Design, build, and improve ASR, audio, and speech-related ML systems for production.
Develop signal processing pipelines for noisy, compressed, telephony-style, or real-world audio.
Train, fine-tune, evaluate, and deploy models for ASR, audio classification, diarization, redaction, or related tasks.
Own ML workflows end-to-end: data preparation, model training, validation, inference, monitoring, and iteration.
Optimize inference for latency, throughput, cost, and reliability.
Debug model quality issues through data analysis, targeted evaluations, and production monitoring.
Collaborate with product and engineering teams to turn business problems into practical ML solutions.

At least 5 years of hands-on experience deploying ASR or other ML systems in production.
Strong background in signal processing, speech recognition, audio ML, or telephony/audio pipelines.
Experience with production ASR systems, streaming inference, VAD, noise handling, diarization, speaker/channel issues, or similar speech technologies.
Strong Python engineering skills and experience building production services.
Experience with frameworks such as PyTorch, TensorFlow, JAX, ONNX Runtime, or similar.
Experience deploying models with Docker, Kubernetes, FastAPI, Triton, vLLM, TorchServe, custom inference services, or cloud ML platforms.
Strong understanding of model evaluation, regression testing, observability, latency, memory, GPU/CPU utilization, and cost-performance tradeoffs.
Comfort working with messy real-world data, noisy labels, domain drift, and ambiguous production issues.

ML Engineer – Signal Processing, ASR

Key skills