Machine Learning Engineer

microTECH Global LTD; Cambridgeshire and Peterborough; Hybrid; GBP 50,000 - 90,000; Full time

Job summary

A leading company in machine learning seeks a specialist to enhance speech recognition models. This hybrid role includes training advanced models and optimizing workflows, requiring a Master's or PhD in a relevant field. The position offers a dynamic workspace with a focus on innovation and performance improvement.

Qualifications

• Strong foundations in machine learning.
• Experience training ML models for production.

Responsibilities

• Train and deploy state-of-the-art machine learning models.
• Enhance speech recognition models with advanced features.
• Optimize models for low-latency inference.

Skills

Machine Learning

Model Optimization

Data Augmentation

PyTorch

TensorFlow

Linux

Version Control

CI/CD

Education

Master’s or PhD in relevant field

Job description

Overview

Work within our clients machine learning team to deploy and optimize models for applications like low-latency speech recognition and large language models (LLMs). Initial focus will be on improving our clients speech recognition model’s training pipeline on multi-GPU systems to boost performance and quality.

Hybrid working, 3 days onsite and 2 days WFH.

No Sponsorship, must have the right to work in the UK.

Responsibilities:

• Train and deploy state-of-the-art ML models.
• Apply optimization techniques (distillation, pruning, quantization).
• Enhance speech models with features such as diarization, multilingual support, and keyword boosting.
• Optimize models for low-latency inference on accelerators.
• Improve training workflows and GPU utilization.
• Use data augmentation to improve performance.
• Stay updated on ML research to guide strategy.

Requirements:

• Master’s or PhD in a relevant field with strong ML foundations.
• Training ML models for production use.
• PyTorch or TensorFlow.
• Handling large datasets (multi-terabyte).
• Familiarity with Linux, version control, and CI/CD systems.
• Knowledge of model compression (e.g., reduced precision).