Machine Learning Engineer

We're working with a well-funded early-stage AI startup building cutting-edge machine learning systems at the intersection of large language models, distributed training, and production AI infrastructure.This is an opportunity to join a highly technical team where engineers work across the full machine learning lifecycle, from large-scale data generation and model training through deployment, optimization, and production infrastructure. The team operates at the boundary of research and engineering, giving engineers the opportunity to contribute to new ideas while building systems that directly power real-world AI products.What You'll Be Working OnBuilding scalable data pipelines to collect, process, and generate large synthetic datasets for machine learningDeveloping infrastructure for distributed multi-GPU model trainingProfiling and optimizing model training and inference performanceDeploying and maintaining high-throughput inference systems for large language modelsWorking closely with researchers to translate new ideas into reliable production systemsBuilding tooling that supports the complete machine learning development lifecycle, from experimentation through deployment and monitoringContributing to technical research, experimentation, and engineering best practicesWe're Looking For Someone Who HasBachelor's or Master's degree in Computer Science or a related technical disciplineStrong Python programming skills and experience with modern machine learning frameworksSolid understanding of transformer architectures and large language modelsExperience building production-quality machine learning systemsComfortable working across both research and engineering environmentsStrong software engineering fundamentals and systems thinkingNice to HaveExperience with GPU programming and performance optimizationFamiliarity with distributed training frameworks such as DeepSpeed, FSDP, Ray, or similar technologiesExperience serving large language models using modern inference frameworksExperience building large-scale data processing pipelines using technologies such as Spark, Beam, or similar distributed systemsFamiliarity with workflow orchestration toolsExperience with experiment tracking, MLOps, and production ML workflowsKnowledge of cloud infrastructure and modern DevOps practicesExperience designing scalable AI infrastructure supporting production machine learning workloadsWhy JoinWork on technically challenging problems at the intersection of AI research and production engineeringSignificant ownership across the full machine learning lifecycleOpportunity to influence architecture, infrastructure, and model developmentCollaborative environment where engineering and research work closely togetherJoin a small, high-performing team building next-generation AI systems from the ground up