Machine Learning Engineer

JOB TITLESenior Applied Machine Learning Engineer (audio / music generation)ABOUT THE ROLEWe’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 DETAILTerms: Fixed-term (5 months) | Potential full-time conversionCompensation: $30,000 (Full 5 Month Term)Location: On-site (Monrovia, CA) with possibility of remoteWHAT YOU’LL WORK ONFine-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 QUALIFICATIONSStrong 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 SKILLSFamiliarity 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 & OBJECTIVESReliably generate musically coherent, commercial-friendly cues (30 ~ 120 seconds)The model responds correctly to conditioning inputs like tempo, key and moodOutputs are stable, repeatable and usable downstream by post-production toolsThe system is modular and ready to be integrated with downstream models.