Developing and maintaining model validation and security scanning pipelines that assess models for quality, correctness, and vulnerabilities prior to deployment
Implementing quantization, pruning, and other optimization techniques to reduce model footprint and improve inference latency across diverse hardware configurations
Building guardrail and content safety systems that enforce compliance policies and mitigate risks such as jail breaking and prompt injection
Designing model routing and prompt management infrastructure that supports intelligent request handling across model variants
Contributing to model evaluation frameworks that measure accuracy, performance, and safety metrics across the model lifecycle

5 years of relevant experience and a Bachelor's/Master's degree in Computer Science, Machine Learning, or a related field
Hands-on experience with modern ML frameworks such as PyTorch, TensorFlow, or JAX, including model training, fine-tuning, and inference optimization
Proficiency in model quantization techniques (GPTQ, AWQ, GGUF) and an understanding of how model compression impacts accuracy and latency
Experience working with large language models (LLMs), transformer architectures, and multi-modal AI systems
Familiarity with model safety and responsible AI practices, including content filtering, red-teaming, or adversarial robustness evaluation
Proficiency in Python and experience building production data or ML pipelines
Experience with containerized deployments, CI/CD practices, and cloud infrastructure (not required, but suggested)

Senior Software Engineer

Key skills