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 a 60-minute coding assessment as 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.