Sr. Model Engineer, RL Training Framework
Palo Alto, CAJob$180–400K/yrPosted 3 days agoStill listed 2 days ago
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At a glance
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Job overview
SpaceXSI is building a Super Intelligence stack spanning compute, models, and applications. The RL infrastructure team seeks an engineer to develop its RL training framework, designing systems for workloads ranging from small-scale ablations to production training runs. The role also focuses on profiling, debugging, and optimizing training performance, and improving the scalability and observability of the RL stack.
Skills & qualifications
Skills
Qualifications
Benefits
Full 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.
SR. MODEL ENGINEER, RL TRAINING FRAMEWORK
The RL infrastructure team is looking for an engineer to help develop our RL training framework.
RESPONSIBILITIES:
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Design and implement the systems backing all RL workloads at SpaceXSI, from small scale ablations to production training runs
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Profile, debug, and optimize end-to-end training performance
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Improve scalability and observability of the RL stack
BASIC QUALIFICATIONS:
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Experience writing production-quality code in Python, PyTorch, JAX, Rust, or C++
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Experience training large models (e.g., pre-training, post-training, fine-tuning, or RL training of neural networks with 1B+ parameters)
PREFERRED SKILLS AND EXPERIENCE:
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Experience building, debugging, and optimizing efficiency of large-scale distributed systems
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Experience with large scale LLM training infrastructure
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Experience with RL numerics
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Strong knowledge of reinforcement learning techniques
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Comfortable diving into unfamiliar areas and solving problems at all levels of the stack
COMPENSATION AND BENEFITS:
$180,000 - $400,000 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.
How this job compares
Set against the 6,257 open Machine Learning Engineer jobs on Olive Jobs, counted November 7, 2026.
- Pay
- This posting$180–400K/yr
- California median$180–262K/yr1,445 salary ranges
- US median$150–245K/yr5,095 salary ranges
This range's midpoint, $290K, is 29% above the California median midpoint ($225K) — in the top quarter of Machine Learning Engineer salary ranges in California.
- Openings
- Olive Jobs lists 1,921 open Machine Learning Engineer jobs in California and 6,257 across the US. 9% of them nationwide are listed as remote or flexible.
- Experience
- Machine Learning Engineer postings that state a minimum ask for 5 years of experience at the median; the middle half ask for 3 to 7 years (5,037 postings).
- Education
- 2,109 of the 6,257 open Machine Learning Engineer jobs (34%) ask for a bachelor's degree or higher.
Counted across the open, public US postings on Olive Jobs on November 7, 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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