> Markdown version of [/jobs/ext/1830359-software-engineer-training-inference-c](https://www.wearedevelopers.com/jobs/ext/1830359-software-engineer-training-inference-c). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Software Engineer - Training/Inference (C++)

- **Company:** SPACEXAI LLC
- **Location:** Palo Alto, CA, United States
- **Salary:** $180,000.0
- **Contract:** Permanent contract
- **Skills:** C++ (Programming Language), Code Generation, Continuous Delivery, Continuous Integration, System Programming, Load Balancing, Autoscaling, Large Language Models, Caching, Parallel Computation, Low Latency, TensorRT
- **Published:** July 14, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3318672793&tx=KL707PFK&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

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., * 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.

## Description

* 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, * 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.


## Related Videos

- [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated)
- [HTTP headers that make your website go faster](https://www.wearedevelopers.com/videos/1676-http-headers-that-make-your-website-go-faster)
- [Fifty Shades of Kubernetes Autoscaling](https://www.wearedevelopers.com/videos/813-fifty-shades-of-kubernetes-autoscaling)
- [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms)
- [Trends, Challenges and Best Practices for AI at the Edge](https://www.wearedevelopers.com/videos/630-trends-challenges-and-best-practices-for-ai-at-the-edge)
- [Event based cache invalidation in GraphQL](https://www.wearedevelopers.com/videos/433-event-based-cache-invalidation-in-graphql)

## Related Articles

- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)
- [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)