Senior AI Systems Engineer

Benevity is the way the world does good, providing companies (and their employees) with technology to take social action on the issues they care about.

As a Senior AI Systems Engineer, you'll build and run the machine learning and GenAI systems that power Benevity's products. You'll turn promising prototypes into reliable, production-grade features that help impact leaders do more good.

Responsibilities:

• Design, build and operate ML and GenAI features end to end, from data ingestion and feature pipelines to model serving and monitoring.
• Build LLM-powered applications such as search, assistants and agents, including RAG pipelines, tool integrations and multi-step workflows.
• Create evaluation frameworks for GenAI systems that combine offline benchmarks, online metrics and human review.
• Implement guardrails for GenAI applications, including prompt injection defenses, hallucination mitigation and responsible AI practices.
• Put MLOps standards into practice: versioning, automated testing, A/B testing, drift detection and safe rollbacks.
• Translate business problems from product managers and data scientists into clear technical requirements and scoped delivery plans.
• Write design docs, take part in design reviews and give thoughtful code reviews.
• Mentor mid-level engineers on production ML practices and system design.
• Balance latency, cost and quality when choosing models, prompts and infrastructure.
• Find and fix bottlenecks in pipelines, serving and platform performance.

Requirements:

• Bachelor's or Master's degree in Computer Science, Mathematics or a related field, or equivalent professional experience.
• 5+ years of software engineering experience, including 3+ years building and deploying ML models in production.
• Hands-on experience across the ML lifecycle: training, evaluation, deployment, monitoring and retraining.
• Strong Python skills and experience with ML frameworks such as PyTorch, TensorFlow or scikit-learn.
• Experience with at least one major cloud platform (AWS, Azure or Google Cloud) and with distributed workloads.
• Working knowledge of data pipelines, feature engineering and large-scale data processing.
• Familiarity with ML tooling such as experiment tracking, model registries, feature stores or orchestration frameworks.
• Experience with CI/CD for ML, including automated validation and rollback strategies.
• Experience shipping LLM-based features, including prompt engineering, few-shot techniques and tool or function calling.
• Experience building RAG systems, including embeddings, vector databases and retrieval tuning.
• Exposure to agentic workflows and orchestration frameworks such as LangChain, LlamaIndex or ADK.
• Solid grasp of APIs, microservices and reliability fundamentals.
• Awareness of data governance, privacy, bias and explainability in AI systems.
• Clear communication and a collaborative approach with technical and non-technical partners.

Benefits:

• Innovative work
• Growth opportunities
• Caring co-workers
• Chance to do work that fills you with a sense of purpose