Speech / Applied ML Engineer

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.