Machine Learning Engineer

microTECH Global Limited; Cambridgeshire and Peterborough; Hybrid; GBP 45,000 - 70,000; Full time

Job summary

A leading tech company is seeking a Machine Learning Engineer to join their innovative team focused on optimizing state-of-the-art models for applications such as speech recognition. Responsibilities include training ML models, applying optimization techniques, and improving model performance on multi-GPU systems, contributing to cutting-edge technology advancements.

Qualifications

• Strong foundations in Machine Learning needed.
• Experience in training ML models for production.

Responsibilities

• Train and deploy state-of-the-art ML models.
• Enhance speech models with features like multilingual support.
• Optimize models for low-latency inference.

Skills

Machine Learning

Data Augmentation

Optimization Techniques

Multi-GPU Systems

Education

Master’s or PhD in a relevant field

Tools

PyTorch

TensorFlow

Linux

Version Control

CI/CD Systems

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.

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).