Lead Machine Learning Engineer (Evaluations)

* At ASAPP, our mission is simple: deliver the best AI-powered customer experience—faster than anyone else. To achieve that, we’re guided by principles that shape how we think, build, and execute. We value customer obsession, purposeful speed, ownership, and a relentless focus on outcomes. ASAPP’s AI Engineering team is seeking an enterprising, talented and curious machine learning engineer * The AI Engineering team is responsible for working closely with the research and modeling teams to create state-of-the-art NLP models for specific tasks, and deploy them in a production setting designed to serve our customers at scale * We are looking for a Machine Learning Engineer to help build and evaluate the core intelligence behind our agentic AI systems. This role will play a key part in designing and owning evaluation frameworks that ensure quality, safety, and performance across complex agentic systems * We’re looking for a Lead Machine Learning Engineer to own and grow the evaluation platform that measures quality, safety, and performance across ASAPP’s agentic AI systems- the infrastructure that tells us, with confidence, whether a model or agent change is actually an improvement before it reaches customers * Help develop the technical roadmap and architecture for the evaluation platform, from offline benchmarking to online/production monitoring of agentic and LLM-based systems * Design eval methodologies appropriate to different stages of the pipeline: golden/regression test sets, human-in-the-loop review workflows, LLM-as-judge approaches, and automated metrics for task success, safety, and hallucinations * Build the data infrastructure evaluation depends on: annotation and labeling pipelines, dataset versioning, data quality checks, and tooling that lets researchers and product teams run and interpret experiments without needing platform team help * Partner closely with Research, Product, and Platform teams to productize experiments into robust AI solutions * Represent the eval platform to stakeholders outside the immediate team- set expectations on what “good” looks like for a model/agent release, and report on platform health and coverage * Stay current with advancements in ML, NLP, voice, and LLM systems, and contribute actively to technical discussions across teams * Mentor and support other engineers through design reviews, feedback, and knowledge sharing * Experience designing data pipelines for ML evaluation- labeling/annotation workflows, dataset versioning and quality control, and reproducible benchmarking * A Bachelor’s Degree in CS or other related fields * Demonstrated technical mentorship of junior and mid-level engineers, driving adoption of best practices and architectural alignment for scalability and extensibility * Deep, hands-on experience building and operating evaluation systems for modern ML/LLM/agentic systems- not just consuming existing eval tools * Strong architectural skills, with proven experience designing complex, data-intensive software systems and production experience with Python, AWS, Kubernetes, and/or Docker * Demonstrated experience leading the technical direction of a project or small team: setting architecture, driving design reviews, and being accountable for a system’s long-term health (not just shipping features) * Desire to learn, teach, and collaborate closely with cross-functional peers * Experience with voice/audio quality evaluations * Experience building and evaluating agentic systems at scale * Production experience with LLM-centric services (e.g., inference, orchestration, evaluation, monitoring) * Experience with conversational/customer-support AI domains (e.g., containment rate, conversation quality, goal completion) * Familiarity with large-scale ML experimentation, benchmarking, or simulation frameworks * Knowledge of techniques for optimizing model architectures for faster inference * Experience with AWS, CI/CD, Kafka, Athena