About the company
HackerRank helps companies like NVIDIA, Amazon, and Microsoft hire and upskill developers based on skills, not pedigree. The platform is trusted by over 2,500 innovative companies. The ML systems powering the platform are the product, not auxiliary features.
Responsibilities
- Design and ship production ML systems across Chakra, integrity, and evaluation domains
- Own the full ML lifecycle: problem framing, data strategy, experimentation, deployment, and iteration
- Build evaluation infrastructure and benchmarking pipelines to measure model quality before and after deployment
- Define architecture and production bar for different signal categories from scratch
- Mentor and support junior ML engineers
- Establish ML best practices: monitoring, model feedback loops, and quality standards
Requirements
- 5+ years building and shipping ML systems in production at scale
- Systems thinking across model accuracy, data pipelines, serving infrastructure, and customer outcomes
- Strong evaluation methodology
- Proficient in Python and Deep Learning frameworks like PyTorch/JAX
- Experience implementing research papers to build DNN architectures from scratch
Nice to have
- Multimodal systems: vision, NLP, audio, or behavioral signal pipelines
- LLM experience: fine-tuning, RLHF, pruning, distillation, knowledge graph, Transformers, SSM, LNN
- Background in adversarial ML, fraud detection, or anomaly detection
- Publications or open-source contributions
- Strong mathematical foundations for Deep Learning