Machine Learning Engineer, Voice
Speak is hiring a Machine Learning Engineer, Voice for a remote role open to applicants in United States. It pays $200k-$300k a year and Level rates it ; you can apply on Speak.
Level's read on this job
- Level rates this machine learning engineer role Builds AI ●●●●: the job is building AI systems.
- Pay on this posting starts in line with the typical machine learning engineer posting on Level.
- This is listed as a remote role.
- The posting mentions OpenAI, PyTorch and speech among its tools and skills.
- Speak has 32 other open roles on Level.
AI in this role
Our mission is to reinvent the way people learn, starting with language.
Learning a language can change a life by opening doors to new cultures, careers, and communities. Two billion people around the world are actively trying to learn a language, but the best way to learn (one-on-one tutoring) is hard to access at scale and hasn’t been meaningfully improved in decades. Speak is building a human-level, AI-powered tutor in your pocket: a conversation-first experience that lets learners actually speak, get instant feedback, and progress through carefully designed lessons. The result is a complete path from beginner to confident speaker across multiple languages.
Speak first launched in South Korea in 2019, where Speak has now become the number one language learning app, and we now serve learners across many markets and 15+ languages. Speak is one of the world’s leading AI companies, with over $150m raised in venture investment from OpenAI, Accel, Founders Fund, Khosla Ventures, and more, with a distributed team across San Francisco, Seoul, Tokyo, Taipei, and Ljubljana.
About this role
We are looking for an experienced Machine Learning Engineer to join our team and help develop cutting-edge speech recognition models that help teach language fluency. In this role you will take ownership of the end-to-end modeling pipeline, from training and experimentation to deployment and monitoring. You will also work closely with Product teams to design innovative learning experiences and measure the efficacy of production models as they affect our end users. We are a small, dynamic team where you will contribute as a developer and thought partner on team projects like ASR, assessment, pronunciation, content personalization, and much more. This is an incredibly exciting time to join an ML team designing a personalized learning experience that will revolutionize language learning for millions of learners worldwide — come join us!
What you'll be doing
Training and deploying ASR models end-to-end, including monitoring, performance tracking, and retraining
Improving the pronunciation model that provides precise feedback, and make it more central to our learning app
Creating metrics to measure ASR performance across tasks and languages
Expanding our ASR systems to new languages and markets
Building and maintaining data infrastructure such as training/evaluation datasets and labeling pipelines
What we're looking for
Extensive experience training large models on GPUs and deploying custom deep learning models
Proficiency in Python and common Deep Learning frameworks like PyTorch
Demonstrated experience owning ML pipelines end to end, from POC to production
Strong communication skills and the ability to explain complex ML concepts to non-technical stakeholders
Sharp product sense and an ability to think broadly and cross-functionally about model quality in the context of user experience
Bonus
Experience with speech or audio
Office
San Francisco, CA
Join a fantastic, tight-knit team at the right time: we're growing very quickly, we've most recently raised our Series C from some of the top investors in the valley, and we've achieved product-market fit in our initial markets. You'd join at a magical time when a single person could significantly change the course of the company.
Do your life's work with people you’ll love working with: we care strongly about our craft and want every person at Speak to feel like they're growing every day. We believe in the idea that working with people you both enjoy and have respect for makes everything better. We hire thoughtfully and only work with people we admire deeply.
Global in nature: We're live in over 40 countries and launching in a number of new markets soon. We have dedicated offices in San Francisco, Ljubljana, Seoul, Tokyo, and Taipei, and you’ll have the opportunity to talk to users in each of these regions on a regular basis as well as travel.
Impact people's lives in a major way: Learning a language is one of the single most life-changing skills one can learn, and right now 99% of people never achieve their goal because the process is broken. We’re helping millions of people achieve their goals and improve their lives.
Speak does not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.
How we rate this
Machine Learning Engineer, Voice at Speak rates 87 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in machine learning engineer jobs at this level
Pay
- This job starts at
- $200k
- Typical machine learning engineer job
- $199k
This posting starts close to the typical machine learning engineer job. See machine learning engineer jobs
Questions you could be asked
- What have you built with speech recognition or text-to-speech, and where did it break?
- Walk me through how you've used OpenAI in your day-to-day work.
- What are the limits of PyTorch that you've run into, and how did you work around them?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: Speech, OpenAI, and PyTorch. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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