Founding ML Engineer

📋 Job Overview

At Bolna, we’re building tools that change the way teams leverage Voice AI. We’re looking for a Founding Machine Learning Engineer to own the end-to-end lifecycle of building, evaluating, deploying, and improving models that power millions of production conversations. This is a high-impact, high ownership role where you won’t just work on Bolna’s ML stack—you’ll help build the foundation it scales on. Our team includes IIT alumni with experience at Bain, Atlassian, Uber, Zomato, and LinkedIn, and is backed by leading investors.

🏢 About Bolna

Bolna is backed by Y Combinator and leading investors, and we are on a mission to build a generational voice AI company from India. We’re profitable, growing at YC speed, and handling 4M+ minutes of real conversations every week.

🎯 The Role

You will be one of the earliest members of Bolna’s ML team and help shape our ML architecture, engineering culture, evaluation standards, and technical roadmap. You will orchestrate thousands of real-time voice AI conversations in parallel and own high-stakes problems that directly affect engineering, customer experience, infrastructure cost, and revenue.

✅ Key Responsibilities

• Build the data engine - Design pipelines to source and clean conversational voice data across Indian languages, accents, and telephony conditions.
• Fine-tune models that ship - Fine tune and train models to improve accuracy, speed, and reliability across different use-cases.
• Define what \"good\" means - Build evaluation datasets and benchmarks for transcription accuracy, voice naturalness, interruption handling, latency, and end-to-end conversation quality. Set up human-in-the-loop pipelines to capture subjective quality at scale.
• Ship to production - Work with the engineering team to deploy models into a latency-sensitive, high-volume system. Monitor performance in the wild, debug regressions, and iterate fast.

📌 Required Qualifications

• 3+ years of hands-on ML experience with deep practical real-world experience in training models.
• Strong Python and PyTorch fundamentals with exposure in distributed training, and modern fine-tuning techniques (LoRA, QLoRA, DPO, RLHF, etc.).
• Training data as a first-class problem. Experience designing data pipelines from collection, cleaning, labeling, deduplication, augmentation and treating data quality as a core engineering discipline.
• Rigorous about evaluation. You know that \"looks good in a demo\" is not a benchmark. You build the evals before you trust the model.
• Bias toward shipping. You'd rather have a model running in production this week than a perfect one in a notebook next quarter.

⭐ Desirable Experience

• Speech and real-time ML expertise: Experience working with speech, audio, multimodal, or real-time machine learning systems.
• Voice AI domain knowledge: Hands-on experience with ASR, TTS, VAD, language identification, speaker systems, or conversational turn-taking.
• Multilingual model development: Experience building and evaluating multilingual models, particularly for Indian languages.
• Inference optimisation: Experience with streaming inference, latency optimisation, quantisation, distillation, or model compression for production deployment.

🎁 Benefits

Competitive package + meaningful ESOP for early ownership + Health Insurance

🛂 Visa & Eligibility

No visa or eligibility information provided.