Prime Intellect
Member of Technical Staff - Inference
Part remote, part office, you need to live within commuting distance of a named location.
Hybrid · Remote
Employer listed it 3 months ago · Added yesterday
Been open since 3 months ago. Long-running listings are sometimes left up after the role is filled.
Salary
Not stated
Location
Hybrid · Remote
Timezone
Not stated
Contract
Full-time
Experience
Lead
Category
Software
This employer didn't state pay. Jobs like this usually pay around $200k–$275k a year, a typical range taken from 596 lead-level software roles on Nomaders that do state pay. It's a guide, not an offer.
Remote flexibility
Hybrid
This role is only partly remote, the employer expects time in the office around Remote, San Francisco, Hybrid, so you need to live within commuting distance.
What the employer says
- Source listing states candidate location: "Remote, San Francisco, Hybrid"
- Listing mentions "Hybrid"
What Nomaders makes of it
- Not suitable if you plan to move between countries
The quotes above are the employer's own words; the reading is ours. Always check the original listing and employment terms before working from another country.
About the role
Own Your Intelligence
Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.
Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.
Prime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.
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.
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.
Benefits
- ·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
How to apply
- 1Check the flexibility label above, hybrid, matches where you plan to live and work.
- 2Tailor your CV to the role at Prime Intellect, mentioning your remote working experience.
- 3Apply directly on the employer's careers page using the button below. Nomaders never handles your application.
Found 1d ago. Last checked today. Always confirm the details on the original posting, salary and location can change after publication.
Listing sourced from Company boards.
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