AI Engineer

Deep familiarity with supervised and unsupervised learning techniques, including traditional and deep-learning methods.
Experience with pipeline tools like Kafka, Dagster/Airflow, Flink, or equivalent streaming and batch processing systems.
Hands-on experience with fine-tuning and customizing state-of-the-art models (LLMs, transformers, diffusion models, etc.) for production scenarios.
Proficiency with performance-optimized inference engines such as TensorRT, FlashInfer, and direct experience programming CUDA kernels.
Comfortable deploying models using frameworks like vLLM, Triton, or similar deployment environments.
Experience deploying, scaling, and maintaining AI/ML systems in production environments serving real-world users.
Prior research experience is encouraged.
A background in traditional software development and system design is a plus.

Relentlessly tackle novel AI/ML challenges—fine-tuning state-of-the-art models, creating innovative algorithms, and pushing boundaries.
Handle AI/ML pipelines from data ingestion and preprocessing to inference optimization and deployment strategies.
Debug, optimize, and proactively ensure your AI models and data pipelines perform robustly at scale in production.
Actively flesh out project specs alongside executives, teammates, and product engineers.
Anticipate and accommodate user needs proactively in your AI systems.
Clearly communicate progress, challenges, and blockers.

Being an LLM whisperer—someone exceptionally skilled at coaxing maximum performance out of LLMs is a significant bonus.