AI Scientist - Content & Growth

Descriptions :

Design and optimize large-scale recommendation algorithms

to enhance personalized user experiences across feeds, content

hubs, and interactive touchpoints.

Build

intelligent content growth pipelines, including real-time hot topic

detection, viral content diffusion modeling, and trending topic

amplification.

Develop and integrate

AIGC-aware recommendation systems, enabling dynamic ranking and

generation strategies based on user preferences and market

signals.

Apply state-of-the-art retrieval,

ranking, and re-ranking models to refine recommendation precision,

diversity, and freshness.

Leverage sequence

models (Transformers, RNNs) and graph-based methods to model user

behavior over time and across content types.

Employ multi-modal learning (text, image, video, social

graphs) to improve understanding of content and boost

personalization effectiveness.

Collaborate

cross-functionally with product, data, and infrastructure teams to

define and drive content growth strategies aligned with business

objectives.

Run large-scale A / B tests, perform

causal inference and behavioral analytics to quantify impact, guide

iteration, and scale success.

Contribute to

system architecture, model deployment pipelines, and performance

optimization for real-time inference at scale.

Stay at the frontier of recommendation, generative AI,

and content intelligence research, translating innovation into

production

impact.

Requirements :

5+ years of experience in recommendation systems, content

AI, or growth-focused machine learning.

Proven

track record in developing large-scale personalized recommendation

engines using deep learning, collaborative filtering, or hybrid

models.

Hands-on experience with hot topic

mining, entity co-occurrence graph modeling, or event-based content

surfacing is a strong plus.

Solid

understanding of retrieval-ranking architectures, cold-start

mitigation, and user lifecycle-based personalization.

Experience with deep learning frameworks (e.g.,

TensorFlow, PyTorch), vector search (e.g., FAISS, Milvus), and

knowledge-enhanced models.

Proficiency in big

data processing (Spark, Hive, Hadoop) and distributed computing

frameworks.

Strong problem-solving and

communication skills; ability to drive cross-functional

collaborations.

Passion for content

ecosystems, user growth loops, and delivering measurable impact

through intelligent systems.

Background in

AIGC integration, content generation ranking, or LLM-based user

interaction modeling.

Experience working in

high-growth environments, social media, or consumer-facing

recommendation platforms.

Familiarity with

user engagement funnels, content virality metrics, and

experimentation platforms (e.g., Optimizely, internal A / B

infra).

Research exposure in causal inference,

reinforcement learning, or multimodal

retrieval.