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Remote Ran Optimization Engineer Jobs in Texas (NOW HIRING)

AI Engineer Location: 100% Remote Duration: 6+ month contract-to-hire Requirement: * Implemented ... LLM optimization & improved response handling. Responsibilities: * Design, develop, and deploy ...

Siege Media is a growing and remote-first GEO agency! We are nationally recognized on Inc.'s Best ... for developers and clients to implement * Coordinate and oversee SEO/GEO content creation by ...

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Remote Ran Optimization Engineer information

What is the difference between Remote Ran Optimization Engineer vs Radio Network Optimization Engineer?

AspectRemote Ran Optimization EngineerRadio Network Optimization Engineer
CredentialsTypically requires a degree in telecommunications, certifications like Nokia, Ericsson, or vendor-specific trainingSimilar credentials, often with certifications in radio network design and optimization
Work EnvironmentPrimarily remote, collaborating with teams across locations, using remote toolsUsually onsite or hybrid, with field visits for testing and adjustments
Industry UsageCommon in telecom providers, network vendors, and remote service providersUsed in telecom companies, network operators, and infrastructure firms

Both roles focus on optimizing radio networks, but the Remote Ran Optimization Engineer emphasizes remote work and virtual collaboration, while the Radio Network Optimization Engineer may involve more onsite activities. Both require similar technical skills and certifications, with the main difference being the work environment.

What are the most commonly searched types of Ran Optimization Engineer jobs in Texas?

The most popular types of Ran Optimization Engineer jobs in Texas are:

What are popular job titles related to Remote Ran Optimization Engineer jobs in Texas?

For Remote Ran Optimization Engineer jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Remote Ran Optimization Engineer jobs in Texas look for?

The top searched job categories for Remote Ran Optimization Engineer jobs in Texas are:

What cities in Texas are hiring for Remote Ran Optimization Engineer jobs?

Cities in Texas with the most Remote Ran Optimization Engineer job openings:

Infographic showing various Remote Ran Optimization Engineer job openings in Texas as of August 2026, with employment types broken down into 92% Full Time, 3% Part Time, and 5% Contract. Highlights an 86% Physical, 5% Hybrid, and 9% Remote job distribution.

Staff Engineer, Inference Optimizations

DigitalOcean

Austin, TX • Remote

$191K - $239K/yr

Full-time

Posted 23 days ago


Job description

DigitalOcean is seeking a Senior Engineer 2 to play a key technical role in our AI Inference Optimization team. DigitalOcean aims to be the Inference Cloud of choice for digitally native companies and you will help ensure we can offer the industry-leading performance for our inference services. You will be responsible for the architectural decisions that maximize throughput and minimize latency for the world's most advanced large models. As an IC leader, you will act as a force multiplier for the engineering organization, solving the most complex bottlenecks in memory bandwidth and compute utilization while guiding the technical roadmap for our high-performance inference fleet.

What You'll Do:
  • Performance Architecture: Lead the technical strategy for benchmarking and performance optimizations at the inference engine and GPU kernel layers, ensuring our infrastructure extracts maximum value from every TFLOP.
  • Deep-Dive Optimization: Engineer solutions for complex performance issues, including attention layer optimizations, memory and precision management, and advanced parallelization across multi-node GPU clusters. 
  • Technological Innovation: Proactively implement cutting-edge optimization techniques to keep DigitalOcean at the forefront of the Gen AI landscape. Some examples of projects you may work on:
    • Improving batch size performance using AMD's AITER library for AMD MI355X - identify and tune AITER's CK (composable kernel) or ASK (assembly) to optimize FP8 / BF16 
    • Identify kernel fusion opportunities for GLM-5 kernels for different layers of the Transformer block (FlashAttention, RMS Norm)
    • Tune expert gateway router kernels for MoE models like Qwen3-235B, DeepSeek V3, GLM-5 etc
  • Hardware & Ecosystem Mastery: Act as the subject matter expert on modern GPU families (NVIDIA/AMD) and their software stacks (CUDA, ROCm, TensorRT, OpenAI Triton), advising on hardware procurement and software integration.
  • Precision Optimization: Develop and deploy state-of-the-art quantization techniques (FP8, INT8, and experimental FP4) to double throughput without losing accuracy.
  • Technical Mentorship: Lead by example through high-quality code and design reviews, elevating the technical bar for the team without the administrative overhead of direct management.
  • Strategic Collaboration: Partner with Product Management and TPMs to translate "theoretical hardware limits" into "shippable product features," ensuring our platform is both powerful and developer-friendly.
  • Community Leadership: Maintain a strong presence in the GPU infrastructure and model performance optimization communities, contributing to and integrating the best of open-source AI.
What You'll Bring to DigitalOcean:
  • Technical Depth: 5+ years of experience in high-performance computing or AI infrastructure, with a proven track record of solving compute utilization and memory bandwidth bottlenecks.
  • Gen AI Literacy: Deep familiarity with the Gen AI (LLM, VLM, LMM) landscape, including the specific quirks and architectural requirements of major model families.
  • Optimization Expert: Hands-on experience with attention-layer optimizations and parallelization strategies across distributed GPU environments.
  • Hardware Fluency: Comprehensive understanding of NVIDIA and AMD GPU architectures and their respective software ecosystems (CUDA, ROCm, etc.).
  • Open Source Mastery: Extensive experience integrating, building with, and contributing to open-source software projects.
  • Systems Design: Excellent system design skills, particularly related to low-level GPU programming - optimization, memory access patterns, and parallel execution.
  • Leadership through Influence: Experience acting as a technical lead, driving design and delivery through cross-functional alignment and expert-level delegation.
  • Low-Level Mastery: Deep understanding of GPU architectures (SMs, Warp scheduling, Tensor Cores).
  • The Toolkit: Expert-level Triton or CUDA. If you've contributed to the Triton compiler or wrote custom CUDA kernels for a major LLM, we want you.
Compensation Range: 
  • $191,200 - $239,000

*This is a remote role

JR: 2026-7625

#LI-Remote