Architect and manage horizontally scalable systems that dramatically accelerate the end-to-end training lifecycle for Speech-to-Text (STT) and Text-to-Speech (TTS) models.
Design and implement internal UIs and tools that make ML systems and workflows accessible to non-technical stakeholders across the company.
Oversee and manage training tooling, job orchestration, experiment tracking, and data storage.

Requirements

Strong experience with the machine learning research pipeline, particularly in STT or related speech domains. This includes experimenting with and evaluating new architectures and modeling approaches, and implementing large-scale training systems.
Proficiency with orchestration and infrastructure tools like Kubernetes, Docker, and Prefect.
Familiarity with ML lifecycle tools such as MLflow.
Experience building internal tools or dashboards for non-technical users.
Hands-on experience with data engineering practices for unstructured audio and text data.
Comfortable working in cross-functional teams that include researchers, engineers, and product stakeholders.

Benefits

Medical, dental, vision benefits
Annual wellness stipend
Mental health support
Life, STD, LTD Income Insurance Plans
Unlimited PTO
Generous paid parental leave
Flexible schedule
12 Paid US company holidays
Quarterly personal productivity stipend
One-time stipend for home office upgrades
401(k) plan with company match
Tax Savings Programs
Learning / Education stipend
Participation in talks and conferences
Employee Resource Groups
AI enablement workshops / sessions

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningSpeech-to-TextText-to-Speechdata engineeringlarge-scale training systemsexperiment trackingmodeling approachesorchestrationinfrastructure

Soft Skills

cross-functional collaborationcommunicationstakeholder engagement