Job Description

### About Namecoach

For the past decade, Namecoach has worked with universities, enterprises, and
global platforms to help people pronounce names correctly across languages and
cultures. And we built the largest verified pronunciation dataset of its kind,
combining human recordings, phonetic transcription, and machine learning.

We're now building **[Euphonia](https://euphonia.namecoach.ai/)** , a
pronunciation intelligence platform for voice AI. Voice agents are
proliferating, but they all break on the same problem: names and out-of-
vocabulary words.

We've closed our Series A; a bridge round to Series B is in motion. The voice
AI moment is now, and we're positioned uniquely well for it.

### Why this role, why now

Voice AI deployment is exploding, but every conversational AI product breaks
on the same problem: names and OOV words. A patient relations agent's TTS bot
mispronounces "Atorvastatin." A university enrollment assistant butchers
"Saoirse." Voice agents lose trust the moment they get a name or word wrong,
and the TTS providers building these models can't easily fix it.

We can. Our pronunciation data, verified by humans across languages for over a
decade, is the critical proprietary asset which Euphonia turns into a drop-in
pronunciation layer. The technical challenge is building the AI systems that
turn that data into compounding pronunciation quality — across voice AI
agents, customer integrations, consumer products, and the broader
pronunciation-reliability problem space.

### What you'll do

The work spans the AI side of Namecoach — wherever applied AI, speech AI, and
pronunciation intelligence meet our product surface. The list below is
illustrative. You'd weigh in on priorities with the founder, and the mix will
shift as the company grows.

**Core pronunciation AI for Euphonia.** Build the AI systems that learn from
corrections and improve pronunciation predictions and controls. Concretely:
fine-tune G2P and pronunciation models on our proprietary dataset (millions of
verified audio pronunciations and phonetic spellings); build multi-model AI
validation pipelines that produce confidence scores and model provenance;
design data architecture for verified pronunciation entries that persist and
reuse high-confidence results; build context-aware ranking systems that pick
the right pronunciation given identity, locale, and prior confirmations; and
develop related proprietary work we'll cover in the defense round. This is our
flagship work and where you'd likely start. We'll go deeper in the take-home
and the defense round.

**Voice AI platform integrations.** Build MCP-style integrations and SDKs so
developers building on voice infrastructure and orchestration platforms can
drop in Namecoach pronunciation quality with a few lines of code.

**Enterprise customer work.** Implementation work for current partners and
customers, ranging from the Fortune 500 to famous sports organizations.
Sometimes deeply technical (fine-tuned models on customer-specific name data,
on-prem deployments), sometimes scrappy (one-off audits, hand-transcribing IPA
for a high-stakes customer launch).

**Pronunciation benchmarks.** Voice AI lacks an authoritative measurement
framework for pronunciation reliability across providers, locales, and word
categories. We want our team to define one — see the _Publish and position as
an expert_ section below.

**Reverse-mode pronunciation coaching.** Same underlying tech, applied in
reverse: a human attempts a name, the system gives per-phoneme feedback. Use
cases include ceremony announcing, customer-facing role prep, language
learning, and onboarding.

**Internal AI tooling and agents.** Help Namecoach itself operate as an AI-
native company. We're small; we want someone who builds agents, automations,
and internal tools that multiply everyone's leverage — operational dashboards,
knowledge-graph RAG systems, growth-loop automations, and beyond.

**Consumer product experiments.** We have a small backlog of consumer-facing
ideas (Namecoach for Individuals, name-search Chrome extension, etc.). If you
want to ship a consumer-facing AI product or features for them, there's room.

The 80/20 rule applies throughout: ship product 80% of the time, do targeted
research and writing that builds individual and company expertise the other
20%.

### How we ship

AI-assisted development is our default working style — Claude Code, Cursor,
agent frameworks, and the modern agentic stack are the primary interfaces to
writing code at Namecoach. This applies across everything we ship: internal
tools, POCs, customer demos, and production product features alike. The CEO
ships this way today, and we want you to as well — ideally pushing the team's
leverage further than we currently do.

If you're already shipping with these tools as your primary development
interface, you'll be at home.

### Get close to the data

Pronunciation quality requires actually-close-to-the-data work — sometimes
that means listening to dozens of audio clips, hand-transcribing IPA and
Namecoach-format phonetics, or tracking down why a single customer's name set
is broken. We need someone who finds this clarifying rather than tedious. Some
weeks you'll be deep in customer-specific work; other weeks on core AI
infrastructure. Both matter. People allergic to the messy parts of real-world
data don't tend to succeed here.

### Publish and position as an expert

Pronunciation reliability in voice AI is an emerging measurement problem —
there's no agreed-upon benchmark or widely-used canonical evaluation
framework. We want our team to be the authoritative voice on this, both
because it's good for the field and because it's good for the company.

We'd support you in:

* Publishing technical blog posts on what's under our hood, how baseline systems compare (frontier LLMs and older TTS providers), and what we've learned from production
* Authoring or co-authoring papers — benchmarking studies, applied-ML methods papers, position pieces on pronunciation reliability as a measurement problem
* Speaking at industry conferences and meetups
* Open-sourcing benchmark datasets and evaluation harnesses where it makes strategic sense
* Building public-facing artifacts (leaderboards, drift dashboards) that position you and Namecoach as experts

### Trajectory and growth

For the right candidate, this role offers a potential path to Chief AI Officer
as the company grows. We're equally open to hiring a more senior AI leader
above this role over time, depending on company growth and the right
candidate's interests — and we'd discuss this openly with you as we go. Your
trajectory depends on you, the company's growth, and the shape of our broader
AI organization. We aim to be transparent about all three throughout.

### Who we're looking for

We're more interested in evidence than years. A senior IC at a voice AI
startup, a mid-level engineer with a strong public portfolio, a founding
engineer at a fast-monetizing AI startup, or a scrappy student or recent grad
with serious voice AI side projects — all credible candidates. What we want to
see: code, projects, papers, blog posts, anything that shows you ship and that
you understand voice AI / phonetics / ML in production-relevant ways.

Strong candidates will have most of the following:

* Production-quality experience with applied ML on speech, language, or audio problems (paid work, school projects, open-source contributions, etc.)
* Comfort with grapheme-to-phoneme modeling, IPA or similar phonetic representations, and at least one of: pronunciation modeling, ASR, TTS, or related speech systems
* Hands-on practice with PyTorch, TensorFlow, JAX, or similar modern ML frameworks, and the HuggingFace ecosystem
* Experience with model training pipelines and experiment tracking tools (MLflow, Weights & Biases, or similar)
* Familiarity with ranking, recommendation, or probabilistic inference systems
* Genuine fluency in modern agentic dev tools — Claude Code, Cursor, agent frameworks — as a primary development interface, plus evidence of having built your own internal tooling, agents, or workflows
* Working knowledge of human-in-the-loop ML, active learning, or feedback-driven model improvement
* Ability to explain technical work to non-experts in writing and on screen-share, calibrated to the listener
* A bias toward shipping over perfecting
* Willingness to get hands dirty with raw data — audio, IPA, customer-specific edge cases
* Self-direction — you set the bar for "looking at best practices" without being told

### Bonus experience

* Background in computational linguistics, phonetics, or speech science (academic or industry)
* Public writing, talks, conference papers, or open-source work on speech AI, voice agents, pronunciation, or related
* Multilingual NLP or pronunciation
* Identity resolution, entity matching, or knowledge graphs
* Founding-engineer experience at an early-stage AI company

### Compensation and engagement structure

* **Cash:** $30–120/hr depending on experience, seniority, geographic market, and outcome of a 1-month paid trial. For senior candidates we're explicitly open to structuring more equity in exchange for lower cash, or higher cash with a standard equity grant.
* **Equity:** offered after the trial month, sized to role and conversion. Standard range 0.25–1.0% NSO grant with 4-year vesting and 1-year cliff for the right fit. Larger grants (up to ~1.5%) available for senior candidates who elect a lower-cash structure, or for the right person growing into CAIO.
* **Hours:** initially 20–30 hrs/wk, flexible scheduling around weekly synchronous touchpoints
* **Geography:** open — we work the timezone overlap, you set your hours
* **Engagement form:** 1099 contractor to start; we can discuss other structures if fit warrants

### Our hiring process

1. **30-minute intro call** with the founder
2. **Paid take-home** — build a small pronunciation evaluation system on a held-out dataset we provide. Full brief shared after the intro call. ~4–6 hours of work, $400 flat, paid on submission regardless of outcome.
3. **Defense round** — 45–60 min walkthrough of your take-home where we dig into your choices together
4. **1-month paid trial** — real work on real problems, ~20 hrs/wk, trial hourly rate set during the defense round based on seniority (typically $30–75/hr)
5. **Decision** — at the end of the month, we either offer a continued contract with equity, or part ways with a closing bonus and a referral

We move fast. From first call to trial start: target 2–3 weeks.

### How to apply

If you're applying through a platform, follow that platform's process. Either
way — directly or via platform — please also include:

* A short note (3–5 sentences) on what drew you to this specific role
* A link to one piece of work you're proud of (code, writing, project, paper, demo, anything)
* Optionally: CV or LinkedIn — we'll read them if you send them

Direct applications: email **[hiring+wellfound@name-
coach.com](mailto:[email protected])**.