Lead Machine Learning Engineer, Evaluations
ASAPP is hiring a Lead Machine Learning Engineer (hybrid, Mountain View, 10-12 office days/month) to own and grow the evaluation platform that measures quality, safety, and performance of its agentic AI/LLM systems. Day to day: designing eval methodologies, building annotation/data infrastructure, and mentoring engineers with Python, AWS, and Kubernetes.
- Compensation
- $170K–$190K / yr
- Location
- Mountain View
- Work setup
- Hybrid
- Seniority
- Lead
- Employment
- Not specified
- Experience
- Not specified
- Category
- Ml / AI
- Posted
Skills
Must have
- Deep, hands-on experience building and operating evaluation systems for modern ML/LLM/agentic systems
- Demonstrated experience leading technical direction of a project or small team (architecture, design reviews, long-term system health)
- Strong architectural skills; experience designing complex, data-intensive systems with production experience in Python, AWS, Kubernetes, and/or Docker
- Experience designing data pipelines for ML evaluation (labeling/annotation workflows, dataset versioning, quality control, reproducible benchmarking)
- Bachelor's Degree in CS or a related field
- Demonstrated technical mentorship of junior and mid-level engineers
- Desire to learn, teach, and collaborate closely with cross-functional peers
Nice to have
- Experience building and evaluating agentic systems at scale
- Experience with voice/audio quality evaluations
- Production experience with LLM-centric services (inference, orchestration, evaluation, monitoring)
- Familiarity with large-scale ML experimentation, benchmarking, or simulation frameworks
- Experience with conversational/customer-support AI domains (containment rate, conversation quality, goal completion)
- Knowledge of techniques for optimizing model architectures for faster inference
- Experience with AWS, CI/CD, Kafka, Athena
Full 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 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.
This a hybrid role with 10-12 days of in-office presence per month to balance flexibility with collaboration.
What you'll do
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Help develop the technical roadmap and architecture for the evaluation platform, from offline benchmarking to online/production monitoring of agentic and LLM-based systems.
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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.
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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.
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Partner closely with Research, Product, and Platform teams to productize experiments into robust AI solutions
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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.
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Stay current with advancements in ML, NLP, voice, and LLM systems, and contribute actively to technical discussions across teams.
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Mentor and support other engineers through design reviews, feedback, and knowledge sharing.