Staff Machine Learning Engineer

Machine Learning Engineers at Cresta work across several high-impact AI initiatives. Final team placement is determined based on experience, strengths, and business needs. Current focus areas include: Agentic Assist: Lead and build next-generation agentic AI systems that augment contact center agents in real time; Agent & System Quality: Design evaluation frameworks and improve the reliability, robustness, and performance of LLM-powered agents; Insights: Architect and scale LLM and retrieval-augmented generation pipelines that ground models in enterprise data.

Responsibilities

Define and lead the technical vision for Cresta's next-generation Agentic AI systems, including Agentic Assist and enterprise AI Agents; Architect scalable, production-grade LLM systems that integrate reasoning, retrieval, planning, tool use, and real-time decision-making into cohesive, intelligent workflows; Design and evolve multi-agent orchestration frameworks that combine RAG, structured knowledge, domain-adapted models, and automated actions; Establish best practices for building robust, reliable, and cost-efficient LLM-powered systems in high-scale production environments; Own evaluation strategy for complex, non-deterministic AI systems, including offline benchmarking, online experimentation, LLM-as-a-judge methodologies, and systematic failure analysis; Proactively identify and mitigate agent failure modes such as hallucinations, tool misuse, retrieval errors, prompt brittleness, context drift, and multi-step reasoning breakdowns; Define measurable quality standards (accuracy, faithfulness, task completion, latency, cost efficiency, robustness) and drive continuous system improvement; Influence cross-team architecture decisions across ML, backend, and product engineering to ensure seamless integration of AI capabilities; Mentor senior engineers, raise the technical bar, and contribute to long-term AI strategy and roadmap planning; Translate cutting-edge research advances into practical, high-impact production systems

Requirements

Bachelor's degree in Computer Science, Mathematics, or a related field; 7+ years of experience building and deploying machine learning systems in production, including deep hands-on experience with LLMs at scale; Demonstrated leadership in architecting complex AI systems, particularly agentic or multi-step LLM workflows; Deep expertise in transformer-based models, embeddings, retrieval systems, and Retrieval-Augmented Generation (RAG) pipelines; Experience designing evaluation frameworks for LLM systems beyond single-turn prompts, including robustness testing and production monitoring; Strong systems thinking: ability to design for scalability, latency constraints, cost efficiency, security, and long-term maintainability; Extensive experience with modern ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face) and distributed/cloud-based infrastructure; Proven ability to influence technical direction across teams as a senior individual contributor; A strong bias toward action — able to prototype rapidly while maintaining production rigor

Nice to have

Master's or Ph.D. strongly preferred

Benefits

Comprehensive medical, dental, and vision coverage; Flexible PTO; Paid parental leave for all new parents welcoming a new child; Retirement savings plan; Remote work setup budget; Monthly wellness and communication stipend; In-office meal program and commuter benefits provided for onsite employees; Equity