About the company
We thrive in the technology industry by using AI and voice analysis to connect diverse talent with opportunities. Our innovative approach saves time and costs, fostering a dynamic work environment where unique perspectives are celebrated and growth flourishes.
Job description
What we need you to tackle
We are looking for a high-ownership ML engineer who can make a production voice model more reliable, measurable, and easier to evolve with limited hand-holding. Strong candidates can explain what they personally built, what tradeoffs they made, what broke, and how they measured whether the model actually improved. They should be comfortable across training data pipelines, audio and speech processing, model training and evaluation, inference, and production debugging. They should use AI aggressively as leverage for prototyping, data exploration, and debugging, but not blindly trust its output. Weak fit: someone who needs clean labeled data handed over, optimizes metrics without questioning the label, works only in research settings, or needs fully defined specs to make progress.
Non-negotiable hard skills
- Data Modeling
- Data Science
- Machine Learning
- Python
- Data Engineering
Your day-to-day
Own the training data pipeline end to end: collection, validation, labeling, versioning, and dataset creation.
Debug the full voice pipeline, from speech-to-text output through audio preprocessing, diarization, feature extraction, and final model output.
Run experiments comparing new models against production and decide what ships based on measured accuracy, latency, and failure modes.
Translate ambiguous questions from founders, product, or Ops into small executable experiments.
Simplify the ML infrastructure so training and inference are easier to trace, debug, and maintain.
Nice to haves
- Use SQL to discover data availability, cohort differences, label delays, and issues that affect training and evaluation readiness.
- Design models and training strategies that tolerate scarce, delayed, or noisy labels (e.g., weak supervision, robust loss, confidence learning).
- Structure data, training, evaluation, serving, and operations into a traceable, evolvable pipeline that reduces complexity and speeds iteration.