Machine Learning Engineer

Responsibilities

• Design, train, evaluate, and deploy machine learning models across text, image, audio, and multimodal domains.
• Develop and improve classification systems for safety, security, abuse detection, and intelligence applications.
• Conduct experiments to benchmark, evaluate, and compare AI models, including large language models and multimodal systems.
• Contribute to model distillation, optimization, and fine-tuning efforts to improve performance, efficiency, and deployability.
• Design evaluation pipelines, metrics, and testing frameworks to measure model capabilities, reliability, and safety.
• Build agentic systems and automated workflows for evaluation, red teaming, research, and large-scale experimentation.
• Own ML projects from initial research and prototyping through production deployment and monitoring.
• Partner with software engineers to productionize ML systems and support ongoing improvements.
• Provide technical expertise and guidance across client engagements and internal research initiatives.

Requirements

• 3–5+ years of professional experience building and deploying machine learning systems.
• Strong proficiency in Python and modern machine learning frameworks such as PyTorch and/or TensorFlow.
• Experience working across multiple modalities, with expertise in one or more of: Computer Vision, Natural Language Processing, and related areas.
• Experience training, fine-tuning, evaluating, and deploying machine learning models in production environments.
• Experience designing evaluation methodologies, benchmarking systems, and model performance metrics.
• Experience with MLOps tools and practices (Docker, Kubernetes, CI/CD for ML, MLflow, etc.).
• Experience with cloud platforms such as Google Cloud Platform (preferred), AWS, or Azure, including ML infrastructure, workflow orchestration, storage, and database services.
• Familiarity or experience with model distillation, synthetic data generation, reinforcement learning, or AI evaluation research is strongly preferred.
• Prior experience in cybersecurity, trust and safety, abuse prevention, threat intelligence, or related domains.
• Experience with retrieval-augmented generation (RAG), AI agent frameworks, and context orchestration systems such as LangChain, LlamaIndex, OpenAI Agents, or AutoGen.