Speech / Applied ML Engineer at VALSEA

Singapore, , Singapore -

Full Time

Start Date

Immediate

Expiry Date

20 Sep, 26

Salary

0.0

Posted On

22 Jun, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, PyTorch, Automatic Speech Recognition, Model Fine-tuning, GPU Optimization, Inference Optimization, Multilingual ML, Data Curation, Model Evaluation, MLOps, Speech Processing, Applied Machine Learning

Industry

technology;Information and Internet

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.

Responsibilities

Improve speech performance for Southeast Asian languages by tuning ASR models and optimizing inference for production constraints. Design evaluation suites and collaborate with engineering to integrate models into production pipelines.