Founding ML Engineer

Quantbot; Bengaluru; On-site; INR 4,000,000 - 8,000,000; Full time

Job summary

Quantbot in Bengaluru seeks an experienced ML engineer to advance production-grade models and real-time inference systems. You will design data pipelines, train and fine-tune models, and ensure robust evaluation and deployment practices.

The role emphasizes Python/PyTorch, distributed training, and modern fine-tuning techniques (LoRA/QLoRA/RLHF), with a strong focus on shipping high-quality ML solutions for real-world use in multilingual contexts.

Qualifications

• 3+ years of hands-on ML experience with real-world model training.
• Strong Python and PyTorch fundamentals, with exposure to distributed training and fine-tuning techniques (LoRA, QLoRA, RLHF).
• Experience designing data pipelines from collection, cleaning, labeling, deduplication, augmentation and treating data quality as a core engineering discipline.
• Rigorous evaluation mindset; you know that using demos alone is not a benchmark, you build evals first.
• Speech model experience is a plus with latency optimization, quantization, and distillation for production.
• Bias toward shipping; you’d rather have a model running in production this week than a notebook prototype next quarter.

Responsibilities

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Skills

Python

PyTorch

Distributed training

LoRA

QLoRA

RLHF

Data pipelines

Data quality

Model evaluation

Speech models

Latency optimization

Quantization

Distillation

Production deployment

Multilingual models

Job description

Batch: 2019/2020/2021/2022/2023.

Required Skills

• 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.
• Speech model experience is a plus with real-time / streaming inference experience where you would have contributed to latency optimization, quantization, and distillation for production deployment.
• Bias toward shipping. You'd rather have a model running in production this week than a perfect one in a notebook next quarter.

Preferred Skills

• 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.