
Round1
AI / ML Engineer
Gurugram3 - 10 years
50L - 80LCTC
26applicants
Interview details
9 mins, 5-6 questionsCloses on Jul 18
Meet your interviewer

Sunil Kumar
Recruiter
AI Voice
Get ready for your interview with Sunil Kumar, Recruiter at Round1
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Founding Machine Learning Scientist / Principal ML Engineer
Role Overview
We are seeking a hands-on, founding Machine Learning Scientist or Principal ML Engineer to design and deploy core recommendation and personalization systems. In this role, you will own the full lifecycle of machine learning solutions—from design to deployment—while establishing the foundation for scalable, real-time ranking infrastructure and shaping the technical direction and culture of a new product from the ground up.
Key Responsibilities
- Design, develop, and deploy recommendation, ranking, and personalization systems.
- Build real-time adaptive engines that learn from user interactions and other signals.
- Develop ranking and recommendation algorithms to deliver highly curated user experiences.
- Construct user embedding systems, similarity models, and graph-based scoring frameworks.
- Explore and implement solutions for cold-start and sparse data scenarios.
- Collaborate with data, product, and engineering teams to deliver impactful customer experiences.
- Deploy models to production using fast iteration loops, model registries, and observability tools.
- Contribute to building the ML engineering team and shaping its culture.
What We Look For
Skills:
- Expertise in recommendation, personalization, search, or ranking systems.
- 3–10 years of experience in developing consumer-facing products such as social, ecommerce, gaming, or media platforms.
- Familiarity with techniques like collaborative filtering, deep retrieval models (e.g., two-tower), learning-to-rank, embeddings with approximate nearest neighbor (ANN) search, and LLM approaches for sparse data.
- Proven ability to build and deploy end-to-end machine learning pipelines.
- Strong understanding of offline and online evaluation, A/B testing, and metric alignment.
Qualifications
- Required Experience: 3–10 years of hands-on work in personalization, recommendations, search, or ranking at scale.
- Bonus Points: Exposure to vector search, graph-based algorithms, LLM-based approaches, and building scalable real-time recommendation or ranking systems; experience addressing cold-start challenges and online experimentation infrastructure.
What We Offer
- Competitive compensation and benefits package
- Significant opportunities for professional growth and career development
- The chance to be a founding member of the ML team, directly influencing the product experience and technical culture of a new platform
- A collaborative and innovative work environment