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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 groundbreaking startups, creating transformative AI technology.
- Benefits: Enjoy flexible work options, competitive salary, health benefits, and a professional development budget.
- 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 and 5+ years in ML engineering or related roles required.
- Other info: Work closely with accomplished researchers and engineers from top tech companies.
The predicted salary is between 43200 - 72000 £ 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's 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're now expanding their core team to bring this revolutionary product to market.
The Role
They're 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 ML community, especially those who have experience in enterprise environments. Engaging in discussions or attending meetups can provide insights into the challenges faced in the industry and how you can contribute.
✨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 tools will set you apart from other candidates.
✨Tip Number 4
Prepare to discuss your approach to building scalable systems and handling failure scenarios. The ability to think critically about the agent lifecycle and observability will be crucial in this role, so be ready to share your thoughts and experiences.
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 explain how you can contribute to their innovative platform.
Showcase Technical Skills: In your application, emphasise your proficiency in Python and any relevant ML libraries. If you have experience with cloud platforms or containerization, make sure to include examples of how you've used these technologies in past roles.
Demonstrate Problem-Solving Ability: Provide examples of how you've tackled complex problems in previous positions. Highlight your builder mindset and ability to work with ambiguous specifications, as this aligns well with the company's needs for fast iteration and innovation.
How to prepare for a job interview at Codesearch AI
✨Showcase Your Technical Expertise
Be prepared to discuss your experience with ML engineering, particularly in integrating LLMs into enterprise environments. Highlight specific projects where you've built scalable systems or improved existing prototypes.
✨Demonstrate a Builder Mindset
Emphasise your comfort with ambiguous specifications and early-stage infrastructure. Share examples of how you've iterated quickly on projects and adapted to changing requirements.
✨Communicate Clearly
Strong communication skills are essential for this role. Practice explaining complex technical concepts in simple terms, as you'll need to collaborate with both technical and non-technical team members.
✨Prepare for Problem-Solving Scenarios
Expect to face hypothetical scenarios related to agent lifecycle management, observability, and failure handling. Think through your approach to these challenges and be ready to discuss your thought process.
