> Markdown version of [/jobs/ext/4694377-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/4694377-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Machine Learning Engineer

- **Company:** Applied AI Engineering LLC
- **Location:** United States
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Continuous Integration, Data Validation, Data Infrastructure, Python (Programming Language), Machine Learning, Regression Testing, Software Systems, Management of Software Versions, Large Language Models, Generative AI, Kubernetes, Apache Kafka, Data Pipelines, Docker
- **Published:** November 12, 2026
- **Apply:** https://startup.jobs/staff-machine-learning-engineer-evaluations-asapp-10022366

## About the Role

* 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., * 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
 * Experience managing a small team of engineers in a fast-paced environment

## 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., * Lead 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.