ML Engineer

𝗔𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗷𝗼𝗯
This role is for one of our client companies — a VC-backed Social networking startup that has raised $10.5M USD in funding.
𝗦𝗮𝗹𝗮𝗿𝘆: Up to 40LPA
Apply once and, if selected, get access to up to 20 remote and onsite interview opportunities.
🚀 𝗪𝗵𝗮𝘁 𝗪𝗲'𝗿𝗲 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴
CodeRound AI matches the top 5% tech talent with the fastest-growing, VC-funded AI startups across Silicon Valley and India.
Top-tier product startups across the US, UK, EU, UAE, and India have hired top engineers through CodeRound.
🚀 𝗠𝗟 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 (2+ 𝗬𝗲𝗮𝗿𝘀 𝗼𝗳 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲)
We’re looking for a hands-on Machine Learning Engineer to build and scale the core real-time recommendation, ranking, and personalization engines powering a next-generation social platform. If you want to own the entire ML lifecycle from modeling to production, let’s connect.
🧩 𝗪𝗵𝗮𝘁 𝗬𝗼𝘂'𝗹𝗹 𝗗𝗼
Design, build, and deploy matchmaking, recommendation, ranking, and personalisation systems
Develop a real-time adaptive recommendation engine that learns from user interactions and other behavioural signals
Build ranking algorithms that deliver highly personalised and curated user experiences
Develop user embeddings, similarity models, and graph-based match-scoring frameworks
Research and implement cold-start solutions for users and recommendations with limited data
Experiment with collaborative filtering, deep retrieval, learning-to-rank, embeddings, ANN search, and LLM-based approaches
Own the full lifecycle of recommendation models, from problem definition and modelling to deployment and monitoring
Establish and improve offline and online evaluation frameworks, A/B tests, and recommendation metrics
✅ 𝗬𝗼𝘂'𝗿𝗲 𝗮 𝗚𝗿𝗲𝗮𝘁 𝗙𝗶𝘁 𝗜𝗳 𝗬𝗼𝘂
2–4 years of experience working on recommendation systems, personalisation, search, feed ranking, or related ML problems at scale
Strong hands-on experience building recommendation, ranking, or personalisation models
Experience with a B2C product, ideally in social, ecommerce, fashion, dating, gaming, video, or other consumer platforms
Understanding of recommendation techniques such as collaborative filtering, learning-to-rank, embeddings, similarity models, and retrieval systems
Experience with end-to-end ML pipelines, including model development, experimentation, and/or production deployment
Strong understanding of offline and online evaluation, A/B testing, and metric alignment
✨ 𝗪𝗵𝘆 𝗝𝗼𝗶𝗻 𝗨𝘀?
Own the entire machine learning pipeline from inception to production deployment.
Build core AI features for millions of users on our platform.
Fast-track your career in a well-funded, rapidly growing startup environment.
Collaborate daily with top-tier engineers driven to shape social connection.

Work arrangement
Yes