ML Engineer
Posted 7 Oct 2026
Advertised as “Senior ML Engineer – Speech & Voice (NXJ-212)”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 3–5 yrs
- Management level
- Individual contributor
- Employment
- Hybrid
- Contract
- Full-time
No salary listed for this role.
The Role
GPU-based microservices handle the core speech pipeline—STT consumption, word-level alignment, diarization, and speaker identification. The primary challenge isn't just serving models, but establishing rigorous data-driven evaluation to separate real accuracy gains from benchmark noise on difficult sports audio. The Senior ML Engineer will own the speech services, lead fair model bake-offs, and hold full authority over which models reach production.
About the Product
The platform delivers real-time AI video processing and automated content generation for professional sports leagues globally. The underlying pipeline operates in high-noise live broadcast environments, demanding tight latency budgets, high throughput, and robust handling of overlapping speech and crowd noise.
Technology Stack: The platform delivers real-time AI video processing and automated content generation for professional sports leagues globally. The underlying pipeline operates in high-noise live broadcast environments, demanding tight latency budgets, high throughput, and robust handling of overlapping speech and crowd noise.
What You’ll Be Doing
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Own and scale the core speech pipeline covering alignment, diarization, speaker identification, and enrollment signature-matching workflows.
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Optimize GPU inference performance for throughput, memory footprint, and cost through batching, precision tuning, and compilation.
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Build and maintain labeled sports test benchmarks stratified by speaker count, audio quality, language, and background noise.
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Define and track production evaluation metrics including DER, WER, word-level speaker attribution, speaker-count error, latency, and GPU cost.
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Conduct rigorous model bake-offs and write clear decision memos detailing trade-offs, licensing constraints, and statistical uncertainty.
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Maintain a shadow or A/B deployment framework to gate every production release with empirical evidence.
What We Expect
Must-have
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3–5+ years of experience shipping ML systems into production, with 2+ years dedicated to speech or audio pipelines.
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Deep Python and PyTorch expertise, including hands-on GPU profiling, memory optimization, and inference acceleration.
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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).