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 Role

We 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 Do

• Production ML Systems: Design, implement, and maintain large-scale ML pipelines for training and online inference. Ensure low latency, high availability, and cost efficiency
• Model Development: Implement deep learning models for ranking, retrieval, multi-task learning, and LLM applications. Work on features, embeddings, and model architectures
• GenAI Engineering: Build RAG pipelines, fine-tuning workflows, and LLM serving infrastructure. Integrate LLMs into recommendation and search to improve relevance and personalization
• Performance Optimization: Profile and optimize training and inference speed. Apply quantization, distillation, batching, and GPU optimization using tools like vLLM, TensorRT, or Triton
• Experimentation: Build A/B testing frameworks for ML models. Analyze experiment results and iterate based on online metrics
• Data & Feature Engineering: Develop real-time and batch feature pipelines using Spark, Flink, Kafka. Maintain feature stores and ensure data quality
• Infrastructure: Improve ML platform tooling: model registry, experiment tracking, CI/CD for ML, monitoring, and alerting

Minimum Qualifications

• Education: BS/MS in Computer Science, Engineering, or related technical field
• Experience: Software or ML engineering experience, with 1+ years shipping ML models to production
• Programming: Strong proficiency in Python, Go, or C++. Solid software engineering skills: data structures, algorithms, system design
• ML Frameworks: Hands-on experience with PyTorch or TensorFlow. Familiar with Hugging Face, XGBoost, LightGBM
• Data & Infra: Experience with distributed data processing: Spark, Hive, Flink. Knowledge of Docker, Kubernetes, and cloud: AWS/GCP/Azure
• ML Fundamentals: Understanding of recommendation systems, NLP, or computer vision. Familiar with training, evaluation, and deployment workflows
• Communication: Ability to work with cross-functional teams and explain technical trade-offs

Preferred Qualifications

• Experience with large-scale recommender systems, search ranking, or ads CTR/CVR prediction
• Built or optimized LLM inference services: RAG, agents, fine-tuning pipelines, vector databases
• Knowledge of GPU programming, CUDA, or inference optimization
• Contributions to ML infrastructure: feature store, model serving, workflow orchestration
• Experience in e-commerce, social media, or marketplace companies
• Familiarity with online learning, reinforcement learning, or multi-modal models