Sr. ML Research Scientist

As a Senior ML Research Scientist (speech), you will own an entire research direction (STT, TTS, or speech-to-speech) end-to-end. You will design novel neural architectures, lead foundation model pre-training from scratch on massive multilingual audio datasets (100k+ hours), and ship models to production while mentoring engineers on the team.

Responsibilities:

• Lead a Research Track: Define and execute the roadmap for STT, TTS, or S2S alongside the CTO and product leads, tying research outcomes to business metrics (accuracy, naturalness, latency, cost-per-minute).
• Foundation Pre-training at Scale: Design novel architectures and pre-train foundation speech models from scratch on massive multilingual corpora (100k+ audio hours), including self-supervised learning, tokenizer/codec design, and scaling laws.
• STT Innovations: Develop streaming ASR with low time-to-first-token, robust code-switching handling, contextual biasing for enterprise entities (loan numbers, names), and BFSI/healthcare adaptation.
• TTS Innovations: Build expressive, low-latency streaming TTS for Indian languages with voice cloning, prosody/emotion control, and accurate pronunciation of mixed-script text, numbers, and dates.
• Full-Duplex Speech-to-Speech: Build conversational voice models in-house: audio tokenizers, neural codecs (EnCodec, DAC, Mimi), speech-LLM alignment, turn-taking, and interruption handling.
• Data Strategy: Lead audio sourcing, licensing, synthetic data generation, annotation quality, and consent-compliant usage of real production audio.
• Platform and Serving Trade-Offs: Partner with the platform team on model distillation, quantization, GPU concurrency, and on-prem deployment footprints.
• Mentorship and Publishing: Mentor 1-3 engineers, review experiment designs/code, and publish/open-source selectively to advance the company's research standing.

Requirements:

• 3-6 years in machine learning, with at least 2-3 years focused strictly on speech (ASR, TTS, speaker/voice, or audio-language models).
• Degree: MS or PhD in Computer Science, Electrical Engineering, or a related field (or equivalent depth demonstrated through shipped production models).
• Track Record of Pre-training: Proven experience pre-training at least one speech foundation model from scratch (not just fine-tuning) that reached production or published benchmarks.
• Deep Architectural Knowledge: Conformer/Zipformer, RNN-T/TDT, encoder-decoder ASR, flow-matching and diffusion TTS, neural codecs (EnCodec, DAC, Mimi), and speech-LLMs.
• Large-Scale Distributed Pre-training: Hands-on experience with multi-node GPU clusters using FSDP, DeepSpeed, or Megatron on 100k+ audio hours; experienced in compute budgeting, scaling laws, and debugging loss spikes/unstable runs.
• Scientific Rigor: Strong experimental design (ablations, statistical significance, leakage prevention) and benchmark design against external vendors.
• Leadership and Communication: Clear technical communication across engineering, product, and leadership, with prior experience mentoring junior engineers.

Nice to Have:

• First-author or co-author publications at Interspeech, ICASSP, ACL/EMNLP, NeurIPS, ASRU, or similar.
• Deep experience with Indic languages, low-resource acoustics, or Hinglish code-switching.
• Production experience building real-time speech systems under strict latency SLAs (< 100ms).
• Experience with RL or preference tuning for speech (naturalness rewards) or LLM post-training.
• Contributions to open-source speech toolkits (NeMo, ESPnet, K2/icefall).