Machine Learning Engineer, Frontier Data Products

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Company Overview

A rapidly growing AI infrastructure company is building the foundation that connects human expertise with frontier AI systems. By leveraging large-scale networks of domain experts and advanced machine learning technologies, the organization enables AI models to learn from real-world professional knowledge and workflows. Its platform supports AI development, evaluation, and deployment across a wide range of industries, helping enterprises and AI companies improve model performance and reliability at scale.

The organization is seeking a Machine Learning Engineer, Frontier Data Products to help build machine learning systems that evaluate, validate, and improve complex work products where correctness is often nuanced and difficult to define. This role sits at the intersection of machine learning, product engineering, and human expertise, focusing on creating systems that effectively combine AI capabilities with expert human judgment.

The successful candidate will work on production-grade ML systems operating under real-world constraints, including incomplete ground truth, noisy labels, latency considerations, and evolving business requirements. This position offers the opportunity to influence how models are evaluated, when human oversight is required, and how feedback loops continuously improve AI-driven workflows.

Key Responsibilities

• Build machine learning systems that score, validate, and improve complex work products where correctness is nuanced and labels are imperfect.
• Design evaluation frameworks for ambiguous tasks where ground truth may be partial, delayed, or disputed.
• Develop feedback loops that transform review processes, disagreements, corrections, and adjudication into measurable model and system improvements.
• Own production ML performance, including precision and recall optimization, regression detection, model drift monitoring, latency management, cost efficiency, and explainability.
• Improve model quality through prompting, fine-tuning, retrieval techniques, active learning, heuristics, and detailed error analysis.
• Partner with backend engineers to integrate model inference into durable, long-running workflows while maintaining transparency and human oversight.
• Define systems that determine how AI models and human experts collaborate effectively.
• Contribute to architectural decisions regarding quality measurement, automation trust, and long-term system scalability.
• Debug production machine learning issues in live environments where silent failures can significantly impact outcomes.

Required Qualifications

• Proven track record of shipping machine learning systems that improved products, workflows, or business metrics.
• Strong expertise in model quality assessment, evaluation design, error analysis, and identifying production failure modes.
• Experience working in ambiguous problem spaces where labels may be imperfect and definitions of correctness evolve over time.
• Strong judgment regarding the appropriate use of prompting, fine-tuning, heuristics, retrieval systems, human review processes, and product constraints.
• Solid engineering fundamentals across the full machine learning stack.
• Experience building and maintaining production machine learning systems.
• Ability to operate effectively across data, models, backend systems, and product workflows.
• Strong problem-solving skills and the ability to make informed decisions with incomplete information.

Preferred Qualifications

• Familiarity with large language model (LLM) applications and model-assisted workflows.
• Experience with evaluation frameworks and human-in-the-loop machine learning systems.
• Experience working with modern ML and backend technology stacks, including Python, workflow orchestration platforms, cloud infrastructure, and database systems.
• Experience designing scalable AI systems that balance automation with human oversight.

Ideal Candidate Profile

• Prefers simple, reliable, and interpretable machine learning systems that can be improved rapidly.
• Values strong evaluation methodologies before deploying model changes.
• Is comfortable working with ambiguity and making pragmatic decisions under uncertainty.
• Focuses on real-world system outcomes rather than benchmark performance alone.
• Thrives in high-ownership environments where technical decisions have long-term impact.

Disclaimer

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