Senior Machine Learning Engineer, Voice AI

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.

We are looking for a Senior Machine Learning Engineer to own our voice ML system end to end in a fast-moving, low-bureaucracy environment.

We have a working voice model in production, and its outputs feed a matching layer between candidates, roles, and teams. The next step is making the whole system more measurable, scalable, and easier to evolve. You will own the full path from training data to model evaluation to inference, and help shape how matching learns from real hiring outcomes.

This is not a research-only or MLOps-only role. You will work across data pipelines, audio preprocessing, model training, evaluation, serving, and production debugging.

The role is a strong fit for someone who can take a working system, find what is actually limiting it, run the experiment that answers the question, ship the improvement, and prove it with numbers.

Contribute to the matching layer that turns behavioral signal into candidate-to-role and candidate-to-team fit, making it explainable, testable, and calibratable as outcome data grows.

Own the training data pipeline: collection, validation, labeling, versioning, and dataset creation.

Train, evaluate, and ship models, benchmarking every candidate against the current production model before deciding what goes live.

Define what gets measured across accuracy, latency, reliability, and failure modes.

Debug the full voice pipeline, from speech-to-text output through audio preprocessing, diarization, feature extraction, model output, and downstream predictions.

Optimize inference speed and reliability, including quantization, distillation, or smaller architectures where they improve the system.

Simplify the ML infrastructure so the pipeline is easier to trace, debug, and evolve.

Work closely with our psychometrician to translate measurement requirements into model targets, validation criteria, and evaluation design.

Experience building predictive or recommendation models on top of extracted features, ideally where labels are scarce, delayed, or noisy.

Strong production ML experience: you have trained, deployed, and maintained models that real users depend on.

Deep experience with modern ML frameworks (PyTorch, Tensor Flow, JAX) and strong Python for training, data pipelines, and experimentation.

Experience designing ML data pipelines and a sharp sense of how bad data limits model performance.

Hands‑on experience with audio, speech, or voice systems, including speech‑to‑text APIs, diarization, and audio feature extraction.

Experience with model optimization and inference efficiency, and the ability to benchmark accuracy, latency, and performance tradeoffs.

SQL skills to inspect what data exists and what is usable for training and evaluation.

Ability to think architecturally across data, training, evaluation, serving, and operations.

Clear communication about tradeoffs, risks, assumptions, and progress.

Experience with speech-to-text (STT) providers and APIs.

AWS, Modal, containerization, async jobs, model serving, production monitoring.

Cohort-specific training, specialized models, or model routing.

You need highly detailed specs before starting.

You prefer slow-moving enterprise structure.

You only want to train models and hand off serving, or only want to run infra and never touch the model.

You ship models without comparing them to production.

You are uncomfortable making decisions with incomplete data.

You use AI-generated code without verifying it.

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…