Senior ML Research Scientist, Speech

About Blue Machines and the Speech Research team

Blue Machines (ApnaTime Tech Pvt. Ltd.) builds enterprise voice AI agents for banks, insurers, healthcare and telecom companies. Our platform has handled tens of millions of production voice minutes, is ISO 27001/27701 and SOC 2 Type II certified, and runs both on cloud and on-prem.

We already run our own models in production: Aurora (STT tuned for BFSI), Floe (language-switch detection), end-of-utterance models, and noise cancellation, all on BM Zap, our low-latency voice framework. The Speech Research team takes this further: better accuracy on Indian languages and code-mixed speech, natural expressive TTS, and full-duplex speech-to-speech models that cut latency across the whole conversation.

What makes this work different

Real traffic: your model ships to live enterprise calls, not a leaderboard

Hard problems: 8 kHz telephony audio, Hinglish and code-switching, accents, noisy environments, domain entities (loan numbers, policy IDs, names)

Tight latency budgets: streaming inference where every 50 ms is noticed by callers

A full loop: data, training, evaluation, serving and production feedback in one team

Location: Bengaluru (on-site / hybrid) · Team: Speech Research, reporting into the CTO org

Senior ML Research Scientist, Speech (3–6 years)

You will own a research direction (STT, TTS or speech-to-speech) end to end. That means designing new architectures, pre-training foundation models from scratch on large multilingual audio, and getting them into production, while mentoring engineers on the team.

What you will do

Lead one research track and set its roadmap with the CTO and product leads, tied to customer metrics (accuracy, naturalness, latency, cost per minute)

Design novel architectures and pre-train speech foundation models from scratch on large multilingual corpora (100k+ hours), including self-supervised pre-training, tokenizer and codec design, and scaling experiments. Fine-tuning and continued training are tools, not the whole job

STT: streaming ASR with low time-to-first-token, code-switch robustness, contextual biasing for names and entities, domain adaptation for BFSI and healthcare

TTS: expressive, low-latency streaming TTS for Indian languages; voice cloning, prosody and emotion control, pronunciation of numbers, dates and mixed-script text

Speech-to-speech: Full-duplex conversational models built in-house: our own audio tokenizers and codecs, speech-LLM pre-training and alignment, interruption and turn-taking, end-of-utterance prediction

Build the data strategy: sourcing, licensing, synthetic data generation, labelling quality and consent-compliant use of production audio

Define evaluation standards and benchmarks that predict real call outcomes, and run honest comparisons against external vendors

Make serving trade-offs with the platform team: distillation, quantisation, GPU concurrency per model, on-prem footprints

Mentor 1–3 engineers; review experiment designs and code

Publish or open-source selectively where it strengthens our position

What you bring

3–6 years in ML, with at least 2–3 years focused on speech (ASR, TTS, speaker/voice or audio-language models)

MS or PhD in CS, EE or related field, or equivalent depth shown through shipped work

Track record of pre-training at least one speech or audio model from scratch (not only fine-tuning) that reached production or strong published results

Deep knowledge of modern architectures: Conformer/Zipformer, RNN-T/TDT, encoder–decoder ASR, flow-matching and diffusion TTS, neural codecs (EnCodec, DAC, Mimi), speech-LLMs

Large-scale pre-training experience: distributed training (FSDP/DeepSpeed/Megatron) on multi-node GPU clusters, 100k+ audio hours, scaling laws and compute budgeting, data loading at scale, debugging loss spikes and unstable runs

Strong experimental design: ablations, significance, avoiding test-set leakage

Clear written and spoken communication with engineers, product and customers

Publications at Interspeech, ICASSP, ACL/EMNLP, NeurIPS or similar

Experience with Indic languages, low-resource languages or code-switching

Built streaming or real-time speech systems with strict latency SLAs

Experience with RL or preference tuning for speech (e.g. naturalness rewards) or LLM post-training

Contributions to open-source speech toolkits

What success looks like in 12 months

Your track has shipped a model pre-trained from scratch by Blue Machines that beats the vendor it replaces on our benchmark and reduces cost or latency

A clear, reproducible training and evaluation stack for your area

Engineers you mentor are running experiments independently