Scribie- Senior Applied ML Engineer – Audio and Foundation Models

Scribie; Bengaluru; Hybrid; INR 1,500,000 - 2,500,000; Full time

Job summary

Scribie is looking for an ML professional to manage the full lifecycle of audio models in their Bangalore office. This role offers substantial ownership in creating impactful audio solutions suitable for high-stakes environments.

You will enhance and deploy multimodal models, ensuring the work directly benefits customers and meets production standards. The position combines in-office collaboration with a flexible work model, providing opportunities for personal growth in a supportive setting.

Qualifications

• Experience in shipping fine-tuned models into production.
• Ability to run large training jobs and address related issues.
• Focus on impactful work rather than just academic benchmarks.

Responsibilities

• Own the full ML lifecycle for audio models.
• Fine-tune large models using advanced techniques.
• Lead the evaluation and production of model inference services.

Skills

Fine-tuning ASR / LLM / multimodal models

Running large training jobs

Debugging failures

Real-world impact focus

Job description

Overview

Scribie is an AI-powered, Human Verified audio and video transcription service, trusted globally since 2008. We specialize in delivering accurate and reliable transcription solutions by blending advanced AI technology with human expertise. Headquartered in the US, we operate with a hybrid model in our Bangalore office, combining the flexibility of remote work with the collaboration of in-person engagement. This approach offers our team both autonomy and growth opportunities in a dynamic and supportive environment.

We’re building production-grade audio foundation models for high-stakes legal and enterprise transcription — real customer data, messy audio, real consequences.

This is not a paper-only research role.

Location: Bangalore (primarily onsite)

Responsibilities

You’ll own the full ML lifecycle:

• Fine-tuning large audio / multimodal models using SFT, LoRA, and RL-based preference optimization (DPO / PPO / ORPO)
• Beating strong baselines like Whisper-large, GPT-4o, Gemini, Claude on domain‑specific data
• Designing WER, diarization, and alignment-driven evaluation stacks
• Taking models from research notebooks → production inference services
• Running daily experiments that directly impact quality, cost, and customer satisfaction

You’ll be our first ML hire, with real ownership over the audio ML roadmap — not a side project, not a support role.

Qualifications

This role is a great fit if you:

• Have shipped fine-tuned ASR / LLM / multimodal models into production
• Are comfortable running large training jobs and debugging failures
• Care about real-world impact, not just benchmarks

Not a Fit

Not a fit if you’re looking for an academic or paper-only research role.