Senior Engineer — ASR / TTS / Speech LLM (Training + Eval + Integration)

OutcomesAI, Inc.; Bengaluru; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

OutcomesAI, Inc. is building an AI-enabled nursing platform that augments clinical teams with AI voice agents and licensed nurses for virtual triage and remote monitoring.

We seek a skilled ML engineer to train and fine-tune speech models, prepare datasets, and build robust evaluation and deployment pipelines that integrate into our HIPAA-compliant stack. You will collaborate with back-end and DevOps teams to deploy models into inference stacks, optimize performance, and contribute to bias-aware

Qualifications

• 4–7 years in applied ML; ≥2 years focused on speech recognition or synthesis.
• Experience with model deployment workflows preferred.

Responsibilities

• Prepare and maintain synthetic and real training datasets for STT/TTS/Speech LLM model training.
• Build evaluation and deployment pipelines for clinical applications (RPM, Triage, inbound/outbound).
• Create data selection and augmentation scripts (noise, codec, jitter).
• Fine-tune models using CTC/RNN-T or adapter-based methods on multi-GPU systems.
• Implement evaluation pipelines measuring WER, Entity-F1, latency; automate MLflow logging.
• Collaborate with ML-ops and QA to integrate trained models into inference stacks.

Skills

Python

PyTorch

Hugging Face

NeMo

ASR fine-tuning

ETL data processing

MLflow

Speech datasets

Education

B.Tech / M.Tech / M.S. in CS/AI or related field

Tools

Espnet

Kaldi/K2

Nemo

Livekit

Pipecat

Diffy

HuggingFace NLP tools

Job description

OutcomesAI is a healthcare technology company building an AI-enabled nursing platform designed to augment clinical teams, automate routine workflows, and safely scale nursing capacity.

Our solution combines AI voice agents and licensed nurses to handle patient communication, symptom triage, remote monitoring, and post-acute care — reducing administrative burden and enabling clinicians to focus on direct patient care.

Our core product suite includes:

• Glia Voice Agents – multimodal conversational agents capable of answering patient calls, triaging symptoms using evidence-based protocols (e.g., Schmitt-Thompson), scheduling visits, and delivering education and follow-ups.
• Glia Productivity Agents – AI copilots for nurses that automate charting, scribing, and clinical decision support by integrating directly into EHR systems such as Epic and Athena.
• AI-Enabled Nursing Services – a hybrid care delivery model where AI and licensed nurses work together to deliver virtual triage, remote patient monitoring, and specialty patient support programs (e.g., oncology, dementia, dialysis).

Our AI infrastructure leverages multimodal foundation models — incorporating speech recognition (ASR), natural language understanding, and text-to-speech (TTS) — fine-tuned for healthcare environments to ensure safety, empathy, and clinical accuracy. All models operate within a HIPAA-compliant and SOC 2–certified framework. OutcomesAI partners with leading health systems and virtual care organizations to deploy and validate these capabilities at scale. Our goal is to create the world’s first AI + nurse hybrid workforce, improving access, safety, and efficiency across the continuum of care.

Contribute to training, evaluation, and integration of speech models into OutcomesAI’s voice intelligence stack.You’ll work closely with the Tech Lead to develop datasets, fine-tune models, and benchmark performance across domains (RPM, Triage).

What You’ll Do

• Prepare and maintain synthetic and real training datasets.STT/TTS/Speech LLM model training: from model selection → fine-tuning → deployment.Build evaluation for clinical applications (RPM, Triage, inbound/outbound).
• Build scripts for data selection, augmentation (noise, codec, jitter), and corpus curation.
• Fine-tune models using CTC/RNN-T or adapter-based recipes on multi-GPU systems.
• Implement evaluation pipelines to measure WER, entity F1, and latency; automate MLflow logging.
• Experiment with bias-aware training and context list conditioning.
• Collaborate with backend and DevOps teams to integrate trained models into inference stacks.
• Support creation of context biasing APIs and LM rescoring paths.
• Assist in maintaining benchmarks versus commercial baselines (Deepgram, Whisper, etc.).

Desired Skills

• Strong programming in Python (PyTorch, Hugging Face, NeMo, ESPnet).
• Practical experience in audio data processing, augmentation, and ASR fine-tuning.Training: SpecAugment , speed perturb, noise/RIRs, codec+PLC+jitter sims for PSTN/WebRTC.Streaming ASR: Transducer/zipformer with chunked attention, frame-sync beam search, endpointing (VAD-EOU) tuning.Context biasing: WFST boosts + neural re-scoring; patient/name dictionaries; session-aware bias refresh.
• Familiarity with LoRA/adapters, distributed training, mixed precision.
• Proficiency with evaluation frameworks WER/sWER, Entity-F1, DER/JER, MOSNet/BVCC (TTS), PESQ/STOI (telephony), RTF/latency at P95/P99. and MLflow logging.Frameworks: Espnet, Speech brain, Nemo, Kaldi/K2, Livekit, Pipecat, Diffy.
• Understanding of telephony speech characteristics, accents, and distortions.
• Collaborative mindset for cross-functional work with ML-ops and QA.

Qualifications

• B.Tech / M.Tech / M.S. in Computer Science, AI, or related field.
• 4–7 years in applied ML; ≥2 years focused on speech recognition or synthesis.
• Experience with model deployment workflows preferred.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.