Model Engineer, Multimodal Understanding
Palo Alto, CAJob$160–250K/yrPosted 1 day agoStill listed 1 day ago
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Job overview
The Model Engineer, Multimodal Understanding joins SpaceXSI’s multimodal team to advance understanding and generation across image, video, audio, and text. The role spans data, model training, infrastructure, evaluation, tooling, and product experiences, and collaborates with research and product teams to deliver multimodal reasoning, world modeling, tool use, agentic behaviors, and interactive human-AI capabilities.
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.
MODEL ENGINEER, MULTIMODAL UNDERSTANDING
You will join the multimodal team to push toward superhuman multimodal intelligence. Advance understanding and generation across modalities—image, video, audio, and text—spanning the full stack: data curation/acquisition, tokenizer training, large-scale pre-training, post-training/alignment, infrastructure/scaling, evaluation, tooling/demos, and end-to-end product experiences.
Collaborate cross-functionally with pre-training, post-training, reasoning, data, applied, and product teams to deliver frontier capabilities in multimodal reasoning, world modeling, tool use, agentic behaviors, and interactive human-AI collaboration. Contribute to building models that can see, hear, reason about, and interact with the world in real time at unprecedented levels.
RESPONSIBILITIES:
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Design, build, and optimize large-scale distributed systems for multimodal pre-training, post-training, inference, data processing, and tokenization at web/petabyte scale
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Develop high-throughput pipelines for data acquisition, preprocessing, filtering, generation, decoding, loading, crawling, visualization, and management (images, videos, audio + text)
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Advance multimodal capabilities including spatial-temporal compression, cross-modal alignment, world modeling, reasoning, emergent abilities, audio/image/video understanding & generation, real-time video processing, and noisy data handling
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Drive data quality and studies: curation (human/synthetic), filtering techniques, analysis, and scalable pipelines to support trillion-parameter models
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Create evaluation frameworks, internal benchmarks, reward models, and metrics that capture real-world usage, failure modes, interactive dynamics, and human-AI synergy
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Innovate on algorithms, modeling approaches, hardware/software/algorithm co-design, and scaling paradigms for state-of-the-art performance
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Build research tooling, user-friendly interfaces, prototypes/demos, full-stack applications, and enable rapid iteration based on feedback
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Work across the stack (pre-training → SFT/RL/post-training) to enable reasoning, tool calling, agentic behaviors, orchestration, and seamless real-time interactions
BASIC QUALIFICATIONS:
- Experience writing production-quality code in Python, PyTorch, JAX, Rust, or C++
PREFERRED SKILLS AND EXPERIENCE:
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Expert-level proficiency in Python (core language), with strong experience in at least one of: JAX / PyTorch / XLA
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Proven track record building or optimizing large-scale distributed ML systems (training/inference optimization, GPU utilization, multi-GPU/TPU setups, hardware co-design)
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Deep experience designing and running data pipelines at scale: curation, filtering, generation, quality studies, especially for noisy/real-world multimodal data
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Strong fundamentals in evaluation design, benchmarks, reward modeling, or RL techniques (particularly for interactive/agentic behaviors)
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Proactive self-starter who thrives in high-intensity environments and is passionate about pushing multimodal AI frontiers
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Willingness to own end-to-end initiatives and do whatever it takes to deliver breakthrough user experiences
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Experience leading major improvements in model capabilities through better data, modeling, algorithms, or scaling
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Familiarity with state-of-the-art in multimodal LLMs, scaling laws, tokenizers, compression techniques, reasoning, or agentic systems
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Proficiency in Rust and/or C++ for performance-critical components
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Hands-on work with large-scale orchestration tools such as Spark, Ray, or Kubernetes
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Background building full-stack tooling: performant interfaces, real-time research demos/apps, or end-to-end product ownership
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Passion for end-to-end user experience in interactive, real-time multimodal AI systems
COMPENSATION AND BENEFITS:
$160,000 - $250,000 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$160–250K/yr
- California median$180–262K/yr1,445 salary ranges
- US median$150–245K/yr5,095 salary ranges
This range's midpoint, $205K, is 9% below the California median midpoint ($225K) — in the middle half 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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