Machine Learning Engineer
2 - 5 years
San Francisco Bay Area
Full-time
Onsite
1 month ago
About the role
About the job
Sonilo is a VC-backed AI music generation company building the next generation of soundtrack creation for video. Founded in Silicon Valley, Sonilo helps creators, brands, and platforms instantly generate original music that understands a video’s content, pacing, and exact duration — eliminating the need for manual editing, stock music searching, or beat matching. From short-form social content to cinematic storytelling, Sonilo enables anyone to turn silent videos into finished content in seconds through AI-powered music generation built for modern creators.
Role Description
As a Machine Learning Engineer, you will combine hands-on engineering with architectural leadership to design and implement reasoning systems, tool orchestration, and multimodal integrations using cutting-edge large language models (LLMs) and diffusion models.
What You'll Do
Develop next-generation AI systems for video-to-music generation and multimodal creative workflows
Build and optimize AI-native product experiences that understand video content, pacing, emotion, and timing to generate high-quality music experiences
Design scalable model architectures and inference pipelines for multimodal generation
Work closely with product, design, and infrastructure teams to bring cutting-edge generative AI capabilities into production
What You Need To Succeed
Master’s or PhD degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related field
Demonstrated experience in generative AI, multimodal systems, LLMs, diffusion models, music/audio generation, or AI agent development
Experience building scalable AI systems, model inference pipelines, or real-time AI applications
Strong foundation in machine learning, software engineering, data structures, and algorithms
Excellent problem-solving and analytical skills, with a proactive and ownership-driven mindset
Ability to move quickly and adapt in a fast-paced startup environment where priorities may evolve rapidly and ambiguity is common
Sonilo is a VC-backed AI music generation company building the next generation of soundtrack creation for video. Founded in Silicon Valley, Sonilo helps creators, brands, and platforms instantly generate original music that understands a video’s content, pacing, and exact duration — eliminating the need for manual editing, stock music searching, or beat matching. From short-form social content to cinematic storytelling, Sonilo enables anyone to turn silent videos into finished content in seconds through AI-powered music generation built for modern creators.
Role Description
As a Machine Learning Engineer, you will combine hands-on engineering with architectural leadership to design and implement reasoning systems, tool orchestration, and multimodal integrations using cutting-edge large language models (LLMs) and diffusion models.
What You'll Do
Develop next-generation AI systems for video-to-music generation and multimodal creative workflows
Build and optimize AI-native product experiences that understand video content, pacing, emotion, and timing to generate high-quality music experiences
Design scalable model architectures and inference pipelines for multimodal generation
Work closely with product, design, and infrastructure teams to bring cutting-edge generative AI capabilities into production
What You Need To Succeed
Master’s or PhD degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related field
Demonstrated experience in generative AI, multimodal systems, LLMs, diffusion models, music/audio generation, or AI agent development
Experience building scalable AI systems, model inference pipelines, or real-time AI applications
Strong foundation in machine learning, software engineering, data structures, and algorithms
Excellent problem-solving and analytical skills, with a proactive and ownership-driven mindset
Ability to move quickly and adapt in a fast-paced startup environment where priorities may evolve rapidly and ambiguity is common