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Software Engineer - Training/Inference (C++)

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Last seen by MeritLog September 8, 2026Source: GreenhouseSource version: greenhouse-job-board-v1

Source: the employer's Greenhouse job board. Open the original listing for current details. Availability is not verified for this retained page.

Job details

Work model: On-site
Salary: Not listed by source
Location: Palo Alto, CA
Occupation: Software Developers(O*NET 15-1252.00)

What the role asks for

What you'd do

Architect and implement scalable distributed infrastructure for model serving (load balancing, auto-scaling, batch scheduling, global KV cache).
Optimize latency and throughput of model inference under real production workloads.
Build reliable, high-concurrency serving systems that serve billions of users with 100% uptime, 0% error rate, and excellent tail latency.
Benchmark, fine-tune, and accelerate inference engines (including low-level GPU kernel work and code generation).
Develop custom tools to trace, replay, and fix issues across the full stack - from orchestration down to GPU kernels.
Create robust CI/CD infrastructure for seamless endpoint deployment, image publishing, and inference engine updates.
Accelerate research on scaling test-time compute, RL rollout, and model-hardware co-design for next-generation systems.

What they're asking for

Deep low-level systems programming (C/C++ or Rust)Skill
Experience with large-scale, high-concurrent production serving.Skill
Experience with GPU inference engines (vLLM, SGLang, Triton, TensorRT-LLM, etc.).Skill
Strong background in system optimizations: batching, caching, load balancing, parallelism.Skill
Low-level inference optimizations: GPU kernels, code generation.Skill
Algorithmic inference optimizations: quantization, speculative decoding, distillation, low-precision numerics.Skill
Experience with testing, benchmarking, and reliability of inference services.Skill
Experience designing and implementing CI/CD infrastructure for inference.Skill

Parsed by MeritLog from the employer’s own posting. The full description follows below.

Job description

SpaceXAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates. About the Role: • We are building the high-performance inference platform that serves Grok to millions of users every day with lightning speed and perfect reliability. • As a Member of Technical Staff - Inference, you will design and optimize large-scale model serving systems end-to-end. You will own everything from distributed infrastructure (global KV cache, continuous batching, load balancing, auto-scaling) to deep low-level optimizations (GPU kernels, quantization, speculative decoding, tail latency). • This is a high-impact role where your work directly determines how fast and reliably users interact with Grok at massive scale Responsibilities: • Architect and implement scalable distributed infrastructure for model serving (load balancing, auto-scaling, batch scheduling, global KV cache). • Optimize latency and throughput of model inference under real production workloads. • Build reliable, high-concurrency serving systems that serve billions of users with 100% uptime, 0% error rate, and excellent tail latency. • Benchmark, fine-tune, and accelerate inference engines (including low-level GPU kernel work and code generation). • Develop custom tools to trace, replay, and fix issues across the full stack - from orchestration down to GPU kernels. • Create robust CI/CD infrastructure for seamless endpoint deployment, image publishing, and inference engine updates. • Accelerate research on scaling test-time compute, RL rollout, and model-hardware co-design for next-generation systems. BASIC QUALIFICATIONS: • Deep low-level systems programming (C/C++ or Rust) • Experience with large-scale, high-concurrent production serving. • Experience with GPU inference engines (vLLM, SGLang, Triton, TensorRT-LLM, etc.). • Strong background in system optimizations: batching, caching, load balancing, parallelism. • Low-level inference optimizations: GPU kernels, code generation. • Algorithmic inference optimizations: quantization, speculative decoding, distillation, low-precision numerics. • Experience with testing, benchmarking, and reliability of inference services. • Experience designing and implementing CI/CD infrastructure for inference. COMPENSATION AND BENEFITS: $180,000 - $440,000 USD Base salary is just one part of our total rewards package at SpaceXAI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks. SpaceXAI is an equal opportunity employer. For details on data processing, view our Recruitment Privacy Notice.

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