Senior Machine Learning Engineer

Accountabilities:: Design, develop, deploy, and maintain machine learning components within real-time search serving systems, ensuring performance under strict latency requirements. Build query-time machine learning capabilities that improve search understanding, query interpretation, and the overall relevance of search results. Develop and scale machine learning and LLM data pipelines across large product catalogs, ensuring data freshness, processing efficiency, and cost optimization. Optimize machine learning models across different merchants, business verticals, languages, and geographical markets. Build and scale online features, model-serving infrastructure, and production systems that support high-volume search and discovery applications. Establish offline evaluation frameworks and safe production rollout processes, including baseline comparisons, A/B testing, fallback mechanisms, and latency and cost monitoring. Improve observability, monitoring, and diagnostic capabilities to identify performance issues and maintain the reliability of production machine learning systems. Collaborate with Search Quality and engineering teams to integrate advanced relevance and ranking models into production environments while meeting performance and cost targets. Analyze model and system performance, identify technical bottlenecks, and implement improvements across both machine learning and backend engineering components. Contribute to technical architecture discussions, influence engineering decisions, and promote scalable software development and machine learning best practices. Take end-to-end ownership of technical challenges, from problem analysis and solution design through implementation, deployment, and ongoing optimization. Requirements: Bachelor's or Master's degree in Computer Science or a related technical discipline, such as BE/B.Tech or ME/M.Tech, with a strong foundation in mathematics and programming. At least 5 years of professional experience building, deploying, and maintaining machine learning-driven systems in production environments. Excellent Python programming skills and practical experience with backend programming languages such as Go or Java. Strong software engineering expertise, including hands-on experience deploying and serving machine learning models and LLMs within scalable cloud environments. Experience working with cloud platforms such as AWS or Google Cloud Platform (GCP), particularly when managing systems with strict latency, scalability, and infrastructure cost requirements. Proven experience designing, developing, and maintaining large-scale data processing and machine learning pipelines. Strong analytical and problem-solving abilities, with the capacity to break complex challenges into machine learning and backend engineering components, investigate root causes, and identify effective solutions. Experience working across multiple customers, business domains, languages, or large-scale datasets. Working knowledge of search technologies and concepts, including query understanding, relevance ranking, autocomplete, autosuggest, and faceted search. Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow. Strong understanding of production machine learning systems, model evaluation, deployment strategies, and performance optimization. Excellent collaboration and communication skills, with the ability to work effectively across multidisciplinary engineering teams and contribute to technical discussions. Preferred Qualifications: Experience with search engines such as Solr, Elasticsearch, or similar technologies. Practical experience deploying vector search solutions in production using technologies such as Milvus or Qdrant. Experience building evaluation frameworks, observability tools, and explainability solutions for machine learning models, LLMs, and model-serving infrastructure. Familiarity with advanced AI-powered search, recommendation, and personalization technologies. A proactive mindset, strong technical ownership, and enthusiasm for exploring emerging machine learning technologies. Benefits: Flexible Working Arrangements: Enjoy flexible working hours and a virtual-first working environment, with access to international collaboration hubs. Professional Development Budget: Receive an annual education budget of $1,500 for books, courses, certifications, and other professional learning resources. Career Growth: Access personal development workshops, competency frameworks, mentorship, and leadership development programs. Performance-Based Compensation: Participate in company performance bonuses and share in organizational success. Equity Opportunities: Eligibility for Restricted Stock Units (RSUs) or stock options, depending on role, seniority, and location. Employee Referral Bonus: Earn referral rewards of up to $3,000 for successful candidate recommendations. Well-being Support: Access an Employee Assistance Program, professional counseling resources, and a Calm subscription for sleep and meditation. Additional Time Off: Benefit from quarterly DisConnect days, providing an additional day off to recharge and focus on personal activities. Paid Volunteering: Receive up to five paid days annually to participate in volunteering activities. Health and Wellness Activities: Opportunities to participate in sports, yoga, and meditation activities. Parental Support: Extended parental leave of up to 26 calendar weeks for primary caregivers. Global Team Culture: Participate in international company events and collaborate with colleagues across multiple continents. Recognition and Rewards: Enjoy employee recognition initiatives celebrating professional milestones and work anniversaries.