Speech / Applied ML Engineer

VALSEA; Singapore; On-site; SGD 120,000 - 160,000; Full time

Job summary

VALSEA is seeking an applied ML engineer to own end-to-end improvements in speech and ASR under real production constraints in Singapore. You will push models from experiments into deployed systems, focusing on multilingual and code-switching scenarios with diverse data and latency/cost limits.

You will collaborate with engineers to optimize inference, memory usage, and GPU utilization while building robust evaluation suites and documenting tradeoffs for sustainability and impact.

Qualifications

• Solid Python and PyTorch fundamentals.
• Understanding of speech and ASR basics.
• Experience with model training, fine-tuning, and evaluation.
• Familiarity with GPU inference and optimisation workflows.
• Practical ML engineering mindset, not just theory.

Responsibilities

• Experiment with and tune speech/ASR models for SEA languages and accents.
• Design and run experiments under realistic production constraints (latency, cost, memory).
• Work on inference optimisation and GPU utilisation.
• Develop strategies for multilingual and code-switching scenarios.
• Collaborate with engineering to integrate models into production pipelines.
• Build evaluation suites and datasets for tracking model performance.
• Document approaches, experiments, and tradeoffs.

Skills

Python

PyTorch

Speech/ASR

GPU inference

Production ML

Latency optimization

English communication

Education

Bachelor's degree in CS/SE

Tools

Git

Docker

CUDA

Kubernetes

Job description

About The Role

This is a high-ownership applied ML role focused on speech in real production constraints. You will improve SEA speech performance across languages, accents, code-switching, and noisy audio while working under real latency, cost, and reliability requirements. You will be trusted with production-impacting changes and expected to operate with maturity, initiative, and speed.

What This Role Is Really About

You are not here to only run notebooks.

You are here to:

• Take ownership of model and pipeline improvements that move core speech metrics.


• Move from experiments to deployed improvements without being micromanaged.


• Identify failure modes and edge cases in real-world speech data.


• Ship models, features, or tuning that measurably improve accuracy, robustness, or latency.


• Think beyond BLEU/WER and understand customer and business impact.

You should be comfortable where:

• Requirements and evaluation criteria evolve.


• Data is messy, multi-lingual, and imperfect.


• Speed matters, but quality and safety matter too.


• You must make decisions with incomplete labels and signals.

Responsibilities

• Experiment with and tune speech/ASR models for SEA languages and accents.


• Design and run experiments under realistic production constraints (latency, cost, memory).


• Work on inference optimisation and GPU utilisation.


• Develop strategies for multilingual and code-switching scenarios.


• Collaborate with engineering to integrate models into production pipelines.


• Build evaluation suites and datasets for tracking model performance.


• Document approaches, experiments, and tradeoffs.

What We Expect From You

• Founding Mindset
  
  
  
  • You think in terms of shipped improvements, not just paper metrics.
  
  
  • You ask \"how will this behave in production?\" before trying a new approach.
  
  
  • You act like speech quality is your responsibility.
  
  
  • You balance research depth with shipping velocity.
  
  
  • You don\'t wait for others to point out model failures; you go find them.
  
  
  
  


• Maturity
  
  
  
  • You communicate clearly about what is known, unknown, and risky.
  
  
  • You admit when an experiment failed and extract learning.
  
  
  • You take feedback from both researchers and engineers without ego.
  
  
  • You stay calm under pressure when a model behaves unexpectedly in production.
  
  
  • You follow through on investigations into failure modes.
  
  
  
  


• Initiative
  
  
  
  • You propose new hypotheses, architectures, or data strategies.
  
  
  • You investigate root causes behind model errors instead of just tweaking hyperparameters.
  
  
  • You improve evaluation pipelines and diagnostics.
  
  
  • You refine data curation and annotation processes.
  
  
  • You continuously balance performance and cost optimisations.
  
  
  
  


• ML / Speech Competence
  
  
  
  • Solid Python and PyTorch fundamentals.
  
  
  • Understanding of speech and ASR basics.
  
  
  • Experience with model training, fine-tuning, and evaluation.
  
  
  • Familiarity with GPU inference and optimisation workflows.
  
  
  • Practical ML engineering mindset, not just theory.
  
  
  
  

Bonus

• Experience with multilingual or low-resource speech.


• Exposure to on-device or low-latency inference.


• Experience shipping ML models into production systems.

What Success Looks Like

• You own improvements to a specific speech use case or language.


• You ship at least one measurable improvement in accuracy, robustness, or latency.


• You identify and document notable failure modes and mitigation strategies.


• You contribute to model evaluation and monitoring infrastructure.

What You Gain

• Real-world applied ML experience under production constraints.


• Direct collaboration with founders and senior engineers.


• A portfolio of experiments and shipped improvements in production.


• A path towards an applied ML or speech-focused engineering role.

Who Should Not Apply

• If you only want to work on toy datasets and offline benchmarks.


• If you avoid messy data and hard debugging.


• If you prefer purely research environments detached from production.


• If you are looking for a low-intensity internship.

Who Will Thrive Here

• Builders who love shipping ML to production.


• Systems thinkers who see the whole pipeline, not just the model.


• Calm debuggers of strange model behaviour.


• High-agency individuals who care about real-world impact.