Machine Learning Engineer - ASR / MT

Position Summary

We are seeking an experienced Machine Learning Engineer to serve as the technical lead for the architectural design and implementation of our Automatic Speech Recognition (ASR) and Machine Translation (MT) systems. The successful candidate will drive the development, fine-tuning, and optimization of high-performance models, with a specialized focus on multilingual capabilities and non-English language processing.

Role and Responsibilities

Deep Learning Architecture Design

Architect and develop advanced deep learning models specifically tailored for ASR and Neural Machine Translation (NMT) workflows, ranging from leveraging pre-trained models to building custom solutions from scratch.

Model Fine-Tuning & Optimization

Lead the strategy and execution of fine-tuning large-scale pre-trained models to achieve superior accuracy and efficiency in specific domains and languages.

Production Deployment & Scalability

Ensure the robustness, latency, and scalability of ML models within a production environment, bridging the gap between research prototypes and deployed services.

Technical Leadership & Strategy

Provide technical direction to the development team, making critical decisions on technology stacks, selecting appropriate open-source frameworks, and mentoring junior engineers.

R&D and Innovation

Continuously research and integrate state-of-the-art (SOTA) techniques in Multilingual NLP and speech processing to maintain competitive advantage.

Skills and Qualifications

Required Qualifications - Good to Have

Domain Expertise

Deep theoretical and practical knowledge of modern ASR frameworks (e.g., OpenAI Whisper, Kaldi, NVIDIA NeMo) and Machine Translation architectures (e.g., Transformers, Sequence-to-Sequence models).

Technical Proficiency

Expert-level proficiency in Python and major deep learning frameworks, specifically PyTorch, TensorFlow, and the Hugging Face ecosystem.

Multilingual Data Experience

Proven track record of training and optimizing models using non-English datasets, handling challenges related to low-resource languages or complex morphologies.

Engineering Leadership

Demonstrated ability to make high-level architectural decisions, define best practices, and effectively mentor and guide engineering teams.

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