Staff Inference Engineer

Uniphore; Palo Alto (CA); On-site; USD 190,000 - 240,000; Full time

Benefits offered by this job

Annual incentive

Pre-IPO stock options

Medical, dental, vision

401(k) with company match

Generous paid time off

Job summary

Uniphore, a leading Business AI company, seeks a Staff Inference Engineer to own the inference layer across cloud and on-premises. You will implement context-aware runtimes, optimize KV-cache and prompt-prefix handling, and drive high-performance multi-GPU deployments.

This hands-on role collaborates with research, model engineering, and platform teams. You will contribute to autoregressive inference, model lifecycle management, and scalable serving with zero-downtime upgrades.

Qualifications

• 5+ years engineering with GPU systems or ML serving.
• Experience with vLLM, SGLang, or TensorRT-LLM preferred.
• Strong Python and C++ (C++17/20); CUDA/C++ debugging.

Responsibilities

• Context-aware runtime: integrate context selection and model execution.
• KV cache management, routing, eviction, and offloading.
• Optimize inference: speculative decoding, batching, and quantization.
• Lifecycle: manage models/adapters with zero-downtime swaps.
• Observability and reliability: DCGM, Prometheus, Grafana integration.

Skills

Python

C++ (C++17/20)

GPU systems

CUDA / HIP kernels

Model inference

Profiling & optimization

vLLM / SGLang / TensorRT-LLM

PyTorch internals

Multi-GPU scaling

Education

Bachelor's or higher in CS/EE

Tools

CUDA

HIP

Triton

CUTLASS

Job description

## Staff Inference EngineerApply: USA - CA - Palo Alto: Full time: Posted Today: JR101237Uniphore is the Business AI company. Our sovereign, composable and secure AI platform connects enterprise data, fine-tunes AI models and deploys agentic AI across the enterprise. We empower every worker to boost productivity and help businesses grow faster, operate smarter and reduce costs. Trusted by more than 2,000 businesses globally, and recognized on the Deloitte Fast 500, Uniphore delivers on the promise of AI as a transformative force for business. **Job Description:****Staff Inference Engineer –**Uniphore is building an enterprise AI pla4orm where agents operate across voice, video, text, and structured data in real time. We are looking for a Senior or Staff Inference Engineer to own the inference layer — combining deep model inference expertise with systems engineering to deliver a context-aware runtime serving finetuned SLMs across cloud, on-premises, and widely used GPU platforms. This is a hands-on individual contributor role working closely with Uniphore’s research, model engineering, knowledge systems, agent, and pla4orm teams.What You Will Build**Context-aware runtime:** Integrate context selection, compression, conversation compaction, and persistent task state with model execution. Adapt context budgets, model selection, and reasoning effort to each task.**KV-cache and computation optimization:** Build paged KV-cache and prompt-prefix-cache management, cache-aware routing, retention, eviction, and offloading. Preserve correctness as context, permissions, model versions, and adapters change.**Inference performance:** Implement and tune speculative decoding, attention kernels, continuous batching, quantization, chunked prefill, and multi-GPU parallel execution.**SLM and adapter lifecycle:** Build model and adapter lifecycle management, multi-adapter serving, zero-downtime hot swapping, and resource scheduling.**Production reliability:** Implement observability (DCGM, Prometheus, Grafana), tenant isolation, overload protection, failure recovery, and safe rollout and rollback.**Cross-environment deployment:** Build portable runtime integrations across NVIDIA and AMD GPU pla4orms, cloud and on-premises environments, and single- or multi-GPU configurations. **Technical Expertise****Model inference internals:** Autoregressive generation, prefill vs. decoding, attention architectures, compute- vs. memory-bound execution, numerical precision.**Speculative decoding:** Draft-and-verify execution, acceptance/rejection sampling, adaptive speculation, and throughput tradeoffs.**GPU kernels:** Flash attention-style kernels, tiling, operator fusion, memory access patterns.CUDA, HIP, Triton, or CUTLASS proficiency.**KV and prefix caching:** Paged KV storage, fragmentation, cache quantization, offloading, cross request prefix reuse, tenant isolation.**Context-window management:** Token budgeting, context selection, prompt compression, conversation compaction, sliding window and sparse attention.**Serving and scaling:** Continuous batching, chunked prefill, admission control, quantization, multi-GPU parallelism, prefill/decode disaggregation.**Qualifications Required:**• 5+ years engineering, including 3+ years hands-on with GPU systems, distributed inference, or ML serving• Production experience with vLLM, SGLang, or TensorRT-LLM• Strong Python and C++ (C++17/20); ability to read and debug CUDA C++ or HIP kernels• Hands-on GPU profiling and performance optimization**Preferred:**• Custom GPU kernel development (CUDA, HIP, Triton, CUTLASS)• PyTorch internals — torch. Compile, custom operators, MoE or disaggregatedprefill/decode• LoRA/SLM adapter serving and zero-downtime hot swapping• NVIDIA and AMD Instinct hardware in production• Telemetry with DCGM, AMD SMI, Prometheus, and Grafana Comp range/OTE: 190- 240K USD **Hiring Pay Range:**$200,000 - $350,000**Benefits:** In addition to competitive base pay, this position also includes an annual incentive opportunity based on target achievement, pre-IPO stock options, benefits including medical, dental, vision, 401(k) with a match, and more, plus generous paid time off, paid holidays, paid day off for your birthday and other paid leave policies to support employees through all phases of life.**Location preference:**USA - CA - Palo AltoUniphore is an equal opportunity employer committed to diversity in the workplace. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, disability, veteran status, and other protected characteristics.