Lead Machine Learning Engineer, Evaluations

Responsibilities

• 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.

Requirements

• 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.
• 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.
• Experience with voice/audio quality evaluations.
• 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.

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.
• Experience designing data pipelines for ML evaluation- labeling/annotation workflows, dataset versioning and quality control, and reproducible benchmarking.
• 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.
• Production experience with LLM-centric services (e.g., inference, orchestration, evaluation, monitoring)
• Familiarity with large-scale ML experimentation, benchmarking, or simulation frameworks.

Experience with AWS, CI/CD, Kafka, Athena

• Compensation package also includes a performance bonus on top of the listed salary range
• Separately, we also offer a compelling equity grant comprised of stock options

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