Job Description

Python ML Engineer (TTS / ONNX / Real-Time Audio)

Build voice AI that actually ships.

What you’ll be doing

You’ll work with our team to develop our Text-to-Speech (TTS) stack end-to-end.

• Train & fine-tune neural TTS models (Kokoro or similar)
• Make voices sound human — prosody, pronunciation, flow
• Turn research models into production systems
• Convert models to ONNX and squeeze every ms out of inference
• Optimize for real-time latency (not just accuracy on a notebook)
• Work on audio pipelines: text normalization → phonemes → spectrogram → waveform
• Ship APIs/services that run reliably under load

This is not a “train a model and call it a day” role.
You’ll be expected to make it fast, stable, and production-ready.

Job Requirements

Our Ideal Candidate

We prioritize skills over experience.

• You write proficient Python
• You have experience with PyTorch (including projects, internships, and personal ventures)
• You’ve worked on training models (whether in NLP, computer vision, or audio—TTS experience is not mandatory)
• You enjoy solving problems based on research papers or repositories and implementing them
• You focus on performance (including speed, memory usage, and efficiency)

You could be:

• A junior engineer who actively contributes to building
• A self-taught machine learning enthusiast with tangible projects
• Someone looking to move beyond theory and develop real applications

Additional advantages if you have experience with

• Text-to-Speech (TTS), speech models, or audio processing
• ONNX, TensorRT, or model optimization techniques
• Methods like quantization, pruning, or inference tuning
• Real-time systems or streaming applications
• Data in multiple languages (especially from ASEAN regions)

What you’ll quickly learn

• How to convert machine learning models into low-latency production systems
• Ways to enhance models beyond a basic level of functionality
• The end-to-end process of constructing real-world voice systems
• How to find a balance between quality, speed, and cost

Work Environment

• Dynamic and fast-paced, without bureaucratic hurdles
• A high degree of ownership (you create it, you own it)
• Independence in your work—ample opportunity for genuine learning
• Emphasis on delivering results, not just presentations

How to Make a Strong Impression

Move beyond the generic resume. Demonstrate:

• Your GitHub projects
• Any models you've trained or deployed
• Audio or machine learning experiments you've conducted
• Systems that you’ve improved

If you can demonstrate your building capabilities, we don’t mind if you’re at a junior level.

Skills