, GPU driver problems, and workload symptoms that look like network problems, but are not. We are hiring a Lead Software Engineer to build a first-class observability and root-cause analysis system for GPU fabrics. This is a hard distributed systems problem, not a dashboarding problem. The system will collect high-volume signals from switches, hosts, active probes, and inference services; reduce and correlate them in real time; understand topology and service ownership; and produce actionable diagnosis while an incident is still unfolding. This role sits at the boundary between networking and inference software. RDMA data paths, GPUDirect transfers, prefill/decode disaggregation, KV cache movement, request routing, and workload backpressure can all create fabric symptoms or hide real fabric failures. The goal is to tell an operator, quickly and with evidence, whether an incident is caused by the fabric, host, NIC, GPU, RDMA path, scheduler, or serving layer — and what to do next. EXAMPLE INITIATIVES - Real-time telemetry engine — Build the ingestion, reduction, storage, and query path for high-cardinality fabric, host, GPU, and workload telemetry. - Service-aware fabric diagnosis — Build collectors, probes, and topology-aware correlation to detect latency, drops, stalls, congestion, bad paths, and degradation. - Software-aware RDMA diagnosis — Tie network behavior to RDMA operations, GPUDirect paths, KV cache transfers, prefill/decode disaggregation, and request latency. RESPONSIBILITIES - Own Baseten’s GPU fabric observability and root-cause analysis architecture. - Build telemetry pipelines across switches, NICs, hosts, GPUs, Kubernetes, and inference services. - Model topology, flow paths, service ownership, and failure domains. - Separate true fabric faults from host, NIC, GPU, kernel, driver, RDMA, scheduler, and workload failures. - Create clear operator workflows for triage, remediation, and post-incident learning. REQUIREMENTS - Staff-level or senior staff-level experience building production infrastructure software. - Strong distributed systems background, especially streaming systems, telemetry pipelines, diagnostics, or control-plane software. - Experience building systems that process high-volume, high-cardinality, noisy operational data. - Understanding of networking fundamentals and high-performance networks - Ability to work with low-level infrastructure signals and build practical correlation, anomaly detection, or root-cause analysis systems. BENEFITS - Competitive compensation, including meaningful equity. - 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 - 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).
Konteks Gaji
Posisi Engineering serupa di LokerDollar dibayar sekitar $170k/yr (kisaran $11.194k–999.999k/yr, dari 492 listing aktif).
Perekrutan di Baseten
Baseten punya 27 lowongan aktif lain di LokerDollar dan telah merekrut di sini sejak 23 Jun 2026 — di kategori Engineering.
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Pertanyaan yang sering diajukan
- Apakah Software Engineer- GPU Fabric Observability di Baseten bisa dikerjakan remote?
- Posisi ini berlokasi di Remote. Detail remote/onsite ada di deskripsi lowongan.
- Berapa gaji untuk Software Engineer- GPU Fabric Observability di Baseten?
- Rentang gaji yang tercantum untuk posisi ini adalah $200k–380k/yr.
- Jenis pekerjaan apa Software Engineer- GPU Fabric Observability di Baseten?
- Posisi ini adalah pekerjaan full time.
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- Klik tombol "Lamar" pada halaman ini untuk menuju halaman aplikasi resmi Baseten.
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