全职 Machine Learning Engineer 工作, 薪水, 8nabler 公司招聘中 - Ricebowl

Company Description

8nabler is a Malaysia-based AI and software company focused on building production-ready conversational and voice AI systems. The company operates at the intersection of machine learning, real-time systems, and applied AI engineering—turning cutting-edge research into scalable, real-world products.

8nabler specializes in developing end-to-end AI solutions, including speech (TTS), conversational AI, and intelligent automation, with a strong emphasis on performance, latency, and reliability in production environments.

With a builder-first culture, 8nabler prioritizes shipping over theory—working on systems that handle real users, real workloads, and real business impact. The team focuses on solving hard engineering problems across model optimization, infrastructure, and AI deployment.

Role Description

We’re looking for builders—not just model trainers.

This role is about taking Text-to-Speech (TTS) from research to real-world production: fast, reliable, and scalable.

What You'll Do

• Develop our end-to-end TTS stack
• Train & fine-tune neural TTS models (e.g. Kokoro or similar)
• Improve voice quality: prosody, pronunciation, natural flow
• Turn research code into production-grade systems
• Convert models to ONNX and optimize inference performance
• Reduce latency for real-time applications (not just notebook accuracy)
• Work across the full audio pipeline: text → phonemes → spectrogram → waveform
• Build APIs/services that perform under real load

This is not a “train a model and stop” role.

You’ll make it fast, stable, and production-ready.

Who we are looking for

We value ability over years of experience.

• Strong Python fundamentals
• Experience with PyTorch (projects, internships, or self-built work)
• Experience with vLLM and Hugging Face transformers
• You’ve trained something (NLP, CV, audio, etc.)
• Comfortable learning from papers/repos and making them work
• Care about performance: speed, memory, efficiency

Bonus Points

• TTS / speech / audio processing experience
• ONNX, TensorRT, or model optimization
• Quantization, pruning, inference tuning
• Real-time or streaming systems
• Multilingual data (especially ASEAN languages)

What You'll Learn (Fast)

• Turning ML models into low-latency production systems
• Optimizing beyond “it works”
• Building real-world voice systems end-to-end
• Balancing quality vs speed vs cost

Environment

• Fast-moving, low bureaucracy
• High ownership (you build it, you own it)
• No hand-holding—but steep learning
• Focused on shipping, not slides

How to Stand Out

Skip the generic CV. Show us:

• GitHub projects
• Models you’ve trained or deployed
• Audio / ML experiments
• Systems you’ve optimized

If you can prove you can build, we don’t care if you’re junior.

• Apply or DM with your work