ML Engineer -Signal Processing / ASR

ML Engineer — Signal Processing / ASR

We are looking for a hands-on Machine Learning Engineer with strong experience in signal processing, ASR, and production ML systems. This role is for someone who has built and deployed real-world audio or speech ML systems, not just experimented with models in notebooks.

You will work on real-time and post-call speech systems where accuracy, latency, reliability, and operational robustness matter.

Responsibilities:

• 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.

Requirements:

• 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.

Nice to Have:

• Experience with real-time ASR, call-center audio, VoIP, or telephony systems.
• Experience with Whisper, NVIDIA NeMo, Kaldi, wav2vec, HuBERT, Conformer, RNN-T, CTC, or transformer-based ASR.
• Experience with PCI/PII redaction, compliance-sensitive ML systems, or privacy-preserving workflows.
• Experience optimizing inference with ONNX, TensorRT, quantization, distillation, batching, or GPU serving.
• Experience with LLMs, RAG, embeddings, rerankers, or prompt-based systems layered on ASR transcripts.

Ideal Profile:
The ideal candidate is a practical ML systems engineer with strong intuition for audio, speech, and production behavior. They can take vague issues like “ASR quality dropped“, “latency spiked,” or “redaction missed edge cases” and turn them into clear investigations, measurable experiments, and production improvements.