Posted 7 weeks ago Lead Machine Learning Engineer (Evaluations) Asapp› ★ 3.6 (94) New York City, New York, United States 🇺🇸 Onsite Full Time AWSKafkaKubernetesMachine Learning $170,000 - $190,000 year Apply on source site Track Check your resume for this jobPro Job description 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 engineerThe 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 scaleWe 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 systemsWe’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 customersHelp develop the technical roadmap and architecture for the evaluation platform, from offline benchmarking to online/production monitoring of agentic and LLM-based systemsDesign 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 hallucinationsBuild 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 helpPartner closely with Research, Product, and Platform teams to productize experiments into robust AI solutionsRepresent 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 coverageStay current with advancements in ML, NLP, voice, and LLM systems, and contribute actively to technical discussions across teamsMentor and support other engineers through design reviews, feedback, and knowledge sharingExperience designing data pipelines for ML evaluation- labeling/annotation workflows, dataset versioning and quality control, and reproducible benchmarkingA Bachelor’s Degree in CS or other related fieldsDemonstrated technical mentorship of junior and mid-level engineers, driving adoption of best practices and architectural alignment for scalability and extensibilityDeep, hands-on experience building and operating evaluation systems for modern ML/LLM/agentic systems- not just consuming existing eval toolsStrong architectural skills, with proven experience designing complex, data-intensive software systems and production experience with Python, AWS, Kubernetes, and/or DockerDemonstrated 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 peersExperience with voice/audio quality evaluationsExperience building and evaluating agentic systems at scaleProduction 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 frameworksKnowledge of techniques for optimizing model architectures for faster inferenceExperience with AWS, CI/CD, Kafka, Athena
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