Member of Technical Staff - Inference

Prime Intellect; San Francisco (CA); Hybrid; USD 150,000 - 300,000; Full time

Benefits offered by this job

Cash compensation range of $150-300k

Flexible work arrangement (remote or San Francisco office)

Full visa sponsorship and relocation support

Professional development budget

Regular team off-sites and conference attendance

Opportunity to shape decentralized AI and RL

Job summary

Prime Intellect is looking for a skilled ML Systems Engineer to build and optimize LLM serving infrastructure and inference systems. This hybrid role involves contributing to the scalability of their reinforcement learning training. Successful candidates will have over 3 years of experience in building ML services, strong knowledge of Python and cloud platforms, and a desire to work on cutting-edge AI infrastructure. They offer a cash compensation range of $150-300k with equity and full relocation support.

Qualifications

• 3+ years building and running large-scale ML/LLM services with clear latency/availability SLOs.
• Hands-on with vLLM, SGLang, TensorRT‑LLM.
• Familiarity with distributed and disaggregated serving infrastructure such as NVIDIA Dynamo.
• Deep understanding of prefill vs. decode, KV-cache behavior, batching, and speculative decoding.
• Comfortable debugging CUDA/NCCL, drivers/kernels, and storage.

Responsibilities

• Build infrastructure to serve LLMs efficiently at scale.
• Optimize and integrate inference systems into our RL training stack.
• Design placement and scheduling algorithms for heterogeneous accelerators.
• Implement multi-region/zone failover and traffic shifting.
• Profile kernels, memory bandwidth and transport; apply quantization techniques.

Skills

Building ML Systems at Scale

Inference Backends

Distributed Serving Infra

Inference Internals

Full-Stack Debugging

Python

PyTorch

Cloud & Automation

Kubernetes

GPU & Networking

Job description

Prime Intellect is building the open superintelligence stack - from frontier agentic models to the infra that enables anyone to create, train, and deploy them. We aggregate and orchestrate global compute into a single control plane and pair it with the full RL post‑training stack: environments, secure sandboxes, verifiable evals, and our async RL trainer. We enable researchers, startups and enterprises to run end‑to‑end reinforcement learning at frontier scale, adapting models to real tools, workflows, and deployment contexts.

Role Impact

This is a hybrid position spanning cloud LLM serving, LLM inference optimization and RL systems. You will be working on advancing our ability to evaluate and serve models trained with our RL Lab at scale. The two key areas are:

• Building the infrastructure to serve LLMs efficiently at scale.
• Optimization and integration of inference systems into our RL training stack.

Core Technical Responsibilities

LLM Serving

• Multi‑tenant LLM Serving: Build a multi‑tenant LLM serving platform that operates across our cloud GPU fleets.
• GPU‑Aware Scheduling: Design placement and scheduling algorithms for heterogeneous accelerators.
• Resilience & Failover: Implement multi‑region/zone failover and traffic shifting for resilience and cost control.
• Autoscaling & Routing: Build autoscaling, routing, and load balancing to meet throughput/latency SLOs.
• Model Distribution: Optimize model distribution and cold‑start times across clusters.

Inference Optimization & Performance

• Framework Development: Integrate and contribute to LLM inference frameworks such as vLLM, SGLang, TensorRT‑LLM.
• Parallelism and Configuration Tuning: Optimize configurations for tensor/pipeline/expert parallelism, prefix caching, memory management and other axes for maximum performance.
• End‑to‑End Performance: Profile kernels, memory bandwidth and transport; apply techniques such as quantization and speculative decoding.
• Perf Suites: Develop reproducible performance suites (latency, throughput, context length, batch size, precision).
• RL Integration: Embed and optimize distributed inference within our RL stack.

Platform & Tooling

• CI/CD: Establish CI/CD with artifact promotion, performance gates, and reproducible builds.
• Observability: Build metrics, logs, tracing; structured incident response and SLO management.
• Docs & Collaboration: Document architectures, playbooks, and API contracts; mentor and collaborate cross‑functionally.

Technical Requirements

Required Experience

• Building ML Systems at Scale: 3+ years building and running large‑scale ML/LLM services with clear latency/availability SLOs.
• Inference Backends: Hands‑on with at least one of vLLM, SGLang, TensorRT‑LLM.
• Distributed Serving Infra: Familiarity with distributed and disaggregated serving infrastructure such as NVIDIA Dynamo.
• Inference Internals: Deep understanding of prefill vs. decode, KV‑cache behavior, batching, sampling, speculative decoding, parallelism strategies.
• Full‑Stack Debugging: Comfortable debugging CUDA/NCCL, drivers/kernels, containers, service mesh/networking, and storage, owning incidents end‑to‑end.

Infrastructure Skills

• Python: Systems tooling and backend services.
• PyTorch: LLM Inference engine development and integration, deployment readiness.
• Cloud & Automation: AWS/GCP service experience, cloud deployment patterns.
• Kubernetes: Running infrastructure at scale with containers on Kubernetes.
• GPU & Networking: Architecture, CUDA runtime, NCCL, InfiniBand; GPU‑aware bin‑packing and scheduling across heterogeneous fleets.

Nice to Have

• Kernel‑Level Optimization: Familiarity with CUDA/Triton kernel development; Nsight Systems/Compute profiling.
• Systems Performance Languages: Rust, C++.
• Data & Observability: Kafka/PubSub, Redis, gRPC/Protobuf; Prometheus/Grafana, OpenTelemetry; reliability patterns.
• Infra & Config Automation: Terraform/Ansible, infrastructure‑as‑code, reproducible environments.
• Open Source: Contributions to serving, inference, or RL infrastructure projects.

What we offer

• Cash Compensation Range of $150-300k with significant equity incentives.
• Flexible work arrangement (remote or San Francisco office).
• Full visa sponsorship and relocation support.
• Professional development budget.
• Regular team off‑sites and conference attendance.
• Opportunity to shape decentralized AI and RL at Prime Intellect.

Growth Opportunity

You'll join a team of experienced engineers and researchers working on cutting‑edge problems in AI infrastructure. We believe in open development and encourage team members to contribute to the broader AI community through research and open‑source contributions. We value potential over perfection. If you're passionate about democratizing AI development, we want to talk to you.