- Build state-of-the-art machine learning models - especially large language models (LLMs) or multimodal models - and continuously enhance performance. Draws from roles like Model

- Implement model behavior evaluation frameworks : design, test, and debug experimental pipelines in both research and product contexts

Infrastructure & Systems Engineering :

- Architect and maintain scalable, high-performance infrastructure for distributed model training and inference. This includes building kernels, performance pipelines, and inference systems

- Tune system performance, manage resource utilization (GPU/CPU/memory), profile system behavior, and deploy optimization tooling

ML Infrastructure & Data Platforms :

- Design and manage data pipelines, orchestration (e.g., Kafka, Spark, Airflow), and large-scale storage systems (Iceberg, Delta) to enable ML workflows

- Craft and maintain data ingestion, streaming, feature engineering, and operational reliability practices.

Model Deployment & Monitoring :

- Integrate models into production, ensuring they are robust, performant, and reliable in real-world usage

- Build telemetry and failure-analysis systems to detect model drift, calibration issues, or adversarial impacts

- Support on-call rotations and incident response for critical production infrastructure.

Cross-functional Collaboration & Mentorship :

- Work alongside ML researchers, software engineers, hardware teams, and product groups to translate AI research into scalable, user-safe implementations

- Mentor junior engineers, conduct code reviews, and lead by example in building reliable ML systems

2. Key Qualifications & Skills :

Technical Proficiency :

- Strong coding skills in languages such as Python, C++, and sometimes Rust

- Deep experience with ML frameworks (e.g., PyTorch, TensorFlow), and familiarity with LLMs or multimodal models

- Hands-on knowledge with GPU programming (CUDA, kernel optimization), performance profiling, or architectures like HBM, warp utilization, low-precision formats

Systems & Infrastructure Mastery :

- Expertise in distributed systems, cloud environments, container orchestration (e.g., Kubernetes), performance tools (Nsight, perf), and infrastructure-as-code tools (Terraform)

- Experience designing simulation frameworks, benchmarking tools, or architectural modeling stacks

ML Engineering Foundations :

- In-depth understanding of deep learning, transformers, reinforcement learning, or transfer learning methods

- Background in computer vision, audio ML, or real-time systems for authentication or perception, when relevant

Soft Skills :

- Strong analytical thinking, problem-solving, and ability to work with ambiguous or loosely defined challenges

- Proactive communicator, able to collaborate efficiently across diverse teams, and resilient in fast-paced environments