Songscription — Founding Music AI Engineer

• 0–4 years of experience in ML engineering, infrastructure, or technically demanding roles

• Internship or role at a technically demanding company (e.g., big tech infra, quant, or high-growth startup)
• Experience building and training ML models (production systems or substantial projects)
• Startup or early-stage company experience
• Experience in audio, sequence, or generative model domains

• BS/MS in CS, EE, or related technical field

• Strong software engineering and coding ability
• Experience with ML frameworks (PyTorch, JAX, etc.)
• Ability to translate ML research ideas into solid, working implementations
• Data pipeline and large-scale dataset experience

• Collaborative; works well alongside researchers and other engineers

• Based in San Francisco and able to work in-person the majority of the week

• Pure researcher with no engineering output or production code
• Senior engineer (5+ years) seeking stability over startup risk

• Salary — $175K–$225K
• Equity — Competitive early-stage equity
• On-site policy — In-office in San Francisco; in person the majority of the week
• Visa sponsorship — Open to visa transfers (e.g. OPT, H-1B transfers)
• Employment type — Full-time
• Hiring count — 1
• Location — San Francisco, CA

The role page lists 6 candidate questions, but they were collapsed in the copied page and did not come through. Re-copy with the "View 6 questions" section expanded and I'll insert them here.

• Company positions itself as a music AI lab integrating AI into music education; transcription tech lets users learn any song they love.
• Intake covered two roles (Founding Full Stack Engineer and Founding Music AI Engineer) — this JD scopes to the Music AI role only.
• For the Music AI role: emphasis on engineering skill over a music-specific background; wants someone who implements ideas effectively, not necessarily a PhD.
• Experience band for the AI role: 0–4 years acceptable, with a preference for younger, startup-inclined candidates.
• Culture: close-knit, high-growth, collaborative; founders work long hours and expect high ownership.
• Compensation: equity is a significant component, with salary flexibility for the right candidate. Onsite in SF preferred, flexible hours but high dedication expected.
• Urgency: role is urgent, prefers candidates who can start ASAP.
• Pain point: team is stretched thin amid product expansion and needs to scale; wants people who can take abstract ideas and implement them independently.

Page lists 6 steps but the detailed breakdown was collapsed in the copied page. Reconstructed from the intake call — re-copy with the interview-process section expanded to confirm exact stages/durations.

• Intro call(s)
• Technical assessment (take-home or live)
• Product interview (probing product intuition)
• Work trial
• (Two additional stages implied by the "6 steps" label — confirm)

For reference only — do not source these specific profiles.

Maximiliano L. — LinkedIn Inference at ElevenLabs | United Kingdom

• Clear evidence of strong technical ability; would have been a great fit before joining ElevenLabs.

Ole Petersen — LinkedIn Engineering @ Listen (ListenLabs) | San Francisco

• Combines a research background with software engineering experience; strong audio/ML project experience at a high-growth startup.

Amine Ketata — LinkedIn ML PhD Student @ TU Munich | Munich, Germany

• Profile before starting the PhD was a great fit; flagged by the hiring manager as an ideal profile, especially pre-PhD.

• Prioritize candidates with clear production engineering output over research-heavy backgrounds — need evidence of end-to-end ML system deployment, not just prototypes or academic papers.
• Focus on startup mindset and ownership — avoid overly senior profiles or people from large-corp environments who lack the agility for a high-agency, early-stage startup.
• Ensure verifiable on-site availability in SF — candidates must be ready to work in person and show immediate transition readiness, including relocation if needed.