Lead Machine Learning Engineer, Evaluations
Mountain View
- Pay
USD 170,000–190,000/year — pay source
Salary 170,000 – 190,000 USD per year Benefits include:
Read the full posting- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type Full-time
Employment type in the employer’s posting: Full-time
From the employer’s posting
What you’ll work on
Full postingShape the technical roadmap and architecture for the evaluation platform
Design eval methods: golden test sets, human review, LLM-as-judge, automated metrics
Build annotation pipelines, dataset versioning, and data quality checks
Partner with Research, Product, and Platform teams to productize experiments
Set expectations for model and agent releases and report on platform health
From the employer’s posting
Help develop the technical roadmap and architecture for the evaluation platform
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
Partner closely with Research, Product, and Platform teams to productize experiments into robust AI solutions
set expectations on what "good" looks like for a model/agent release, and report on platform health and coverage
What you’ll bring
All qualificationsCore experience
- 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).
- Experience designing data pipelines for ML evaluation- labeling/annotation workflows, dataset versioning and quality control, and reproducible benchmarking.
- Experience building and evaluating agentic systems at scale.
- Experience with voice/audio quality evaluations.
- 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).
Qualification wording
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).
Experience designing data pipelines for ML evaluation- labeling/annotation workflows, dataset versioning and quality control, and reproducible benchmarking.
Experience building and evaluating agentic systems at scale.
Experience with voice/audio quality evaluations.
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).
Education & alternatives
- 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.
Tools in this posting
- Python
- AWS
- Docker
- Kafka
- Kubernetes
Source — Tool mentions in context
- 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.
- Knowledge of techniques for optimizing model architectures for faster inference. - Experience with AWS, CI/CD, Kafka, Athena Compensation package also includes a performance bonus on top of the listed salary range
Posting record
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- Live
- First seen by us
- Sep 12, 2026 · 47 days ago
- Recorded sightings
- 31
- Last seen by us
- Oct 28, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
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Job description
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.
What you'll need
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Deep, hands-on experience building and operating evaluation systems for modern ML/LLM/agentic systems- not just consuming existing eval tools.
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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).
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Strong architectural skills, with proven experience designing complex, data-intensive software systems and production experience with Python, AWS, Kubernetes, and/or Docker.
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Experience designing data pipelines for ML evaluation- labeling/annotation workflows, dataset versioning and quality control, and reproducible benchmarking.
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A Bachelor’s Degree in CS or other related fields
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Demonstrated technical mentorship of junior and mid-level engineers, driving adoption of best practices and architectural alignment for scalability and extensibility.
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Desire to learn, teach, and collaborate closely with cross-functional peers.
What we'd like to see
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Experience building and evaluating agentic systems at scale.
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Experience with voice/audio quality evaluations.
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Production experience with LLM-centric services (e.g., inference, orchestration, evaluation, monitoring)
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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
Salary
170,000 – 190,000 USD per year
Employment type
Full-time
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
Salary 170,000 – 190,000 USD per year Benefits include:
- Location & working pattern
Mountain View
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
More source context
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 Employment type
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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