Senior Machine Learning Engineer
Cresta is an enterprise customer-experience AI platform that unifies autonomous AI agents, real-time agent assistance, and conversation intelligence for contact centers.
Funding history
About Cresta
Cresta Intelligence, Inc. operates Cresta, a human-centric AI platform for enterprise contact centers. Its products automate selected customer conversations, assist human agents in real time, and analyze conversations to improve customer experience, revenue, and operating efficiency.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will lead the design and development of AI agents and agent-assist systems. You will build multi-step workflows that combine real-time guidance, retrieval, reasoning, summarization, and automated actions. You will deploy and optimize LLM and RAG systems, establish evaluation strategies and quality metrics, mitigate failure modes, and improve scalability, latency, security, cost efficiency, and reliability.
Requirements
- Bachelor’s degree in Computer Science, Mathematics, or a related field
- 5–8+ years of industry experience building and deploying production machine learning systems
- Significant experience working with LLMs
- Expertise in NLP, generative AI, transformer architectures, embeddings, and retrieval systems
- Experience designing and deploying enterprise RAG systems
- Experience building and evaluating complex agentic or multi-step LLM workflows
- Knowledge of PyTorch, TensorFlow, Hugging Face, and distributed or cloud-based infrastructure
- Ability to optimize real-time ML systems for performance, scalability, and reliability
- Technical leadership skills
Responsibilities
- Lead the design and development of AI agent and agent-assist systems
- Architect multi-step agent workflows combining guidance, retrieval, reasoning, summarization, and automated actions
- Design, deploy, and optimize LLM-powered systems, RAG pipelines, multi-agent orchestration, and domain-adapted models
- Improve reasoning, planning, and tool-use capabilities
- Develop evaluation strategies using offline benchmarking, online experimentation, and LLM-as-a-judge methodologies
- Diagnose and mitigate hallucinations, retrieval errors, tool misuse, prompt brittleness, and reasoning breakdowns
- Define and measure quality metrics for reliability and performance
- Optimize AI systems for scalability, latency, security, and cost efficiency
- Collaborate with product, frontend, and backend teams to integrate AI capabilities
- Mentor engineers and contribute to technical strategy and roadmap development
Benefits
- Medical, dental, and vision coverage
- Flexible PTO
- Paid parental leave
- Retirement savings plan
- Remote work setup budget
- Monthly wellness and communication stipend
- In-office meal program for onsite employees
- Commuter benefits for onsite employees
- Equity