Principal Machine Learning Engineer (Sensor Intelligence) | Python | Audio | Sensor Data | Machine Learning | PyTorch | London
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
- 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
- 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
- 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
- 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
- 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
- Strong machine learning foundations, processes, and standards that enable future team growth
