Senior Staff Algorithm Engineer Recommendation

Framework Ventures

New Delhi

On-site

INR 9,000,000 - 18,000,000

Full time

14 days+

Get more replies from employers

Send a job-specific resume in minutes.

Benefits offered by this job

Competitive compensation

Education subsidy for growth

Team events and wellness programs

Wellness and meal allowance

Healthcare schemes for employees and +

Job summary

OKX is seeking a senior leader to direct the next generation of its social feed recommendation system. You will evolve a content feed into a unified engine surfacing content and platform features for tens of millions of users and trading activity.

You will drive a multi-year roadmap, unify intent across content, features and search, and pioneer agent-based capabilities to fulfill user intent proactively with low latency and high quality recommendations.

Qualifications

Master’s degree in CS / Math from a top university.
8+ years in recommendation / search with 5+ years owning pipelines at large scale (10M+ DAU).
Experience designing unified intent representations across content, feature, and search.
Deep understanding of attention mechanisms in sequential modeling and related losses (ListMLE / Softmax).
Expert in MMoE / PLE / ESMM and bridging offline metrics with online KPIs.
Hands-on uplift modeling and bias correction techniques.
Familiarity with RLHF / DPO in recommender systems.
Experience with LLM Agent frameworks and tool use for proactive intents.

Responsibilities

Elevate the Ranking System with iterative improvements and measurable impact.
Unify User Understanding across content, features, and search domains.
Define the Technical Roadmap from Transformer-based ranking toward generative recommendations.
Pioneer the Agent Paradigm by integrating recommendation and search with an LLM Agent framework.

Skills

Master’s degree & top university

8+ years in recommendation/search

10M+ DAU experience

Transformer & ranking core

Multi-Task training (MMoE/PLE/ESMM)

Uplift modeling & bias correction

RLHF / DPO in recsys

LLM Agent frameworks & tool use

Large-scale distributed training (10B+

Education

Master’s degree in CS/Math

Top university credential

Tools

Flink

Kafka

LLM / Agent tooling

Job description

Who We Are

At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me, Do the Right Thing, and Get Things Done. These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er. OKX is part of OKG, a group that brings the value of Blockchain to users around the world, through our leading products OKX, OKX Wallet, OKLink and more.

About the Role

You will own the technical direction of OKX's next-generation social feed recommendation system — evolving it from a content feed into a unified recommendation engine that surfaces both content and platform features. Your decisions directly shape the experience of tens of millions of users and drive platform trading conversion.

Responsibilities

Elevate the Ranking System — Drive continuous ranking model iteration with measurable impact on user retention and trading conversion
Unify User Understanding — Build a cross-domain intent framework spanning content consumption, feature usage, and search, shifting the system from "what users clicked" to "what users are trying to do"
Define the Technical Roadmap — Chart and execute a 12-24 month evolution from Transformer-based ranking toward generative recommendation (sequence generation + preference alignment)
Pioneer the Agent Paradigm — Integrate recommendation and search capabilities into an LLM Agent framework, enabling proactive intent fulfillment rather than passive content delivery

Requirements

Background — Master's or above in CS / Math from a top university; 8+ years of experience with 5+ years in core recommendation / search roles; track record of owning end-to-end recommendation pipelines at 10M+ DAU scale
User Intent & Profiling (Core) — Experience designing unified intent representations across heterogeneous domains (content / feature / search); ability to fuse real-time behavioral signals with long-term stable preferences; hands-on experience with tiered user profile systems (cold-start - interest exploration - stable preference)
Transformer & Ranking (Core) — Deep understanding of Attention mechanisms in sequential behavior modeling and their limitations (DIN / SIM / HSTU evolution); ability to propose independent solutions under engineering constraints; proficiency in Listwise losses (ListMLE / Softmax Loss) and joint multi-candidate ranking
Multi-Task Training (Core) — Expert-level knowledge of MMoE / PLE / ESMM and gradient conflict identification and mitigation; ability to design composite loss function frameworks from scratch; proven methodology for bridging offline metrics (AUC / NDCG) and online business KPIs
Business Attribution (Core) — Hands-on Uplift Modeling experience; proficiency in Position / Selection Bias correction and prediction probability Calibration
Generative Recommendation (Strong Plus) — Understanding of Semantic Tokenization (FSQ / RQ-VAE) and conditional sequence generation; working-level knowledge of RLHF / DPO applied to recommendation systems
Recommendation & Search Agent (Strong Plus) — Engineering experience with LLM Agent frameworks (Tool Use / ReAct); ability to define the collaboration boundary between Agent-based and traditional recommendation; experience designing systems that translate natural language intent into structured retrieval requests
Engineering — Large-scale distributed training (10B+ parameter models); real-time feature engineering (Flink / Kafka); inference optimization under strict latency SLA
Bonus First-author publication at RecSys / KDD / WWW | Bandit / RL production deployment | Background in fintech / crypto

Perks & Benefits