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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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