Senior Machine Learning Engineer
Build production machine learning systems that detect, evaluate, and understand security risks in advanced AI applications.
About the role
We are looking for a senior ML engineer who can bridge research and production. You will build the systems that turn AI security research into scalable capabilities used to evaluate and protect real AI applications. You will work across model evaluation, detection, inference infrastructure, data pipelines, experimentation, and production services. Depending on your background, the work may involve training or fine-tuning models, building classifiers and detectors, developing evaluation pipelines, optimizing inference, or creating infrastructure for large-scale adversarial testing. The role requires strong software engineering fundamentals as well as genuine depth in machine learning. We are looking for engineers who understand not only how to call a model API, but how models behave, how ML systems fail, and how to build reliable systems around them.
Responsibilities
- Design, implement, and productionize ML systems for AI security and model evaluation.
- Build scalable pipelines for evaluating LLMs, agents, and AI applications against large suites of security tests.
- Develop classifiers, detectors, evaluators, and other ML-based security capabilities.
- Work with large language models, embeddings, transformers, and modern model-serving infrastructure.
- Develop data pipelines for collecting, labeling, validating, and monitoring training and evaluation data.
- Design experiments and analyze model performance using rigorous evaluation methodologies.
- Optimize inference systems for latency, cost, throughput, and reliability.
- Build APIs and production services around ML capabilities.
- Collaborate closely with researchers to productionize promising research.
- Own systems from initial design through deployment, monitoring, and iteration.
- Improve engineering standards around testing, observability, reproducibility, and ML system reliability.
Requirements
- 5+ years of professional experience building and deploying machine learning systems.
- Strong Python and software engineering fundamentals.
- Deep practical experience with PyTorch, TensorFlow, JAX, or equivalent ML frameworks.
- Strong understanding of transformers, LLMs, embeddings, model evaluation, and modern ML architectures.
- Experience taking ML systems from experimentation through reliable production deployment.
- Experience designing data and evaluation pipelines at meaningful scale.
- Understanding of distributed systems and production infrastructure.
- Ability to debug complex failures across application, infrastructure, data, and model layers.
- Strong communication skills and ability to work effectively with researchers and software engineers.