At a Glance
- Tasks: Join us to design and deploy cutting-edge AI/ML models for the rental housing market.
- Company: Letly is an innovative fintech platform revolutionising rental workflows with AI technology.
- Benefits: Enjoy a competitive salary, equity options, and opportunities for merit-based progression.
- Why this job: Be part of a dynamic team tackling real-world challenges with AI in a high-impact role.
- Qualifications: Strong software engineering skills and experience with ML frameworks are essential.
- Other info: This is an entry-level, full-time position based in Greater London.
The predicted salary is between 28800 - 42000 £ per year.
Letly is an AI-Native vertical fintech platform focused on the rental housing market. We are automating rental workflows end-to-end and building a fintech platform underneath to manage the trillions of dollars spent on rent globally. We recently closed a pre-seed round and are hiring one talented AI/ML engineer with a strong software engineering background and a passion for deploying AI/ML models into real-world, production-grade applications.
Apply if:
- You have strong foundational software engineering knowledge, including data structures, algorithms, system design, and OOP.
- You have advanced knowledge of LLM architectures and ML/DL frameworks (e.g. TensorFlow, PyTorch, LangChain, Keras, scikit-learn).
- You’re ready to design, deploy and maintain production-grade Machine Learning systems.
- You’re willing to champion best practices in code quality, testing, observability and MLOps.
- You have experience with MLOps tools and practices (CI/CD, Docker, Kubernetes) and cloud platforms (GCP, AWS, or Azure).
- You’re a smart, intense, and focused individual willing to build things efficiently in a close team.
- You want to tackle large technical hurdles, and build first-of-its-kind software using AI.
You will:
- Write high-quality, maintainable, well-documented, and tested code, adhering to software engineering best practices.
- Design, implement, and deploy production-grade AI/ML models to address various platform needs (including NLP and OCR).
- Optimise AI models and associated systems for performance, scalability, and cost-effectiveness in a production environment.
- Implement and manage the infrastructure for MLOps, including fine-tuning, deployment, monitoring and versioning.
- Develop robust data pipelines for ingestion, cleaning, model training, and continuous deployment.
- Build retrieval-aware repositories for model training, evaluation, and real-time context-rich inference.
- Collaborate closely with the software engineers to integrate AI models seamlessly into the platform architecture using APIs.
- Be a key part of a high-performance, engineering and product-led company with a high degree of autonomy and impact.
Compensation:
- Competitive salary and meaningful equity in the company.
- Annual performance-based compensation (cash and equity).
- Opportunity for true meritocratic progression in role and compensation.
Stand-up the rule-evaluation core of the compliance microservice: JSONLogic/CEL engine to load rule set from Mongo and return pass/fail result. Wire a Pub/Sub sink to integrate with the rest of the system, emitting ComplianceDecision events, and land these in BigQuery for analytics. Light-up observability – deploy OpenTelemetry traces + Grafana dashboards, and config alerts for latency, failure %, override rate.
Founding Machine Learning Engineer (Greater London) employer: Letly
Contact Detail:
Letly Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Founding Machine Learning Engineer (Greater London)
✨Tip Number 1
Familiarise yourself with the specific AI/ML frameworks mentioned in the job description, such as TensorFlow and PyTorch. Having hands-on experience with these tools will not only boost your confidence but also demonstrate your readiness to tackle the challenges at Letly.
✨Tip Number 2
Engage with the latest trends in MLOps and cloud platforms like GCP or AWS. Being well-versed in CI/CD practices and containerisation tools like Docker can set you apart from other candidates and show that you're prepared for a production-grade environment.
✨Tip Number 3
Network with professionals in the fintech and AI sectors. Attend meetups or webinars to connect with people who work in similar roles. This could lead to valuable insights about the company culture at Letly and potentially even referrals.
✨Tip Number 4
Prepare to discuss real-world applications of AI/ML models during your interview. Think of examples where you've successfully deployed models or optimised systems, as this will showcase your practical experience and problem-solving skills relevant to Letly's needs.
We think you need these skills to ace Founding Machine Learning Engineer (Greater London)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your strong foundational software engineering knowledge, particularly in data structures, algorithms, and system design. Emphasise your experience with LLM architectures and ML/DL frameworks like TensorFlow and PyTorch.
Craft a Compelling Cover Letter: In your cover letter, express your passion for deploying AI/ML models into real-world applications. Mention specific projects or experiences that demonstrate your ability to design, deploy, and maintain production-grade Machine Learning systems.
Showcase MLOps Experience: If you have experience with MLOps tools and practices, be sure to include this in your application. Discuss your familiarity with CI/CD, Docker, Kubernetes, and cloud platforms like GCP, AWS, or Azure, as these are crucial for the role.
Highlight Collaboration Skills: Let them know about your ability to work closely with software engineers. Provide examples of how you've integrated AI models into existing architectures using APIs, as collaboration is key in this role.
How to prepare for a job interview at Letly
✨Showcase Your Technical Skills
Be prepared to discuss your foundational software engineering knowledge, including data structures and algorithms. Highlight your experience with ML/DL frameworks like TensorFlow or PyTorch, and be ready to explain how you've deployed AI/ML models in real-world applications.
✨Demonstrate MLOps Knowledge
Since the role involves managing MLOps infrastructure, make sure to talk about your experience with CI/CD, Docker, and Kubernetes. Discuss any projects where you implemented these tools to streamline deployment and monitoring of machine learning systems.
✨Prepare for Problem-Solving Questions
Expect technical questions that assess your problem-solving abilities. Practice coding challenges related to system design and optimisation of AI models, as these are crucial for the role. Use platforms like LeetCode or HackerRank to sharpen your skills.
✨Emphasise Collaboration and Communication
This position requires close collaboration with software engineers. Be ready to share examples of how you've worked in teams, integrated AI models into existing architectures, and communicated complex technical concepts to non-technical stakeholders.