Head of Autonomous Learning Systems & Adaptive Algorithms 🤖📚

Role Overview 🌍

The Head of Autonomous Learning Systems & Adaptive Algorithms leads the creation of self-improving machine systems capable of learning from real-time interaction, user behaviour, and dynamic environments with minimal human supervision. This role focuses on the architecture, safety, and deployment of adaptive intelligence across a massive global ecosystem of products.

You will work at the edge of machine learning, cognitive modelling, and distributed computing—designing systems that refine themselves continuously, personalise experiences at scale, and adapt to shifting user needs. The role blends visionary thinking with meticulous engineering discipline, guiding teams that turn early-stage research into stable, responsible, production-ready intelligence.

Key Responsibilities 🚀1. Build the Next Generation of Adaptive Intelligence

• Lead the design of systems that update themselves through online learning, reinforcement learning, contextual bandits, and other adaptive frameworks.
• Architect scalable feedback loops enabling products to respond to real-world changes in real time.
• Develop generative and predictive models capable of continuous self-correction and behaviour refinement.

2. Research Leadership & Breakthrough Development 🔬

• Identify new research domains with high long-term value, such as meta-learning, neuro-symbolic models, simulation-to-real transfer, or continual learning architectures.
• Partner with global research teams to transition breakthroughs from experimental prototypes to robust systems.
• Shape internal knowledge frameworks and publish influential technical reports.

3. Large-Scale Deployment & Infrastructure ⚙️

• Build infrastructure that supports streaming datasets, online inference, distributed training, and multi-agent learning systems.
• Collaborate with platform engineering to optimise model efficiency, latency, and reliability across millions of devices and user interactions.
• Establish processes for automated testing, rollback, red-teaming, and performance analysis.

4. Responsible AI, Safety & Compliance 🛡️

• Define organisational standards for monitoring drift, preventing unintended model behaviours, and ensuring safe adaptation.
• Lead safety reviews, ethics evaluations, and long-term risk assessments for autonomous learning systems.
• Drive cross-functional governance practices around privacy, transparency, and user trust.

5. Leadership & Team Development 🌟

• Manage senior researchers, ML engineers, infrastructure specialists, and applied scientists.
• Cultivate a culture of curiosity, rigor, and responsible experimentation.
• Guide career development and contribute to global hiring strategies.

Required Qualifications 🎓

• PhD or equivalent experience in Machine Learning, Artificial Intelligence, Robotics, Computer Science, or related disciplines.
• 10+ years of experience building large-scale ML systems, including at least 5 years leading technical teams.
• Deep understanding of reinforcement learning, online learning, and adaptive systems.
• Experience designing distributed training pipelines, high-throughput inference systems, or multi-agent architectures.
• Strong background in Python, C++, or similarly performant languages.

Preferred Qualifications ⭐

• Experience with safety-critical or high-regulation environments (healthcare, finance, robotics, transportation).
• Publications or patents in areas such as continual learning, adversarial robustness, or self-supervised systems.
• Familiarity with human cognition modelling, behavioural science, or brain-inspired architectures.
• Demonstrated success guiding teams through ambiguous technical and ethical challenges.

What Success Looks Like in 12 Months 🏆

• Deployment of at least one organisation-wide adaptive learning system that enhances personalisation, safety, or performance.
• Clear long-term architecture and governance strategy for autonomous learning across product lines.
• Breakthrough research contributions adopted into production pipelines.
• A stronger, more cohesive team culture driven by scientific discipline and creative exploration.
• Noticeable improvements in system reliability, learning efficiency, and user experience.

Role Identity & Culture 🌱

This is a role for someone who enjoys wandering through complex systems like a natural philosopher—mapping how intelligence grows, how behaviour shapes itself, and how machines can learn responsibly from the world they inhabit. They should be equal parts engineer, scientist, strategist, and ethical compass.