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Apprentice Spatial Audio Algorithms Jobs (NOW HIRING)

Senior Software Engineer, Google Beam

Mountain View, CA · On-site

$144.50K - $190.50K/yr

... algorithms. * 1 year of experience in a technical leadership role. * Experience developing ... Powered by realistic 3D imaging and spatial audio and integrated with today's leading remote video ...

Senior Experience Designer

Atlanta, GA · On-site

$98.10K - $104.80K/yr

... ML, algorithms, digital signal processing, audio engineering, image processing, computer vision ... Experience with immersive or spatial media, such as immersive audio, immersive video, or ...

Design and implement scalable machine learning pipelines for large-scale 3D spatial data processing ... Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and ...

Design and implement scalable machine learning pipelines for large-scale 3D spatial data processing ... Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and ...

Design and implement scalable machine learning pipelines for large-scale 3D spatial data processing ... Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and ...

... audio, tactile, spatial and temporal understanding powered by physical AI. You will develop ... Design and implement simulation environments and evaluation frameworks for algorithm validation.

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How much do apprentice spatial audio algorithms jobs pay per hour?

As of Jun 1, 2026, the average hourly pay for apprentice spatial audio algorithms in the United States is $22.81, according to ZipRecruiter salary data. Most workers in this role earn between $18.27 and $25.24 per hour, depending on experience, location, and employer.
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Member of Technical Staff - Multimodal Understanding

xAI

Palo Alto, CA • On-site

Full-time

Posted 14 days ago


Job description

Job Summary:
xAI is dedicated to creating AI systems that enhance human understanding of the universe. The role involves collaborating with the multimodal team to develop advanced capabilities in multimodal reasoning and real-time interactions across various data types, including image, video, audio, and text.
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.
Qualifications:
Required:
• Hands-on experience with multimodal pre-training, post-training, or fine-tuning (vision, audio, video, or cross-modal).
• Expert-level proficiency in Python (core language), with strong experience in at least one of: JAX / PyTorch / XLA.
• Proven track record building or optimizing large-scale distributed ML systems (training/inference optimization, GPU utilization, multi-GPU/TPU setups, hardware co-design).
• Deep experience designing and running data pipelines at scale: curation, filtering, generation, quality studies, especially for noisy/real-world multimodal data.
• Strong fundamentals in evaluation design, benchmarks, reward modeling, or RL techniques (particularly for interactive/agentic behaviors).
• Proactive self-starter who thrives in high-intensity environments and is passionate about pushing multimodal AI frontiers.
• Willingness to own end-to-end initiatives and do whatever it takes to deliver breakthrough user experiences.
Preferred:
• Experience leading major improvements in model capabilities through better data, modeling, algorithms, or scaling.
• Familiarity with state-of-the-art in multimodal LLMs, scaling laws, tokenizers, compression techniques, reasoning, or agentic systems.
• Proficiency in Rust and/or C++ for performance-critical components.
• Hands-on work with large-scale orchestration tools such as Spark, Ray, or Kubernetes.
• Background building full-stack tooling: performant interfaces, real-time research demos/apps, or end-to-end product ownership.
• Passion for end-to-end user experience in interactive, real-time multimodal AI systems.
Company:
XAI is an artificial intelligence startup that develops AI solutions and tools to enhance reasoning and search capabilities. It is a sub-organization of SpaceX. Founded in 2023, the company is headquartered in Palo Alto, USA, with a team of 1001-5000 employees. The company is currently Late Stage.