Machine Learning Engineer

Our clients ML Platform team builds the infrastructure and models that power personalized experiences for millions of users across our clients e-commerce and content ecosystem. They work on large-scale recommendation, search, ads ranking, and GenAI applications similar to other social-commerce platforms.Our clients engineers own the full ML lifecycle: data, training, serving, and experimentation at 10M+ QPS.The RoleWe are hiring a Machine Learning Engineer to build and optimize production ML systems. You will focus on one of these areas based on team fit: Feed Recommendation, Search Relevance, Ads Ranking, or GenAI Applications.You will collaborate with ML Scientists, Backend Engineers, and Product to ship models that directly move metrics like engagement, conversion, and GMV.What You’ll DoProduction ML Systems: Design, implement, and maintain large-scale ML pipelines for training and online inference. Ensure low latency, high availability, and cost efficiencyModel Development: Implement deep learning models for ranking, retrieval, multi-task learning, and LLM applications. Work on features, embeddings, and model architecturesGenAI Engineering: Build RAG pipelines, fine-tuning workflows, and LLM serving infrastructure. Integrate LLMs into recommendation and search to improve relevance and personalizationPerformance Optimization: Profile and optimize training and inference speed. Apply quantization, distillation, batching, and GPU optimization using tools like vLLM, TensorRT, or TritonExperimentation: Build A/B testing frameworks for ML models. Analyze experiment results and iterate based on online metricsData & Feature Engineering: Develop real-time and batch feature pipelines using Spark, Flink, Kafka. Maintain feature stores and ensure data qualityInfrastructure: Improve ML platform tooling: model registry, experiment tracking, CI/CD for ML, monitoring, and alertingMinimum QualificationsEducation: BS/MS in Computer Science, Engineering, or related technical fieldExperience: Software or ML engineering experience, with 1+ years shipping ML models to productionProgramming: Strong proficiency in Python, Go, or C++. Solid software engineering skills: data structures, algorithms, system designML Frameworks: Hands-on experience with PyTorch or TensorFlow. Familiar with Hugging Face, XGBoost, LightGBMData & Infra: Experience with distributed data processing: Spark, Hive, Flink. Knowledge of Docker, Kubernetes, and cloud: AWS/GCP/AzureML Fundamentals: Understanding of recommendation systems, NLP, or computer vision. Familiar with training, evaluation, and deployment workflowsCommunication: Ability to work with cross-functional teams and explain technical trade-offsPreferred QualificationsExperience with large-scale recommender systems, search ranking, or ads CTR/CVR predictionBuilt or optimized LLM inference services: RAG, agents, fine-tuning pipelines, vector databasesKnowledge of GPU programming, CUDA, or inference optimizationContributions to ML infrastructure: feature store, model serving, workflow orchestrationExperience in e-commerce, social media, or marketplace companiesFamiliarity with online learning, reinforcement learning, or multi-modal models