Staff AI Engineer

Overview

1. Full-System Implementation & Ownership

• End-to-End Capability: Possess the technical ability to build the entire AI product. While leveraging the team for speed, you are capable of implementing every component yourself, from database schema design to the inference engine.
• Lead Implementation: Code the most complex features and act as the technical backstop for the team. If a subsystem fails or a model fails to converge, you are the one who fixes it.
• Holistic Engineering: Ensure the entire system works harmoniously. You don’t just optimize the model—you optimize how the model interacts with the database, the network, and the user.

2. Technical Architecture & Strategy

• System Design: Design robust, scalable, and fault-tolerant AI architectures, ensuring all components built by the team fit together seamlessly.
• Technical Roadmap: Translate vague business requirements into concrete, executable engineering tasks.
• Standardization: Establish and enforce engineering standards, including coding styles, testing frameworks, and MLOps tools.

3. Technical Leadership & Force Multiplication

• Mentorship: Elevate Senior and Junior engineers. Code reviews focus not only on correctness, but also on teaching design patterns and architectural thinking.
• Code Review Authority: Act as the standard-bearer for code quality, preventing careless technical debt and ensuring a clean, maintainable codebase.

Experience

• 8–12 years of total technical experience.
• 4+ years of deep, hands-on experience building production AI systems.
• Proven track record of taking at least one complex AI product from concept to scale, owning the full technical lifecycle.

Technical Competencies

• Full-Stack AI Fluency: Expert in Python (PyTorch/TensorFlow) with strong proficiency in the surrounding ecosystem, including SQL, API design, Docker, and cloud infrastructure. Able to build both the “brain” and the “wrapper.”
• Architectural Vision: Ability to visualize the entire system and understand how decisions in the data pipeline impact frontend latency and user experience.
• Deep Implementation Skills: Comfortable writing complex custom loss functions, optimizing CUDA kernels, and debugging distributed system race conditions.
• Portfolio & Presence: Sharing GitHub and Hugging Face accounts is highly preferred. Active repositories, open-source contributions, or published models are a strong plus.

Professional Attributes

• Total Ownership: Take full responsibility for the success of the technical solution end-to-end.
• Pragmatic Expert: Balance “perfect code” with “shipping on time,” without ever compromising system stability.