Applied Scientist / ML Engineer (Search & Recommendations)
We are looking for a highly skilled Applied Scientist / Machine Learning Engineer to lead the innovation and development of our next-generation Search and Recommendation systems. The ideal candidate will have deep expertise in classical ML, Deep Learning, NLP, and advanced Transformer-based architectures, including BERT and modern Large Language Models (LLMs).
Key Responsibilities
Search & Recommendation Development
- Lead the end-to-end design, development, and deployment of search, personalization, and recommendation algorithms.
- Build systems that significantly enhance user experience and drive measurable business impact.
Transformer-Based Model Implementation
- Apply, fine-tune, and optimize models such as BERT, RoBERTa, and other encoder architectures for:
- Semantic search
- Relevance ranking
- Query understanding
- Embedding generation
Large Language Model (LLM) Innovation
- Research, prototype, and implement solutions using LLMs.
- Work on model selection, prompt engineering, LoRA-based fine-tuning, and quantization for efficient inference.
- Design and implement RAG (Retrieval-Augmented Generation) systems using vector databases and advanced retrieval pipelines.
ML Productionization (MLOps)
- Build, train, validate, and deploy machine learning models into scalable, low-latency production environments.
- Collaborate with engineering teams to ensure reliability, robustness, and maintainability.
Data Strategy & Feature Engineering
- Partner with Data Engineering to define datasets and develop innovative features for training and evaluation.
- Ensure data quality and consistency across search and recommendation pipelines.
Evaluation & Optimization
- Define and track KPIs such as NDCG, CTR, latency, perplexity, and other model metrics.
- Continuously iterate to improve model performance and system quality.
Essential Technical Qualifications
- MS/PhD in Computer Science, Data Science, Engineering, or equivalent experience.
- Expert-level Python skills; strong knowledge of ML/DL libraries (NumPy, Pandas, etc.) and solid software engineering practices.
- Deep experience with PyTorch or TensorFlow.
- Proven hands-on work with Transformer models (BERT, encoder-only models) for IR, NLU, or embedding generation.
- Practical experience with LLMs, including fine-tuning, deployment, and familiarity with frameworks such as Hugging Face, LangChain, and LlamaIndex.
- Strong foundational understanding of classical ML algorithms and statistical modeling.
- Direct experience building or optimizing search ranking systems, recommendation engines, dense retrieval, or vector-based search.
- Experience with cloud platforms (AWS, GCP, Azure) and MLOps tools such as MLFlow, Kubeflow, Docker, Kubernetes.
