Speech Machine Learning Engineer

SoundNativ; Colombia; Remote; COP 90,000,000 - 150,000,000; Full time

Benefits offered by this job

Competitive pay

Remote-friendly

Flexible hours

Ownership from week one

Tools provided

Job summary

SoundNativ, a Colombia-based talent, is hiring to own phoneme recognition, pronunciation scoring and accent analysis pipelines from training to production. You will deploy models behind a Python/FastAPI scoring service for real-time results, and build robust data pipelines for audio and text.

We value a strong foundation in ML, production readiness and a user-centered mindset to avoid unfair scoring for speakers of diverse languages.

Qualifications

• 3+ years of training, evaluating and deploying ML models in production, ideally in speech or audio.
• Fluent in Python and PyTorch.
• Experience with ASR, forced alignment or phoneme recognition.
• Comfortable owning infrastructure: GPUs, inference latency and cost.

Responsibilities

• Training and fine-tuning speech models (wav2vec 2.0, Whisper-style encoders and friends) for phoneme-level scoring and accent detection.
• Deploying them behind our Python and FastAPI scoring service for real-time and batch inference.
• Building evaluation sets that cover the first languages our learners speak, and tracking how scores hold up across them.
• Data pipelines for audio and text: cleaning, labelling, augmentation and versioning.
• Exploring where LLMs and speech-to-speech models can coach, not just score.

Skills

Python

PyTorch

ML in production

Speech processing

Tools

wav2vec 2.0

Whisper-style encoders

GPUs

Inference latency

Job description

When a learner says a word in SoundNativ, we tell them which sound was off and how to fix it, in about a second. That feedback is the product. If it is wrong, nothing else matters.

You will own the models and pipelines behind it: phoneme recognition, pronunciation scoring and accent analysis, from training run to production endpoint.

What you'll work on

• Training and fine-tuning speech models (wav2vec 2.0, Whisper-style encoders and friends) for phoneme-level scoring and accent detection.
• Deploying them behind our Python and FastAPI scoring service for real-time and batch inference.
• Building evaluation sets that cover the first languages our learners speak, and tracking how scores hold up across them.
• Data pipelines for audio and text: cleaning, labelling, augmentation and versioning.
• Exploring where LLMs and speech-to-speech models can coach, not just score.

Who you are

• Strong foundations in machine learning, statistics and signal processing, and pride in models that make it to production.
• Equal parts creative and methodical. You try the proven and the unproven, and you measure both.
• You care about the person on the other end. A score that feels unfair to a Korean speaker is a bug.
• You like long stretches of focused work without meetings, and you don't wait for permission.

Requirements

• 3+ years training, evaluating and deploying ML models in production, ideally in speech or audio.
• Fluent in Python and PyTorch.
• Experience with ASR, forced alignment or phoneme recognition.
• Comfortable owning infrastructure: GPUs, inference latency and cost.

Nice to have

• Background in phonetics, or a good ear for accents.
• Published research or open-source work in speech.
• Experience optimising models for low-latency or on-device inference.

What we offer

• Competitive pay, based on your experience.
• Work from anywhere.
• Flexible hours. We care about what you ship, not when you are online.
• Real ownership from week one, and a direct line to the founders.
• The AI tools you need to move fast, paid for.