$240K – $265K/yr
Role overview
Job description
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.
What you'll need
Deep, hands-on experience building and operating evaluation systems for modern ML/LLM/agentic systems- not just consuming existing eval tools.
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).
Strong architectural skills, with proven experience designing complex, data-intensive software systems and production experience with Python, AWS, Kubernetes, and/or Docker.
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.
Desire to learn, teach, and collaborate closely with cross-functional peers.
What we'd like to see
Experience building and evaluating agentic systems at scale.
Experience with voice/audio quality evaluations.
Production experience with LLM-centric services (e.g., inference, orchestration, evaluation, monitoring)
Familiarity with large-scale ML experimentation, benchmarking, or simulation frameworks.
Experience with conversational/customer-support AI domains (e.g., containment rate, conversation quality, goal completion).
Knowledge of techniques for optimizing model architectures for faster inference.
Experience with AWS, CI/CD, Kafka, Athena
Benefits include: Competitive compensation with stock options Comprehensive medical, vision, and dental insurance 401k matching Fitness and wellness stipend Mental well-being benefits Professional learning and development stipend Parental leave, including adoptive and foster parents 3 weeks paid time off (increases with tenure) along with sick leave, bereavement and jury duty ASAPP is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, disability, age, or veteran status. If you have a disability and need assistance with our employment application process, please email us at careers@asapp.com to obtain assistance. #LI-SL1 #LI-Hybrid
About this listing
- Source: ASAPP's official careers site (via Lever) · last confirmed on the source 8 Nov 2026
- Posted 11 Sept 2026
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What they are looking for
Skills & requirements
Qualification
What you'll do 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; Stay current with advancements in ML, NLP, voice, and LLM systems, and contribute actively to technical discussions across teams; What you'll need Deep, hands-on experience building and operating evaluation systems for modern ML/LLM/agentic systems- not just consuming existing eval tools
Department · Technology
Hiring company
ASAPP
ASAPP delivers the leading agentic customer experience platform for enterprises at scale.
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