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For Companies At a Glance
- Tasks: Join us to transform AI prototypes into scalable systems and build cutting-edge infrastructure.
- Company: Codesearch AI partners with innovative start-ups, creating groundbreaking AI technology that solves real problems.
- Benefits: Enjoy flexible work options, competitive salary, health benefits, and a budget for professional development.
- Why this job: Be part of a revolutionary team shaping the future of AI with direct impact on real-world applications.
- Qualifications: MSc or PhD in ML/Computer Science, 5+ years in ML engineering, strong Python skills, and cloud experience required.
- Other info: Opportunity to publish research and collaborate with top-tier engineers from LinkedIn and FAIR.
The predicted salary is between 54000 - 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 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 productise 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
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 and the company's mission.
✨Tip Number 2
Network with professionals in the AI and ML space, especially those who have experience with enterprise applications. Engaging in discussions on platforms like LinkedIn or attending relevant meetups can provide insights and potentially lead to referrals.
✨Tip Number 3
Showcase your hands-on experience by contributing to open-source projects related to ML engineering or LLMs. This not only enhances your skills but also provides tangible evidence of your capabilities to the hiring team.
✨Tip Number 4
Prepare to discuss your previous projects in detail, particularly those involving scalable systems and infrastructure. Be ready to explain your thought process, challenges faced, and how you overcame them, as this will highlight your problem-solving skills.
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 specific examples that demonstrate your skills in building scalable systems and integrating AI technologies.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for AI and your builder mindset. Mention your familiarity with the technologies listed in the job description and how your background aligns with the company's mission to productise their Agentic AI platform.
Showcase Your Projects: If you have worked on relevant projects, especially those involving LLMs or AI integration in enterprise environments, be sure to include them. Highlight your role, the challenges faced, and the impact of your contributions.
Prepare for Technical Questions: Anticipate technical questions related to ML engineering, cloud platforms, and coding practices. Brush up on your knowledge of Python libraries and deployment processes, as well as your understanding of observability tools like Prometheus and Grafana.
How to prepare for a job interview at Codesearch AI
✨Showcase Your Technical Skills
Be prepared to discuss your experience with ML engineering, particularly in integrating LLMs into enterprise environments. Bring examples of your past projects and be ready to explain the technical challenges you faced and how you overcame them.
✨Demonstrate a Builder Mindset
Highlight your ability to work with ambiguous specifications and early-stage infrastructure. Share instances where you've iterated quickly on projects or adapted to changing requirements, as this role requires a proactive and flexible approach.
✨Communicate Effectively
Strong communication skills are essential for this position. Practice explaining complex technical concepts in simple terms, as you'll need to collaborate with both technical and non-technical team members. Be clear and concise in your responses during the interview.
✨Research the Company and Its Technology
Familiarise yourself with Codesearch AI and their agentic AI platform. Understanding their technology and the problems they aim to solve will help you tailor your answers and demonstrate genuine interest in the role and the company.
