Machine Learning

Weekday AI (YC W21); Bengaluru; On-site; INR 1,200,000 - 2,000,000; Full time

Job summary

A technology-driven company is looking for a Machine Learning Engineer to enhance search relevance and recommendations. In this full-time role, you'll design and implement ML models, analyze large datasets, and collaborate with product managers and engineers to deliver high-performance search experiences. Candidates should possess strong skills in ML, NLP, and information retrieval, along with proficiency in Python and popular ML frameworks. This position is based in Bengaluru, Karnataka, India.

Qualifications

• 3+ years of experience building and deploying ML models in production environments.
• Strong understanding of search systems, information retrieval, and ranking algorithms.
• Hands-on experience with NLP techniques including embeddings, transformers, and text classification.

Responsibilities

• Design and implement ML models to improve search ranking, query understanding, and content retrieval.
• Develop and optimize algorithms for semantic search, personalization, and recommendation systems.
• Analyze large-scale datasets and logs to extract insights and improve search relevance.
• Collaborate with backend and infrastructure teams to productionize ML solutions.

Skills

Machine Learning

Natural Language Processing

Information Retrieval

Python

TensorFlow

PyTorch

scikit-learn

Spark

Hadoop

Education

Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science or a related field

Tools

Vector databases

Elasticsearch

OpenSearch

FAISS

Job description

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Job Description

This role is for one of the Weekday's clients. JobType: full‑time.
As a Machine Learning Engineer on the Search & Information Retrieval team, you will design, develop, and optimize machine learning solutions that enhance search relevance, ranking, and recommendations. You will work closely with product managers, data scientists, and engineers to deliver high‑performance, scalable search experiences that help users discover content efficiently. This role requires expertise in ML, NLP, and information retrieval, along with hands‑on experience deploying models in production.

Key Responsibilities

• Design and implement ML models to improve search ranking, query understanding, and content retrieval.
• Develop and optimize algorithms for semantic search, personalization, and recommendation systems.
• Analyze large‑scale datasets and logs to extract insights and improve search relevance.
• Build robust evaluation pipelines to measure model impact on business and user experience metrics.
• Collaborate with backend and infrastructure teams to productionize ML solutions in low‑latency, scalable environments.
• Experiment with and integrate state‑of‑the‑art NLP methods, embeddings, and large language models into search systems.
• Monitor model performance; retrain pipelines, and ensure accuracy and freshness of search results.
• Contribute to the development of search‑related data pipelines, features, and tools.

What Makes You a Great Fit

• Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or a related field.
• 3+ years of experience building and deploying ML models in production environments.
• Strong understanding of search systems, information retrieval, and ranking algorithms.
• Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or scikit‑learn.
• Hands‑on experience with NLP techniques, including embeddings, transformers, and text classification.
• Solid knowledge of retrieval models (BM25, vector search) and relevance evaluation metrics (NDCG, MAP).
• Experience with large‑scale distributed data processing tools (Spark, Hadoop).
• Familiarity with deploying ML models via APIs or microservices.
• Bonus: experience with vector databases and semantic search frameworks (Elasticsearch, OpenSearch, FAISS), personalization systems, A/B testing, and contributions to the ML/Search community.
• Strong problem‑solving skills, analytical thinking, and ability to collaborate in cross‑functional teams.
• Comfortable working in Agile development environments and managing multiple priorities.