About the job
About Us
At Speak, we are on a mission to revolutionize language learning.
Learning a language can be transformative, offering access to new cultures, career opportunities, and community connections. With two billion individuals worldwide actively pursuing language acquisition, traditional one-on-one tutoring remains difficult to access at scale and has not significantly evolved in decades. Speak is creating a human-level, AI-driven tutor that fits in your pocket—a conversation-first platform that enables learners to practice speaking, receive immediate feedback, and navigate through intricately designed lessons. This innovative approach ensures a comprehensive journey from novice to confident speaker across various languages.
Launched in South Korea in 2019, Speak has quickly ascended to become the premier language learning app, now catering to users in numerous markets and over 15 languages. As one of the leading AI companies globally, we have secured over $150 million in venture funding from distinguished investors including OpenAI, Accel, Founders Fund, and Khosla Ventures. Our diverse team operates across San Francisco, Seoul, Tokyo, Taipei, and Ljubljana.
About This Role
We are seeking an experienced ML Manager to spearhead our machine learning team, which currently consists of four specialists focused on voice ML and broader ML systems. Your role will be pivotal in enhancing execution, measurement, and strategic direction.
This is a hands-on management position where you will directly supervise the team, collaborate closely with Andrew to establish goals and ambitions, and ensure we continuously implement ML improvements that positively influence product metrics. You will also play a crucial role in refining our strategy as new modeling techniques emerge (such as speech-to-speech), while keeping the team focused on high-impact initiatives.
Your Responsibilities
Foster and develop the ML team by setting clear expectations, providing regular feedback, and offering strong coaching.
Set quarterly ML objectives and success metrics, establish baselines, and create a visible scoreboard to track progress.
Oversee the full lifecycle of key ML initiatives: planning, evaluation, iteration, rollout, and impact assessment.
Develop a repeatable model evaluation and experimentation framework (quality, performance, reliability), including rollout criteria.
Collaborate with Product and Engineering leaders to align project roadmaps, clarify responsibilities, and ensure ML deliverables are on schedule and predictable.
Identify weaknesses in our existing ML strategy, propose actionable initiatives, and convert strategic direction into implementable plans.