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For Companies At a Glance
- Tasks: Join us to transform AI prototypes into scalable systems and build innovative infrastructure.
- Company: Codesearch AI partners with cutting-edge start-ups, creating revolutionary AI technology.
- Benefits: Enjoy flexible work options, competitive salary, health benefits, and a professional development budget.
- Why this job: Be part of a dynamic team shaping the future of AI with impactful projects.
- Qualifications: MSc or PhD in ML/Computer Science and 5+ years in ML engineering or related fields required.
- Other info: Opportunity to publish research and directly influence product development.
The predicted salary is between 60000 - 84000 £ per year.
Over the past 8 years, Codesearch AI have had the pleasure to work with some of the most ground-breaking and successful start-ups around. We can safely say this company is as exciting as it gets. We are an exclusive partner to a YC-backed start-up that is building truly transformative AI technology. Their agentic AI platform goes well beyond chat interfaces, offering ground-breaking memory capabilities that solve real enterprise problems with unprecedented accuracy. As validation of their innovative approach, one of the world’s most widely used AI tools is already exploring adoption of their technology.
With a founding team of accomplished researchers and engineers from organizations like LinkedIn and FAIR, they are now expanding their core team to bring this revolutionary product to market.
The Role
They are seeking their first dedicated ML Engineer to help productise their Agentic AI platform. This role is perfect for someone who loves to move fast, ship usable systems, and operate at the intersection of LLMs, infrastructure, and software engineering.
What You’ll Be Doing
- Take working prototypes of LLM-based agents and productize them into scalable, robust systems
- Build infrastructure and pipelines to support and integrate AI Agents in real-world enterprise environments
- Collaborate with the founding team to integrate models into internal and external user flows
- Write clean, production-ready code - often improving or refactoring existing prototypes
- Think holistically about agent lifecycle, observability, failure handling, and scalability
- Help define the tech stack and architecture for core components of the platform
- Contribute to novel research and publish at top conferences when opportunities arise
What You’ll Have
- MSc or PhD in Machine Learning, Computer Science or a related field
- 5+ years of experience in ML engineering, MLOps and/or backend/infra-focused roles
- Experience integrating LLMs into enterprise SaaS or internal tooling
- Strong Python experience with ML/LLM libraries (e.g., Transformers, LangChain, LangGraph, OpenAI APIs)
- Experience with cloud platforms (AWS, GCP, or Azure), deployment, and CI/CD pipelines
- Familiarity with containerization (Docker, Kubernetes) and observability (e.g., Prometheus, Grafana)
- A builder mindset: you’re comfortable with ambiguous specs, early-stage infrastructure, and iterating fast
- Excellent communication and self-management skills
Nice To Have
- Familiarity with agentic frameworks, orchestration tools, or vector databases
- Background in DevOps/MLOps or platform engineering
- Passion for building something from scratch and seeing the impact of your work in production
What We Offer
- Competitive salary with equity options based on experience and profile
- Flexible work arrangements with remote/hybrid options
- Comprehensive health benefits and wellness programs
- Professional development budget for conferences and continued learning
- A front-row seat to the agentic AI evolution
- Full ownership and trust over your code and system decisions
- A lean, expert team with direct access to product, users, and strategic investors
- Opportunity to shape the future of AI in a fast-growing market segment
Lead Machine Learning Engineer (Agentic Infrastructure) employer: Codesearch AI
Contact Detail:
Codesearch AI Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Machine Learning Engineer (Agentic Infrastructure)
✨Tip Number 1
Familiarise yourself with the latest advancements in agentic AI and LLMs. Understanding the nuances of these technologies will not only help you during interviews but also demonstrate your genuine interest in the role.
✨Tip Number 2
Network with professionals in the AI and machine learning community, especially those who have experience in enterprise applications. Engaging in discussions or attending meetups can provide insights and potentially lead to referrals.
✨Tip Number 3
Showcase your hands-on experience with relevant tools and technologies, such as Docker, Kubernetes, and cloud platforms. Being able to discuss specific projects where you've implemented these will set you apart from other candidates.
✨Tip Number 4
Prepare to discuss your approach to building scalable systems and handling failures. The role requires a builder mindset, so be ready to share examples of how you've tackled challenges in previous projects.
We think you need these skills to ace Lead Machine Learning Engineer (Agentic Infrastructure)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning engineering, particularly with LLMs and infrastructure. Use keywords from the job description to demonstrate your fit for the role.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for AI and your experience in productising ML systems. Mention specific projects where you've integrated LLMs or built scalable systems, and express your enthusiasm for joining a pioneering team.
Showcase Your Technical Skills: In your application, provide examples of your proficiency with Python and relevant ML libraries. If possible, include links to GitHub repositories or projects that demonstrate your coding skills and experience with cloud platforms.
Highlight Collaboration Experience: Since the role involves working closely with a founding team, emphasise any past experiences where you collaborated on projects. Discuss how you contributed to team success and how you handle communication in a fast-paced environment.
How to prepare for a job interview at Codesearch AI
✨Showcase Your Technical Expertise
Be prepared to discuss your experience with machine learning, particularly in integrating LLMs into enterprise environments. Highlight specific projects where you've built scalable systems or improved existing prototypes, as this will demonstrate your hands-on skills and understanding of the role.
✨Demonstrate a Builder Mindset
This role requires someone who thrives in ambiguous situations and can iterate quickly. Share examples of how you've tackled challenges in early-stage infrastructure or product development, showcasing your ability to adapt and innovate under pressure.
✨Communicate Clearly and Effectively
Strong communication skills are essential for collaborating with the founding team and integrating models into user flows. Practice explaining complex technical concepts in simple terms, as this will help you connect with interviewers and show your ability to work within a team.
✨Prepare Questions About the Company and Role
Research Codesearch AI and their agentic AI platform thoroughly. Prepare insightful questions that reflect your interest in their technology and vision. This not only shows your enthusiasm but also helps you assess if the company aligns with your career goals.
