Machine Learing Engineer

Your Skillset

• Strong experience designing, building, and maintaining end-to-end machine learning systems in production.
• Deep understanding of ML algorithms, embeddings, retrieval systems, and evaluation methodologies.
• Strong experience with large language models (LLMs), fine-tuning, inference optimization, and agent frameworks.
• Expertise in ML infrastructure, including feature stores, vector databases, model serving, and real-time inference pipelines.
• Strong Python skills and experience with PyTorch, TensorFlow, FastAPI, NumPy, scikit-learn, and data processing frameworks.
• Experience with scalable data pipelines (batch + streaming), including tools like Spark, Kafka, or similar.
• Experience implementing ML solutions such as recommendation engines, ranking models, and personalization systems.
• Solid understanding of statistical analysis (A/B testing, experimentation, causal inference).
• Ability to work closely with engineering teams to productionize ML models with reliability, monitoring, and CI/CD best practices.

You’re the Type Who

• Writes clean, reusable, and well-documented code for ML pipelines and distributed systems.
• Enjoys experimenting with the latest AI frameworks, ML optimizations, and deployment techniques, turning ideas into fast prototypes.
• Partners closely with engineering, product, and data teams to ship ML-powered features that directly impact the user experience.
• Thrives in fast-paced, early-stage environments where experimentation, ownership, and rapid iteration matter.
• Thinks analytically and solves complex technical problems with efficiency and creativity.
• Stays curious about the latest advances in ML infrastructure, LLM optimization, retrieval-augmented systems, and real-time personalization.
• Thrives in ambiguity and builds scalable structure from chaos.