Machine Learning Engineer

This is a fully remote position, open to applicants in United Kingdom.

📋 Description

• Develop Production-Grade Evaluation Systems: Create and implement thorough evaluation frameworks that assess agent performance, monitor enhancements over time, and guarantee that our AI systems provide consistent value to customers.

• Manage Experimentation-to-Production Pipeline: Oversee the complete ML lifecycle from prototype to production, constructing scalable systems that facilitate rapid iteration while ensuring reliability and performance in customer settings.

• Foster Cross-Team ML Integration: Collaborate closely with product teams to seamlessly embed ML capabilities into customer-facing features, making certain that technical excellence translates into user value and product differentiation.

• Enhance AI Agent Performance: Persistently refine our AI agents through systematic experimentation, prompt engineering, and architectural improvements, measuring success through customer impact and system performance.

• Scale ML Infrastructure: Establish the foundational ML systems, monitoring, and tools that will support our expansion from startup to large scale, ensuring we can deploy new capabilities swiftly without sacrificing quality.

• Collaborate with Engineering Leadership: Work directly with our CTO through regular check-ins and strategic alignment while functioning with high autonomy and self-direction in daily execution.

• Mentor Through Excellence: Naturally mentor junior ML engineers by conducting code reviews, providing technical guidance, and sharing practical experience from developing production ML systems.

⛳️ Requirements

• Proven Production ML Experience: At least 6 years of experience in building and scaling machine learning systems in production environments, with hands-on experience transitioning from experimentation to customer-facing deployments.

• Deep Neural Networks Foundation: A solid background in classical neural networks and deep learning principles before specializing in modern LLMs and transformer architectures - possessing an understanding of the foundations, not just familiarity with the latest tools.

• Product-Focused ML Mindset: Experience in constructing ML systems that address real business challenges, with a history of integrating classification, prediction, or recommendation systems into actual products used by customers.

• Multi-Company Perspective: Experience across various organizations (scale-ups, startups, or a mix), providing practical insights on what tools to build versus buy and how to avoid over-engineering.

• Technical Versatility: Proficient Python skills with adaptability across ML frameworks and tools - comfortable adjusting to our stack, including LangChain, evaluation frameworks, and workflow orchestration tools like Temporal.

• Self-Directed Leadership: Capability to work independently while staying closely aligned with leadership, comfortable with regular check-ins yet able to drive projects autonomously.

• Cross-Functional Collaboration: Experience in closely working with product teams and potentially customers, translating technical capabilities into business value and user experiences.

🏝️ Benefits

• Real-World AI Impact: Contribute to the actual productionization of LLMs and machine learning to address significant cybersecurity challenges - your work will directly protect organizations from genuine threats, rather than merely optimizing internal metrics.

• Technical Leadership Opportunity: Collaborate directly with our CTO on cutting-edge ML infrastructure while having the freedom to influence technical decisions and build systems that scale with our rapid growth.

• Expert Team Partnership: Join a team of experienced leaders with backgrounds in Big Tech and Scale-ups, including leadership team members who have participated in multiple acquisitions and an IPO.

• Build the AI-Native Future: Influence how generative AI reshapes cybersecurity from the ground up, establishing ML practices and technical standards that will shape the industry.

• Multiple Growth Pathways: Clear opportunities to advance into roles such as Head of ML Engineering, become a domain technical lead, transition into customer-facing technical roles, or excel as a senior individual contributor - the choice is yours based on your interests and our needs.

• Breakthrough Technology: Work at the convergence of generative AI and cybersecurity, developing solutions that leverage the latest advancements in LLMs and AI agents to tackle some of the most pressing challenges faced by security teams today.