Cresta Staff Machine Learning Engineer at Cresta responsible for leading technical vision and building scalable LLM systems for next-generation AI agentic systems. Design evaluation frameworks and improve reliability of LLM-powered agents.
Responsibilities
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. This track requires strong pre-LLM ML foundations, deep expertise in LLMs and modern prompting techniques, a rapid prototyping mindset, and a proven ability to translate cutting-edge research into scalable, production-grade systems.
Agent & System Quality: Design evaluation frameworks and improve the reliability, robustness, and performance of LLM-powered agents. This includes diagnosing and mitigating failure modes such as hallucinations, retrieval errors, tool misuse, context drift, prompt brittleness, and multi-step reasoning breakdowns, while defining measurable quality metrics (e.g., accuracy, faithfulness, task completion, latency, and cost) for complex, non-deterministic systems.
Insights: Architect and scale LLM and retrieval-augmented generation pipelines that ground models in enterprise data. This track focuses on building high-performance ML systems that process complex data, extract structured insights, and deliver real-time, actionable intelligence at scale.
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.
Qualification
Bachelor’s degree in Computer ScienceStrong systems thinking
Required
Bachelor’s degree in Computer Science, Mathematics, or a related field; Master’s or Ph.D. strongly preferred.
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.
