AI/ml

We are building an advanced content intelligence platform that analyzes top-ranking SERP content, extracts semantic patterns, identifies topical gaps, and generates actionable recommendations for content optimization. We are looking for an experienced NLP / Machine Learning Engineer to design and develop semantic analysis algorithms using embeddings, clustering, topic extraction, and large language models. This role is highly technical and focused on applied NLP, semantic search, information retrieval, and content intelligence systems. --- # What You’ll Work On You will design systems that: - Analyze and compare ranking pages on Google SERPs - Extract semantic topics, entities, and content structures - Detect missing topical coverage in content - Build semantic similarity and embedding pipelines - Develop clustering and topic modeling systems - Generate intelligent content recommendations - Improve content relevance and search intent matching - Build scalable NLP infrastructure for large-scale analysis --- # Responsibilities - Design and implement NLP algorithms for semantic content analysis - Build embedding-based similarity systems - Develop topic extraction and clustering pipelines - Create content gap detection algorithms - Work with transformer models and vector databases - Optimize ranking and relevance scoring systems - Improve semantic understanding of long-form content - Evaluate algorithm quality using real-world SEO performance metrics - Collaborate with product and engineering teams to productionize ML systems --- # Required Skills ## Strong NLP / ML Fundamentals - Semantic embeddings - Transformer architectures - Text similarity systems - Topic modeling - Clustering algorithms - Information retrieval ## Experience With - Python - PyTorch or TensorFlow - Sentence Transformers - HuggingFace ecosystem - Vector databases (pgvector, Pinecone, Weaviate, FAISS, Milvus) - Scikit-learn - spaCy ## Knowledge Of - Embedding models - Cosine similarity - HDBSCAN / K-Means clustering - BERTopic - Semantic search - LLM prompting and evaluation - Content extraction and parsing --- # Nice to Have - SEO or search engine knowledge - Experience building recommendation systems - Experience with large-scale data pipelines - Experience with ranking systems / retrieval systems - Knowledge graphs or entity extraction experience - Experience with OpenAI / Claude APIs - Distributed ML systems experience --- # Example Problems You’ll Solve - How do we identify missing semantic topics in a page? - How can we compare SERP intent across competitors? - How do we cluster long-form content into meaningful topic groups? - How do we measure topical depth instead of keyword density? - How can embeddings be used to improve content recommendations? - How do we generate explainable NLP insights? --- # Ideal Candidate You are someone who: - Understands modern NLP deeply - Thinks in systems and semantic relationships - Can balance research and practical engineering - Builds scalable, production-ready ML systems - Cares about precision, relevance, and explainability - Has experience turning ML concepts into real products --- # Tech Stack Examples of technologies we use: - Python - FastAPI - PostgreSQL + pgvector - Sentence Transformers - OpenAI embeddings - BERTopic - spaCy - PyTorch - Docker - Kubernetes --- # Compensation Competitive salary based on experience. Remote-friendly / hybrid options available. --- Please send: - Resume / LinkedIn - Relevant NLP or ML projects - Examples of semantic search, NLP, or recommendation systems you’ve built - GitHub profile (preferred) - Short explanation of a complex NLP system you designed