ML Engineer

Job Title

Pay Range $47hr - $52hr

Requirement/Must Have

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related quantitative field.
• 1+ years of hands-on experience in machine learning, data science, search relevance, or ranking systems.
• Proven expertise in Python and ML frameworks (MLFlow, TensorFlow, PyTorch, Scikit-learn, or equivalent).
• Strong background in statistical analysis, data exploration, and working with large-scale datasets.
• Experience with feature engineering, data preprocessing, and data manipulation libraries (Pandas, NumPy, Spark).
• Demonstrated experience building or working with ranking models (learning-to-rank, neural ranking, or similar).
• Experience with semantic search, embedding, or dense retrieval methods.
• Deep understanding of search engines (Elasticsearch, Solr, OpenSearch), lexical search algorithms (BM25), information retrieval concepts, search relevance tuning, tokenization, stemming, and query parsing.
• Experience with MLOps practices and tools (model versioning, experiment tracking, pipeline orchestration).
• Proficiency in SQL and querying large datasets.
• Strong problem-solving and analytical skills with the ability to think critically about complex search and ranking problems.
• Excellent communication skills; ability to explain ML and search concepts to both technical and non-technical stakeholders.
• Ability to collaborate with cross-functional teams.

Responsibilities

• Design, train, and evaluate ranking models (learning-to-rank, neural networks, embedding-based approaches) to optimize search relevance and personalization.
• Analyze search query logs, evaluate user behavior data to identify opportunities for relevance improvements and inform ranking strategies.
• Develop and engineer features from search, product, and user data to power ML models and improve ranking performance.
• Implement semantic search for improved product Client across chemistry and life science domains.
• Optimize Elasticsearch/Lucene configurations, including tokenization, stemming, query parsing, and lexical search algorithms (BM25) to work in concert with ML models.
• Build and maintain end-to-end ML pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment using MLOps best practices.
• Develop personalized ranking strategies that adapt to user segments, query intent, and business objectives; integrate collaborative filtering and content-based approaches.
• Monitor search and ML model performance metrics in production; identify drift and continuously improve models based on new data and domain insights.

Nice to Have

• Experience in eCommerce Search.
• Knowledge of microservices architectures, event-driven systems, and CI/CD Pipelines.
• Experience in training & fine-tuning the models.
• Experience with large language models (LLMs) or prompt engineering.
• Experience with semantic indexing and dense vector search (e.g., vector databases).
• Experience in Search Metrics evolution.
• Familiarity with data visualization and analytics tools (Tableau, Looker, etc.).
• Background in NLP, information retrieval, or computational linguistics.
• Experience on search or ML-focused teams.