Staff Inference Engineer
Palo Alto, CAFull-time$175–219K/yrPosted 1w agoStill listed 3 days ago
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Job overview
Uniphore is seeking a Senior or Staff Inference Engineer to own the inference layer of its enterprise AI platform. The hands-on individual contributor will build a context-aware runtime serving fine-tuned small language models across cloud, on-premises, and GPU environments. The role focuses on inference optimization, model and adapter lifecycle management, production reliability, and collaboration with research, model engineering, knowledge systems, agent, and platform teams.
Skills & qualifications
Skills
Qualifications
Benefits
Full job description
Uniphore 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:
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Custom GPU kernel development (CUDA, HIP, Triton, CUTLASS)
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PyTorch internals — torch. Compile, custom operators, MoE or disaggregated prefill/decode
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LoRA/SLM adapter serving and zero-downtime hot swapping
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NVIDIA and AMD Instinct hardware in production
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Telemetry with DCGM, AMD SMI, Prometheus, and Grafana
Hiring Pay Range: $175,000 - $218,700 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 Alto Uniphore 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.
For more information on how Uniphore uses AI to unify—and humanize—every enterprise experience, please visit www.uniphore.com.
How this job compares
Set against the 5,890 open Machine Learning Engineer jobs on Olive Jobs, counted October 27, 2026.
- Pay
- This posting$175–219K/yr
- California median$174–260K/yr1,324 salary ranges
- US median$145–245K/yr4,657 salary ranges
This range's midpoint, $197K, is 10% below the California median midpoint ($218K) — in the middle half of Machine Learning Engineer salary ranges in California.
- Openings
- Olive Jobs lists 1,787 open Machine Learning Engineer jobs in California and 5,890 across the US. 5% of them nationwide are listed as remote or flexible.
- Experience
- This posting asks for at least 3 years of experience; Machine Learning Engineer postings that state a minimum ask for 5 years at the median (4,730 postings).
- Education
- 2,410 of the 5,890 open Machine Learning Engineer jobs (41%) ask for a bachelor's degree or higher.
Counted across the open, public US postings on Olive Jobs on October 27, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.
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