Machine Learning Engineer

JOB TITLE

Senior Applied Machine Learning Engineer (audio / music generation)

ABOUT THE ROLE

We’re building an AI-powered music system focused on commercial-ready audio generation. Our initial priority is getting the music generation quality right - structure, musicality, consistency, and production readiness.

We are looking for a Senior Applied ML Engineer to own the end-to-end audio generation pipeline for our MVP. This role is hands-on and pragmatic: you’ll fine-tune open-source music models, integrate inference pipelines, and work closely with audio and backend engineers to deliver usable results quickly and efficiently. This role starts as a contract engagement (details below), with a path to full-time position for the right fit.

ROLE DETAIL

• Terms: Fixed-term (5 months) | Potential full-time conversion
• Compensation:  $30,000 (Full 5 Month Term)
• Location: Hybrid/On-site (Monrovia, CA)

WHAT YOU’LL WORK ON

• Fine-tuning open-source music generation models.
• Implement conditioning controls (beats per minute, key, mood, section, density).
• Training and deploying parameter-efficient fine-tunes (LoRA / adapters).
• Building reference-conditioned generation.
• Support long-form generation via chunking and continuation.
• Integrating with Backend inference pipelines and APIs.
• Collaborating with audio DSP engineers to ensure outputs are production ready.

REQUIRED QUALIFICATIONS

• Strong experience with Python and PyTorch.
• Hands-on experience with audio or speech generation models.
• Familiarity with diffusion or autoregressive generative models.
• Experience using or fine-tuning open-source ML models, familiar with HF Interfaces.
• Understanding of audio representations.
• Experience deploying ML models to production or API environments.

NICE-TO-HAVE SKILLS

• Familiarity with CLAP / audio embeddings or retrieval-assisted generation.
• Experience working with LoRA / PEFT methods.
• Basic understanding of audio production workflows (tempo, key, stems, loudness).
• Experience Optimizing inference cost and latency.

ROLE GOALS & OBJECTIVES

• Reliably generate musically coherent, commercial-friendly cues (30 ~ 120 seconds)
• The model responds correctly to conditioning inputs like tempo, key and mood
• Outputs are stable, repeatable and usable downstream by post-production tools
• The system is modular and ready to be integrated with downstream models.