Machine Learning Engineer - 2

Qualification

4–7 years of relevant experience as a Machine Learning Engineer or in an applied research/engineering role. Mandatory: Deep hands-on PyTorch expertise — custom datasets, distributed/multi-GPU training, mixed precision, and inference optimization. You can read a PyTorch profiler trace and act on it. Mandatory: Multiple models shipped to production and kept in production, with evidence of measured latency/throughput improvements. Mandatory: Hands-on experience training, fine-tuning, debugging, optimizing, and productionizing modern deep-learning architectures, including CNNs, vision transformers, and vision foundation models such as DINO- and SAM-style models. You are comfortable reading unfamiliar model implementations, adapting them to new use cases, and improving their training efficiency, inference performance, and production reliability. Mandatory: Experience running experiments to a defined hypothesis with minimal supervision — and reporting findings others could act on. Bachelor's degree in Computer Science, IT, Statistics, or a related field; non-IT degrees with strong relevant experience are acceptable. Must-have