Machine Learning Lead Engineer, Machine Learning (ML) Required, Work From Home

United StatesRemoteFull-timelead

Machine LearningData & AI

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Requirements Summary

Demonstrated professional experience in machine learning engineering; hands-on ML experience is required.

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Machine LearningData & AI

As a Machine Learning Lead Engineer, you will lead the engineering execution that transforms advanced machine learning research into reliable, scalable, production-ready systems. You will oversee the full machine learning lifecycle, from data preparation and model training to evaluation, inference, and deployment. Working at the intersection of research, infrastructure, and product development, you will build efficient pipelines and high-performance GPU-powered systems. You will establish robust production practices that improve model quality, reliability, safety, and real-world performance. This hands-on leadership role is ideal for an experienced ML engineer who combines strong software engineering fundamentals with technical judgment, experimentation, and a commitment to delivering measurable product improvements. The position is 100% remote.

Own the end-to-end machine learning systems lifecycle, including data processing, model training, evaluation, inference, deployment, and ongoing optimization.
Build, maintain, and enhance training and fine-tuning pipelines for large-scale machine learning models.
Design and implement evaluation frameworks to measure model capabilities, robustness, safety, and performance in real-world applications.
Architect and optimize high-performance inference systems, improving latency, GPU utilization, memory efficiency, operational costs, and reliability.
Develop scalable data pipelines that support high-quality real-world and synthetic training datasets.
Establish and maintain production infrastructure for deploying, monitoring, troubleshooting, and continuously improving machine learning models.
Collaborate closely with research and application engineering teams to translate model capabilities into practical product enhancements.
Make informed technical trade-offs, experiment with new approaches, and iterate rapidly based on measurable system and model performance.
Identify, diagnose, and resolve production issues efficiently to minimize service disruptions and user impact.
Establish performance benchmarks and quality targets to ensure that research outputs translate into dependable production solutions.
Promote maintainable code, reliable machine learning workflows, and consistent engineering standards across the team.
Support and guide team members, helping align technical priorities and enabling efficient delivery of high-impact machine learning initiatives.
Ensure that model and system iterations are measurable, safe, and contribute to continuous improvements in the user experience.

Requirements

~1 min read

Demonstrated professional experience in machine learning engineering; hands-on ML experience is required.
Proven experience building and deploying production machine learning systems used by real users, beyond research prototypes or demonstrations.
Strong understanding of modern large-model training, fine-tuning, evaluation, and inference techniques.
Proficiency in Python and practical experience with machine learning frameworks such as PyTorch or JAX.
Experience with GPU-based training and inference systems, including operating and optimizing workloads at meaningful scale.
Strong software engineering and systems fundamentals, with the ability to write production-grade code that is reliable, maintainable, and correct.
Experience designing and operating machine learning pipelines and production infrastructure across the model lifecycle.
Understanding of model failure modes, evaluation practices, system reliability, and performance optimization.
Strong technical judgment and the ability to solve ambiguous, complex problems independently.
A practical, experimentation-driven mindset, with a focus on measurement, iteration, and delivering working solutions.
High standards for code quality, correctness, robustness, and production readiness.
Ability to collaborate effectively with research, infrastructure, and application engineering teams.
Willingness to complete a 60-minute coding assessment as part of the hiring process.
Ability to work effectively in a fully remote environment.