ML Engineer

• 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 discovery 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.

• 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., * 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.

Our Benefits Include:

• Medical, Dental, and Vision Insurance
• 401(k) Retirement Plan
• Health Savings Account (HSA)
• Disability Insurance (Short-Term and Long-Term)
• Life and AD&D Insurance
• Paid Sick Leave (where required by applicable state or local law)
• Supplemental Insurance Plans
• Identity Theft Protection
• Pet Insurance
• Employee Wellness Programs
• Employee Assistance Program (EAP)
• Career Growth and Professional Development Opportunities

Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading technology staffing and workforce solutions company serving Fortune 500 companies, government agencies, and enterprise organizations across the United States and Canada. We deliver agile, scalable talent solutions across IT, engineering, life sciences, clinical, and professional staffing, powered by a high-performing recruitment engine operating across North America and Asia.