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

Accountabilities

• 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:

• 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 a60-minute coding assessmentas part of the hiring process.

• Ability to work effectively in a fully remote environment.

Benefits:

• Fully remote, work-from-home arrangement.

• Medical insurance.

• Dental insurance.

• Vision insurance.

• Savings plan options.

• Paid time off (PTO).

• Opportunity to work on advanced machine learning systems, large models, and GPU-based infrastructure.

• Hands-on technical leadership opportunities across model development, deployment, and production optimization.