Lead Machine Learning Engineer
@ ASAPPLead Machine Learning Engineer
About the job
ASAPP focuses on delivering AI-powered customer experiences. The Lead Machine Learning Engineer will develop and manage evaluation platforms, collaborate cross-functionally, stay current with ML advances, and mentor team members, all in a hybrid work environment.
Requirements
- Building evaluation systems for ML/LLM
- Leading technical project direction
- Designing complex data systems
- Python, AWS, Kubernetes experience
- Data pipeline development
Qualifications
- Bachelor's degree in CS or related
- Experience with agentic systems
- Mentorship of engineers
- Strong architectural skills
- Hands-on evaluation experience
Full 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
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