Job description

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars. SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next. ABOUT THE ROLE You'll join the Grok Voice Model team as a software engineer. You'll design, build, and run the software systems behind Grok Voice, which delivers natural, low-latency, multilingual spoken interactions across devices and in real time. The team owns the full engineering stack behind the model, from large-scale data processing to distributed training to production deployment. RESPONSIBILITIES Design, build, and maintain large-scale distributed data pipelines that ingest, process, and annotate real-world and synthetic audio, so the training and evaluation systems get reliable, high-quality data. Build and optimize the distributed training infrastructure and software for speech-language models in JAX and PyTorch, including the systems that run supervised fine-tuning and reinforcement learning at scale. Build automated evaluation and testing systems that measure accuracy, audio quality, latency, and real-time performance, plus the experimentation infrastructure used to validate every model release. Integrate voice models into production applications and real-time services, write the technical specifications for spoken interactions, and own deployment from prototype to global scale with a focus on stability, low latency, and reliability. Profile and optimize system performance across training and inference, including throughput, latency, memory use, and compute efficiency. BASIC QUALIFICATIONS Bachelor's degree or higher in Computer Science, Software Engineering, Computer Engineering, Electrical Engineering, or a related engineering field. Expert-level Python, writing clean, efficient, production-quality code for large-scale systems. Hands-on experience building distributed data processing systems with tools like Spark and Ray. Experience building and running large-scale distributed training and inference systems on Kubernetes. Experience implementing and optimizing model training and inference software in JAX or PyTorch. Experience building automated testing, evaluation, and A/B experimentation pipelines for production systems. A proactive, self-driven attitude and the ability to deliver in a fast-paced, high-caliber team. COMPENSATION AND BENEFITS: $150,000 - $450,000 USD Base salary is just one part of our total rewards package at SpaceXSI, 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. SpaceXSI is an equal opportunity employer. For details on data processing, view our Recruitment Privacy Notice .

Requirements

Must-haves

Design, build, and maintain large-scale distributed data pipelines that ingest, process, and annotate real-world and synthetic audio, so the training and evaluation systems get reliable, high-quality data.
Build and optimize the distributed training infrastructure and software for speech-language models in JAX and PyTorch, including the systems that run supervised fine-tuning and reinforcement learning at scale.
Build automated evaluation and testing systems that measure accuracy, audio quality, latency, and real-time performance, plus the experimentation infrastructure used to validate every model release.
Profile and optimize system performance across training and inference, including throughput, latency, memory use, and compute efficiency.
Bachelor's degree or higher in Computer Science, Software Engineering, Computer Engineering, Electrical Engineering, or a related engineering field.
Expert-level Python, writing clean, efficient, production-quality code for large-scale systems.
Hands-on experience building distributed data processing systems with tools like Spark and Ray.
Experience building and running large-scale distributed training and inference systems on Kubernetes.
Experience implementing and optimizing model training and inference software in JAX or PyTorch.
Experience building automated testing, evaluation, and A/B experimentation pipelines for production systems.
A proactive, self-driven attitude and the ability to deliver in a fast-paced, high-caliber team.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

pythonkubernetes

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)