We are looking for an ML Tech Lead to own Cotomo’s ML stack end-to-end — from experimentation and post-training to evaluation, inference, deployment, and infrastructure.
Our product uses a broad range of models across LLMs, ASR, TTS, image and video generation, and other multimodal systems. You will work across these technologies as the product evolves, rather than being tied to a single model or modality.
This is primarily an individual contributor role, not a traditional management position. You will spend most of your time building, experimenting, debugging, and shipping yourself, while also serving as the technical lead for our small ML team.
This is also not a pure research role. Our goal is to build the best possible product, not to build models in-house for their own sake.
Depending on the problem, the best solution may be an internally trained model, an open-weight model, a frontier API, or a combination of them. We expect you to make those decisions based on actual product quality, latency, reliability, and cost.
Responsibilities
- Own Cotomo’s ML stack end-to-end across language, speech, image, video, and multimodal models
- Improve conversational quality, personality, memory, personalization, latency, reliability, and cost
- Build and improve training, post-training, evaluation, and experimentation pipelines
- Evaluate and A/B test frontier APIs, open-weight models, and in-house models, making pragmatic decisions based on product quality and unit economics
- Own production ML systems, including model serving, GPU infrastructure, observability, and cost optimization
- Work closely with Product and Engineering to turn new model capabilities into measurable user impact
- Provide technical direction, reviews, and hands-on support for our small ML team
Requirements
- 4+ years of full-time professional experience in machine learning, AI, or a closely related engineering role
- You’ve built and operated production ML systems and are comfortable owning them beyond the experimentation stage
- You have a strong understanding of modern deep learning and LLM systems across training, fine-tuning, evaluation, and inference
- Strong software engineering skills come naturally to you, including working with GPU-based ML infrastructure
- Ambiguous technical problems don’t need to be fully scoped before you start — you can define the problem, make trade-offs, and drive it through to production
- You think in terms of product outcomes, not just model benchmarks
- You’re pragmatic about architecture and are equally comfortable choosing an external API, an open-weight model, or an in-house solution when it is the best fit
- You can set technical direction and raise the bar for a small team while remaining primarily a hands-on individual contributor
Nice to haves
While not specifically required, tell us if you have any of the following.
- Conversational AI, AI companions, character AI, voice AI, or other consumer AI products
- ASR, TTS, image generation, video generation, or other multimodal systems
- Post-training, preference optimization, reinforcement learning, synthetic data, or personalization
- Distributed training or large-scale inference systems
- Latency, reliability, and cost optimization for production ML
- Staff, Principal, Lead, or senior Research/ML Engineer-level technical ownership
Compensation
¥9,000,000 ~ annually.
With performance-based stock options.