Songscription — Founding Music AI Engineer

Songscription — Founding Music AI Engineer Type: Full-time | On-site | San Francisco, CA Compensation: $175K–$225K + competitive early-stage equity Hiring count: 1 Visa sponsorship: Open to visa transfers (e.g. OPT, H-1B transfers) Reports to: Tim Beyer, Co-founder / Chief Scientific Officer What Our Team Says About This Role Songscription.ai is a music AI lab building novel foundation models that translate audio into sheet music for a global user base of musicians. As a pre-Series A company, the team offers the chance to join as a founding engineer alongside PhD researchers and product experts — a rare opportunity to shape both the culture and the future of consumer AI from the ground floor in San Francisco. About Songscription An AI lab building frontier foundation models for music learning and education, with consumer products that make those models accessible to musicians worldwide. The flagship web app turns an audio file, YouTube link, or live recording into sheet music, MIDI, and piano roll for learning, performing, or creating. Stanford-founded and pre-Series A. Founded: 2024 | Team size: ~5 | Total funding: $5M (seed) Industry: AI, Consumer, Education, EdTech Website: www.songscription.ai Office: San Francisco, CA Why Candidates Should Join Ground-floor founding seat: join as a founding engineer at a pre-Series A AI lab, with significant early-stage equity upside. Work alongside PhDs on novel consumer AI: a close-knit crew of researchers and consumer-product experts building something genuinely new. Shape product and culture: every early hire influences both the product direction and how the team works. Compensation: $175K–$225K + significant early equity. In person in SF. The Role A high-agency founding engineering hire who can take abstract ideas and implement them in a rock-solid way, working closely with the Head of AI to build production-ready ML systems for the next generation of music learning. Prior music-AI experience is not required — the team already has domain experts and is looking for exceptional general technical ability plus a startup mindset. What You'll Be Doing Implementing and shipping ML models for audio and symbolic music understanding into production systems used by real musicians Translating research ideas from the AI team into solid, working engineering implementations Collaborating with data, design, and product teams to integrate models into user-facing products Working with large-scale music datasets: curating, preprocessing, and building data pipelines Contributing to model evaluation, optimization, and deployment for real-world latency and reliability Tech stack: Python, PyTorch / JAX, large-scale ML systems Qualifications Priority legend: Requirement (weighted / critical) · Requirement · Nice to Have · Trait to Avoid. Requirements are the scoring floor; the 0–4 YOE item is the experience Requirement and follows the experience exception (out-of-band = scaled Red Flag, not a rule-out). Seniority 0–4 years of experience in ML engineering, infrastructure, or technically demanding roles Work experience 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 Education BS/MS in CS, EE, or related technical field Hard skills 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 Soft skills Collaborative; works well alongside researchers and other engineers Miscellaneous Based in San Francisco and able to work in-person the majority of the week Traits to Avoid Pure researcher with no engineering output or production code Senior engineer (5+ years) seeking stability over startup risk Role Details 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 Candidate Questions 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. Intake Call Summary 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. Interview Process 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) Ideal Candidate Profiles 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. Rejected Candidate Feedback 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.