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.