Multimodal AI Systems Engineer

Maven Ventures; Palo Alto (CA); On-site; USD 160,000 - 250,000; Full time

Benefits offered by this job

Equity

Medical coverage

401(k) retirement plan

Dental coverage

Job summary

SpaceXSI in Palo Alto is seeking a Model Engineer for Multimodal Understanding to advance cross-modal capabilities and build large-scale distributed systems for pre-training, post-training, and inference. You will collaborate with cross-functional teams across data, training, and product to push multimodal AI toward real-world impact.

The role emphasizes end-to-end ownership, data pipelines, evaluation, and tooling, with opportunities to influence model architectures, tokenizer training, and

Qualifications

• Production-quality Python/C++/Rust code experience.
• Experience with ML model training and deployment.
• Experience with multimodal data is a plus.

Responsibilities

• Design, build, and optimize large-scale distributed systems for multimodal pre-training, post-training, inference, data processing, and tokenization at web/petabyte scale
• Develop high-throughput pipelines for data acquisition, preprocessing, filtering, generation, decoding, loading, crawling, visualization, and management (images, videos, audio + text)
• 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
• Drive data quality and studies: curation (human/synthetic), filtering techniques, analysis, and scalable pipelines to support trillion-parameter models
• Create evaluation frameworks, internal benchmarks, reward models, and metrics that capture real-world usage, failure modes, interactive dynamics, and human-AI synergy
• Innovate on algorithms, modeling approaches, hardware/software/algorithm co-design, and scaling paradigms for state-of-the-art performance
• Build research tooling, user-friendly interfaces, prototypes/demos, full-stack applications, and enable rapid iteration based on feedback
• Work across the stack (pre-training → SFT/RL/post-training) to enable reasoning, tool calling, agentic behaviors, orchestration, and seamless real-time interactions

Skills

Python

PyTorch

JAX

Rust

C++

Distributed systems

Tools

Spark

Ray

Kubernetes

Job description

SpaceXSI in Palo Alto is seeking a Model Engineer for Multimodal Understanding to advance cross-modal capabilities and build large-scale distributed systems for pre-training, post-training, and inference. You will collaborate with cross-functional teams across data, training, and product to push multimodal AI toward real-world impact.

The role emphasizes end-to-end ownership, data pipelines, evaluation, and tooling, with opportunities to influence model architectures, tokenizer training, and