machine learning engineer

Enigma is an early-stage technology company developing innovative connected hardware and software products.

• 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;
• Lead model design, training, validation, and deployment for real-time sensor classification tasks;
• Develop evaluation methodologies that reflect real-world operating conditions;
• 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 and quality standards;
• Identify gaps in training and validation datasets;
• Assess collected data for suitability and model readiness;
• Communicate technical decisions clearly to stakeholders;
• Influence technical outcomes without direct managerial authority.

• 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;
• 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;
• Nice to have: 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, experience designing and executing data collection programmes.

• No conditions specified