Arcani
Head of Machine Learning
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Company Description
Our systems listen, so others can be forearmed.
We are hiring Head of Machine Learning as we embark on the next critical phase of our Acoustic Detection product roadmap.
Arcani takes its name from the Arcani of Roman Britain - elite scouts known for their agility, intel, and mobility. Today, we’re building the modern equivalent - sensor scouts designed to detect and protect, giving defenders the edge.
We’re hiring across multiple roles. If you want to join a fast-moving startup and help shape the future of threat detection, we’d love to hear from you.
Type: Full-time
Security: Must be eligible to work with defence customers and sensitive projects
Arcani is at the forefront of tackling solutions for detection from the most cutting-edge threats, such as drones and Unmanned Ground Vehicles. We develop cutting-edge hardware and AI sensor systems that address this challenge in difficult environments. Our team is driven by a shared vision to create impactful solutions that give defenders the tools they need to protect themselves.
The Role
ARCANI is seeking a Head of Machine Learning to lead the development, deployment, and continuous improvement of the machine-learning capability underpinning our acoustic detection systems.
The successful candidate will take ownership of ARCANI’s machine-learning strategy, technical architecture, and delivery roadmap. They will lead a growing ML team and work closely with our acoustics, embedded systems, software, and product teams to translate cutting-edge research into reliable, real-time operational capability.
This is a hands-on leadership role. The Head of Machine Learning will be expected to provide technical direction, review and contribute to model development, establish robust engineering processes, and ensure that ARCANI’s machine-learning systems perform reliably on edge hardware in complex real-world environments.
Key Responsibilities
Machine-Learning Strategy and Leadership
- Define and deliver ARCANI’s machine-learning strategy in support of the company’s product and commercial objectives.
- Own the technical roadmap for acoustic detection, classification, localisation, and tracking.
- Lead, mentor, and develop ARCANI’s machine-learning engineers and researchers.
- Establish priorities, allocate technical resources, and manage ML development programmes.
- Build a high-performing ML function with appropriate development, testing, documentation, and review processes.
- Support recruitment and capability development as the machine-learning team grows.
Model Development
- Lead the development of models for the detection and classification of drones and other relevant acoustic targets.
- Oversee the development of real-time three-dimensional acoustic localisation and tracking capabilities.
- Develop systems that combine ambisonic audio features, spatial audio processing, convolutional neural networks, and causal temporal models.
- Evaluate and apply suitable deep-learning, signal-processing, and classical machine-learning approaches.
- Improve model performance across detection range, classification accuracy, localisation accuracy, false-alarm rate, and processing latency.
- Develop methods for identifying previously unseen or emerging target types.
- Support multimodal fusion between acoustic, radio-frequency, and other sensor inputs where appropriate.
Data Strategy and Model Training
- Own ARCANI’s machine-learning data strategy, including data collection, annotation, storage, governance, and quality assurance.
- Define standards for field data collection during trials, demonstrations, and operational deployments.
- Develop representative datasets covering different drone types, environments, weather conditions, ranges, terrains, and background noise.
- Establish effective processes for data labelling, dataset versioning, and training-data traceability.
- Address class imbalance, limited datasets, domain shift, and environmental variation.
- Develop simulation, augmentation, and synthetic-data approaches where these improve operational performance.
- Ensure that operational feedback and newly collected data are incorporated into model-development cycles.
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Edge Deployment and MLOps
- Lead the optimisation and deployment of models onto embedded and edge-computing hardware.
- Ensure models operate within practical constraints relating to compute, memory, power, bandwidth, and latency.
- Work closely with embedded and software engineers to integrate models into ARCANI’s deployable systems.
- Establish robust MLOps processes covering experimentation, model versioning, testing, deployment, rollback, and monitoring.
- Develop processes for secure over-the-air model updates.
- Ensure deployed models are reproducible, traceable, and appropriately controlled.
- Monitor model performance across deployed systems and identify degradation or data-drift risks.
Testing, Validation, and Operational Performance
- Define measurable performance criteria for machine-learning systems.
- Design and oversee structured model-validation and field-testing programmes.
- Support Technology Readiness Level progression and independent test and evaluation activities.
- Analyse performance against operational requirements, including probability of detection, false-alarm rate, classification accuracy, bearing accuracy, range estimation, and time to alert.
- Develop confidence-scoring and uncertainty-estimation methods suitable for operational use.
- Investigate model failures and implement corrective actions.
- Produce clear evidence demonstrating model performance to customers, partners, and test organisations.
Intellectual Property and Technical Assurance
- Identify and protect novel machine-learning methods, architectures, datasets, and deployment approaches.
- Support patent applications, invention disclosures, and technical due diligence.
- Maintain awareness of relevant academic research, competitor capabilities, and emerging technologies.
- Ensure that ARCANI’s machine-learning development is technically rigorous and appropriately documented.
- Support cybersecurity, safety, assurance, and responsible-AI activities relevant to defence applications.
- Contribute to technical documentation for procurement, certification, grant, and investment processes.
Essential Experience and Skills
- Significant professional experience developing and deploying machine-learning systems.
- Demonstrable experience leading machine-learning engineers, researchers, or technical teams.
- Strong practical knowledge of deep learning, neural-network architectures, and model-training methods.
- Experience developing models for audio, acoustics, time-series data, computer vision, radar, RF, or other sensor data.
- Strong Python development skills and experience with frameworks such as PyTorch or TensorFlow.
- Experience deploying models onto embedded, edge, or resource-constrained computing platforms.
- Understanding of model optimisation techniques such as quantisation, pruning, distillation, and hardware acceleration.
- Experience with MLOps, data pipelines, experiment tracking, model versioning, and automated testing.
- Strong understanding of dataset design, annotation, augmentation, and validation.
- Ability to interpret research and convert it into production-ready technology.
- Strong analytical and problem-solving skills.
- Ability to communicate complex technical information to engineering, commercial, military, and non-technical stakeholders.
- Experience delivering technology from early-stage research through to tested products.
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Desirable Experience
- Experience in acoustic machine learning, sound-event detection, or sound-event localisation and detection.
- Knowledge of ambisonics, microphone arrays, beamforming, spatial audio, or array signal processing.
- Experience with causal temporal networks, convolutional architectures, transformers, or recurrent models for real-time signal analysis.
- Experience in drone detection, counter-UAS, defence, aerospace, robotics, or autonomous systems.
- Experience working with NVIDIA Jetson, ARM processors, NPUs, or similar edge-computing platforms.
- Experience integrating ML outputs into command-and-control or situational-awareness systems.
- Familiarity with military test and evaluation, NATO standards, or defence procurement.
- Experience developing explainable, safety-critical, or mission-critical AI systems.
- Knowledge of RF sensing, sensor fusion, or multi-sensor tracking.
- Experience supporting patents, technical publications, or research partnerships.
- Experience working within a high-growth startup or scale-up environment.
Qualifications
- A degree or postgraduate qualification in machine learning, artificial intelligence, computer science, electrical engineering, acoustics, mathematics, physics, or a related discipline is preferred.
- Equivalent professional experience and a strong record of delivering operational machine-learning systems will also be considered.
Personal Attributes
- A technically credible and hands-on leader.
- Comfortable operating between research, engineering, and product delivery.
- Motivated by solving difficult real-world problems rather than producing purely experimental models.
- Pragmatic, delivery-focused, and able to make decisions with incomplete information.
- Able to work effectively in a fast-moving and evolving startup environment.
- Committed to high engineering standards and continuous improvement.
- Comfortable participating in outdoor trials and customer demonstrations.
- Able to build trusted relationships with technical teams, customers, and strategic partners.
What ARCANI Offers
- A senior technical leadership position within a rapidly growing defence-technology company.
- The opportunity to shape a core machine-learning capability from an early stage.
- Direct involvement in the development and deployment of technology with immediate operational relevance.
- The opportunity to work with defence users, NATO partners, and leading technology organisations.
- Competitive salary and benefits.
- Potential participation in an employee equity or share-option scheme.
- Flexible working arrangements, subject to operational and security requirements.
- Bonus, and frequent salary reviews.
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