Baseten
Baseten
Baseten машиналық оқыту және жасанды интеллект қолданбаларын іске қосатын компанияларға модель инференсі инфрақұрылымын ұсынады. Оның платформасы жоғары өткізу қабілеті бар инференсті, жылдам орналастыруды, автоматты масштабтауды, қауіпсіз корпоративтік модельдерді ұсынуды және ашық бастапқы кодты модельдерді қаптамаға жинауға қолдауды біріктіріп, жылдам әрі ауқымды қызмет көрсетуге арналған. Baseten инженерлік және машиналық оқыту командаларына модель инфрақұрылымының операциялық талаптарын басқаруға көмектеседі, осылайша олар нақты салаға арналған модельдерді әзірлеуге назар аудара алады. Компания жасанды интеллект, SaaS және корпоративтік технологиялар салаларында жұмыс істейді, командасында 11–50 қызметкер бар.

Software Engineer, LLM Inference Platform - Baseten Hybrid

Build Baseten’s distributed LLM inference platform for AI companies. Work across Kubernetes orchestration, model APIs, routing, autoscaling, and production observability.

Сипаттама

  • Build infrastructure and orchestration for large-scale distributed LLM inference, covering routing, autoscaling, scheduling, and runtime management.
  • Develop and operate Model APIs supporting structured outputs, tool and function calling, and multimodal serving.
  • Implement API versioning, request validation, usage metering, quotas, and authentication.
  • Create instrumentation for metrics, traces, and logs, along with repeatable benchmarks for speed, reliability, and quality.
  • Define strong practices for testing, release automation, and operational excellence.
  • Diagnose and strengthen production systems across Kubernetes, distributed runtimes, networking, and GPU workloads.
  • Collaborate with Inference Performance engineers and partner teams to deliver customer-facing optimizations.
  • Lead projects from architecture and deployment through monitoring and iteration informed by customer feedback.
  • Balance system performance, reliability, operational simplicity, and developer experience.

Талаптар

  • Bachelor’s, master’s, or doctoral degree in computer science, engineering, or a related field, or equivalent practical experience.
  • At least three years of experience building and operating distributed systems, backend infrastructure, or large-scale APIs where reliability, latency, and scale are critical.
  • Demonstrated ownership of low-latency, dependable backend services involving rate limiting, authentication, quotas, metering, and migrations.
  • Strong infrastructure judgment, including experience with profiling, tracing, capacity planning, and SLO management.
  • Ability to diagnose performance and reliability problems across application, runtime, and infrastructure layers.
  • A strong focus on developer experience.
  • Willingness to learn new languages, frameworks, and systems, with an interest in inference engineering.
  • Excellent written communication and collaboration skills, including clear design documentation and effective cross-functional work.
  • Experience with or contributions to LLM inference engines or frameworks such as vLLM, SGLang, TensorRT-LLM, TGI, or Dynamo is a plus.
  • Deep Kubernetes experience, including operators and custom resources; familiarity with service meshes or API gateways is a plus.
  • Experience with distributed scheduling, autoscaling, or service orchestration is a plus.
  • Experience running GPU workloads in production is a plus.
  • Background in developer-facing infrastructure or APIs, or contributions to open-source infrastructure or machine-learning systems, is a plus.
  • Familiarity with observability tools, CI/CD systems, or release automation is a plus.

Артықшылықтар

  • Competitive compensation with meaningful equity.
  • For U.S. employees and dependents, full coverage of medical, dental, and vision insurance.
  • Flexible paid time off, including a company-wide Winter Break.
  • Paid parental leave.
  • Fertility and family-building support through a Carrot stipend.
  • For U.S. employees, a company-facilitated 401(k) plan.
  • Exposure to a range of ML startups, creating substantial opportunities for learning and professional networking.

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