Newxel

Senior ML Engineer – Speech & Voice (NXJ-212)

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SeniorZdalnie·WarszawaFull-time

#452708·Dodano 28 dni temu·3

Źródło: Newxel

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Tech Stack / Keywords

Python PyTorch DockerpyannoteNeMoSortformerVBxECAPA-TDNNWhisperwav2vec

Firma i stanowisko

The platform delivers real-time AI video processing and automated content generation for professional sports leagues globally. It operates in high-noise live broadcast environments requiring tight latency budgets, high throughput, and robust handling of overlapping speech and crowd noise.

Wymagania

3–5+ years of experience shipping ML systems into production, with 2+ years dedicated to speech or audio pipelines.
Deep Python and PyTorch expertise, including hands-on GPU profiling, memory optimization, and inference acceleration.
Hands-on experience with at least two key speech domains: diarization (pyannote, NeMo/Sortformer, VBx), speaker embeddings (ECAPA-TDNN), or ASR and forced alignment (Whisper, Parakeet, wav2vec-family).
Strict evaluation rigor: experience building test sets, computing DER/WER/EER correctly, and running statistical hypothesis tests.
Ability to critically analyze academic papers or model cards and reproduce published claims on internal datasets.
Hands-on experience deploying containerized GPU services in production environments using Docker and queue/API architectures.

Nice to have:

Hands-on experience with Azure ecosystem tools (Service Bus, Blob Storage) and event-driven microservices.
Experience with domain adaptation or fine-tuning speech models on noisy, broadcast, or far-field sports audio.
Experience handling multilingual STT pipelines, particularly with Hebrew, Arabic, or Spanish.
Experience designing shadow deployment paths, experiment tracking workflows, and annotation team processes.

Obowiązki

Own and scale the core speech pipeline covering alignment, diarization, speaker identification, and enrollment signature-matching workflows.
Optimize GPU inference performance for throughput, memory footprint, and cost through batching, precision tuning, and compilation.
Build and maintain labeled sports test benchmarks stratified by speaker count, audio quality, language, and background noise.
Define and track production evaluation metrics including DER, WER, word-level speaker attribution, speaker-count error, latency, and GPU cost.
Conduct rigorous model bake-offs and write clear decision memos detailing trade-offs, licensing constraints, and statistical uncertainty.
Maintain a shadow or A/B deployment framework to gate every production release with empirical evidence.

Newxel

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