Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Reinforcement Learning Scientist

Stealth AI Startup
Liverpool
9 months ago
Applications closed

Related Jobs

View all jobs

Research Scientist -Machine Learning

Machine Learning Scientist, Biomolecule Design

Senior Machine Learning Scientist

Staff Machine Learning Scientist London, England

Senior Staff Machine Learning Scientist, Operations London

Senior Staff Machine Learning Scientist, Operations

Join Us: Research Scientist - Online Reinforcement Learning (RL) at an Agentic AI Start-Up!


Are you ready to revolutionize the future of intelligent agents? We're anAgentic AI start-upon a mission to build the next generation of autonomous systems capable of real-time learning, adaptation, and decision-making. If you’re passionate aboutOnline Reinforcement Learningand want to shape the frontier of AI, we’d love to hear from you!


About Us


We are a well-funded, ambitious, fast-growing start-up buildingAI agentsthat can learn, adapt, and thrive in dynamic, interactive environments. Our vision is to empower businesses and individuals with cutting-edge, agentic AI solutions that redefine how machines interact with the world.


The Role


As aResearch Scientist in Online Reinforcement Learning, you will:

  • Innovate: Develop groundbreaking algorithms for real-time learning and decision-making in dynamic, multi-agent systems.
  • Collaborate: Work closely with a team of researchers and engineers to create scalable solutions that deliver real-world impact.
  • Experiment: Lead experimental projects to address challenges like stability, data efficiency, and exploration in online RL.
  • Productize AI: Translate research insights into deployable AI systems for robotics, gaming, autonomous platforms, and more.
  • Share Knowledge: Publish research at top-tier conferences (e.g., NeurIPS, ICML, ICLR) and contribute to the global AI community.


What You’ll Bring


  • PhD or equivalentin Machine Learning, Reinforcement Learning, Computer Science, or related fields.
  • Expertisein RL algorithms (e.g., PPO, A3C, DQN) and their application to dynamic environments.
  • Proven Research Impact: Strong publication record in top conferences/journals and a passion for advancing AI.
  • Technical Skills: Proficiency in Python, RL frameworks (PyTorch/TensorFlow), and cloud-based ML tools.
  • Start-Up Mindset: A proactive, problem-solving attitude and a love for tackling challenges in fast-paced environments.
  • Visionary Thinking: A deep interest in agentic AI and its potential to transform industries.


Why Join Us?


  • Impactful Work: Shape the future of agentic AI in industries like autonomous vehicles, robotics, and intelligent systems.
  • Ownership: Be part of a start-up where your ideas and contributions directly drive our success.
  • Cutting-Edge Tech: Access to the latest tools, resources, and computational infrastructure.
  • Growth Opportunities: Thrive in a collaborative, growth-focused culture that values curiosity and innovation.
  • Start-Up Perks: Competitive salary, meaningful equity, flexible work options, and a chance to grow with us.


Our Mission


At our core, we’re driven by the belief that intelligent agents can reshape the way we live, work, and explore. Join us on our journey to build a future where AI systems are not just tools but partners in discovery and creation.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.