Senior Software Engineer, AI/ML, Geo and Gemini App

• Build context engineering pipelines, agentic workflows with tool usage, and robust evaluation frameworks.
• Analyze model behavior, creating high-quality evaluation datasets to identify weaknesses and guide performance improvements.
• Enhance serving and evaluation infrastructure to power new user-facing features.
• Act as a technical bridge between engineering, product, UX, and research teams to translate user needs into valuable features.
• Analyze user metrics and model outputs to enhance personalization and overall system helpfulness.

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 5 years of experience developing C++ backend infrastructure components within distributed systems.
• 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
• Experience with Large Language Models (LLMs) and Generative AI concepts (e.g., prompt engineering, retrieval-augmented generation (RAG)).
• Experience with model quality optimization using methods such as loss analysis or quality hillclimbing.

Preferred qualifications:

• 5 years of experience with data structures and algorithms.
• 1 year of experience in a technical leadership role.
• Experience working in fast-paced, startup-like environments with rapidly changing priorities.
• Experience in data analysis, experimental design, and statistical methods.
• Experience with common ML frameworks (e.g., TensorFlow, JAX, PyTorch).
• Familiarity with user metrics logging and A/B testing for software products.

• Bachelor’s degree or equivalent practical experience.
• 5 years of experience developing C++ backend infrastructure components within distributed systems.
• 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
• Experience with Large Language Models (LLMs) and Generative AI concepts (e.g., prompt engineering, retrieval-augmented generation (RAG)).
• Experience with model quality optimization using methods such as loss analysis or quality hillclimbing.