Machine Learning Engineer

Are you passionate about building great products? Do you want to redefine the way travellers explore the world? Keen to be part of this growth journey with a bunch of amazing people? Then Pelago is the place for you!

We are looking for ambitious and motivated talents who are excited about staying on the cutting edge of Technology and always keen on innovating new ways to drive growth and taking our startup to new heights.

WHO ARE WE? 

Pelago is a travel experiences platform created by Singapore Airlines Group. Think of us as a travel magazine that you can book - highly curated, visually inspiring, with the trust and quality of Singapore Airlines. We connect you with global, local cultures and ideas so you can expand your life.

We are a team of diverse, passionate, empowered, inclusive, authentic and open individuals who share the same values and strive towards a common goal!

WHAT CAN WE OFFER YOU?  

- A unique opportunity to take end-to-end ownership of your workstream to deliver real value to users. 

- Platforms to solve real user problems concerning travel planning & booking with innovative products/services.

- An amazing peer group to work with, and the ability to learn from the similarly great minds around you. 

- An opportunity to be an integral part of shaping the company’s growth and culture with a diverse, fun, and dynamic environment with teammates from different parts of the world.

- Competitive compensation and benefits - including work flexibility, insurance, remote working and more!

WHAT WILL YOU DO 

As a Machine Learning Engineer at Pelago, you will be a critical member of our Data Science team, focused on enhancing our platform’s search and recommendation capabilities. Your responsibilities will include:

Develop & Deploy ML Models

- Design, implement, and optimize machine learning models to power personalized travel search and recommendation systems.

- Leverage retrieval-based techniques to improve the relevance and accuracy of search results.

Integration & Production

- Integrate ML solutions seamlessly with our existing infrastructure using tools such as Elasticsearch to enable scalable and efficient indexing and retrieval.

- Deploy end-to-end ML pipelines, ensuring models perform reliably in production environments.

Collaboration Within the Data Science Team

- Work closely with data scientists, analysts, and other ML engineers to share insights, validate hypotheses, and continuously enhance model performance.

- Contribute to cross-functional projects by providing technical expertise and data-driven recommendations that align with business goals.

Research & Innovation

- Stay updated with emerging trends in machine learning, particularly in LLM Retrieval-Augmented Generation (RAG) frameworks, to continuously enhance our algorithms.

- Experiment with and iterate on novel techniques to drive personalized user experiences and innovative search functionalities.

Communication & Stakeholder Engagement

- Collaborate with product, engineering, and data analytics teams to translate complex business challenges into effective technical solutions.

- Clearly communicate model performance metrics and insights to both technical and non-technical stakeholders.

WHAT EXPERTISE ARE MUST HAVES FOR THIS ROLE?

- Bachelor’s degree in Computer Science, Engineering, or a related field.

- 2-3 years of hands-on experience in machine learning engineering, specifically in building retrieval search and recommendation systems.

- Proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, or similar.

- Practical experience with search technologies, especially Elasticsearch, to support scalable data retrieval.

- Strong analytical and problem-solving skills with the ability to work with large datasets and complex algorithms.

WHAT EXPERTISE ARE GOOD TO HAVE?

- Exposure to LLM Retrieval-Augmented Generation (RAG) frameworks and state-of-the-art techniques in natural language processing.

- Experience in a travel or consumer-facing digital platform.

- Familiarity with cloud-based ML platforms (AWS, GCP, or Azure) and deployment practices.

- Additional experience in data processing and feature engineering to enhance model performance.

If you’re as excited as we are in this journey, do apply directly with a copy of your full resume. We'll reach out to you as soon as we can!