Overview

We are an early-stage technology company developing innovative connected hardware and software products. We are seeking an experienced Principal Machine Learning Engineer to lead the development of machine learning systems for real-time classification of physiological and sensor-derived signals.

This is a hands-on individual contributor role working closely with software, firmware, and mobile engineering teams. The successful candidate will take models from research through to production deployment across embedded and mobile platforms, while defining the company's approach to machine learning development, evaluation, and deployment.

Role Summary The Principal Machine Learning Engineer will:

Build and evaluate machine learning models for real-time sensor classification tasks
Work across multiple sensor modalities, including audio and physiological signals
Lead the development of first-generation production models from research through deployment
Define data requirements and evaluation strategies for robust model performance
Drive the adoption and effective use of AI-assisted development tooling
Provide technical leadership for machine learning initiatives

Why This Role Matters

This is a foundational hire within a growing business. The successful candidate will shape the organisation's machine learning capability, establish technical standards, and define how novel sensor challenges are approached, evaluated, and solved.

Key Responsibilities

Machine Learning Modelling & Evaluation

Lead model design, training, validation, and deployment for real-time sensor classification tasks
Work across audio and physiological sensor data
Develop evaluation methodologies that reflect real-world operating conditions

Model Efficiency & Deployment

Models will be deployed on resource-constrained platforms, including embedded devices and mobile applications.
Design models that meet latency, memory, power, and compute constraints
Ensure models can be translated effectively to deployment targets
Collaborate with firmware and mobile engineering teams on deployment
Lead or support model optimisation, compression, quantisation, and on-device inference efforts

Data Strategy

Define data collection requirements, quality standards, and evaluation methodologies
Identify gaps in training and validation datasets
Assess collected data for suitability and model readiness
Drive data strategy to support robust model performance

Technical Leadership

Set technical direction for machine learning initiatives
Communicate technical decisions clearly to stakeholders
Collaborate effectively across engineering disciplines
Influence technical outcomes without direct managerial authority

AI Tooling & Development Practices

Modern AI-assisted development tools are increasingly important in machine learning workflows. The successful candidate will:

Use contemporary AI tooling effectively in model development and evaluation
Stay current with advances in the AI tooling ecosystem
Help define organisational best practices for AI-assisted engineering workflows
Evaluate and recommend appropriate tooling and processes

Candidate Profile The ideal candidate combines deep modelling expertise with practical engineering skills and a track record of delivering production machine learning systems on real-world sensor data.

Core Requirements

Strong expertise in machine learning for sensor and signal-processing applications
Proven ability to solve novel modelling challenges beyond standard approaches
Deep understanding of audio modelling and signal-processing fundamentals
Experience defining data requirements and evaluation methodologies
Ability to design models that operate within deployment constraints
Strong software engineering practices, including development in shared codebases
Proficiency with modern machine learning frameworks such as PyTorch or equivalent
Strong understanding and practical use of AI-assisted development tooling
Product-oriented mindset with the ability to own problems end-to-end

Desirable Experience

Deployment and optimisation of machine learning models for embedded or edge environments
Experience with physiological signals, wearable sensor data, or sensor fusion
Familiarity with low-power inference and TinyML approaches
Experience working with multimodal sensor systems
Research or academic background combined with a strong delivery focus

Nice to Have

Experience designing and executing data collection programmes

Leadership & Communication

Comfortable acting as the senior machine learning authority within an organisation
Able to influence cross-functional stakeholders without formal authority
Strong communication skills with both technical and non-technical audiences
Effective collaborator across software, firmware, and product teams

What Success Looks Like

High-performing, well-evaluated machine learning models operating reliably in real-world environments
Successful deployment across embedded and mobile platforms within resource constraints
A clear, scalable data strategy supporting continuous model improvement
Strong machine learning foundations, processes, and standards that enable future team growth

Principal Machine Learning Engineer (Sensor Intelligence) | Python | Audio | Sensor Data | Machine Learning | PyTorch | London

Job description