ML Engineering Manager

Staff ML Engineering Manager | Remote | Atlanta, GA | $200K - $230KA fast-growing AI team is building the infrastructure that puts machine learning into the hands of legal professionals at scale. They're looking for a Staff ML Engineering Manager to lead the team that takes models out of experimentation and into production - owning the full stack of ML systems from training pipelines and inference services to retrieval architectures and real-time AI features. This is a rare role: genuine technical leadership over ML systems, in a domain where the stakes are high and the problems are genuinely interesting.What you'll doLead and grow a high-performing ML engineering team, setting technical direction while staying close to the codeOwn the end-to-end ML systems stack: training pipelines, model serving, inference APIs, retrieval systems, and orchestration layersDrive the architecture for how LLMs, embeddings, and predictive models are integrated into live legal productsDefine and enforce engineering standards for model reliability, observability, cost efficiency, and production readinessPartner with applied ML researchers and data scientists to move work from experimentation into robust, scalable systemsBuild for both real-time AI responses and high-throughput batch processing - and know when to use whichShape the team's culture around rigor, ownership, and a high bar for production MLWhat you need8+ years in ML engineering or backend engineering with deep exposure to production ML systemsProven experience leading engineering teams - setting direction, growing engineers, and owning outcomesHands-on depth in Python and the ML ecosystem: model serving, pipeline orchestration, embeddings, or LLM integrationStrong systems instincts: you think about failure modes, latency, scalability, and cost before they become problemsExperience with cloud-native ML infrastructure on AWS, Azure, or GCPThe ability to operate as a technical peer to senior ML researchers and a credible leader to your team simultaneouslyA bias toward production reliability over elegant prototypesNice to haveExperience with RAG pipelines, vector databases, or semantic search in productionMLOps depth: model versioning, monitoring, drift detection, retraining workflowsBackground in legal tech, SaaS, or other high-stakes, document-heavy domainsExperience leading a team through an AI transition - taking a product from ML-adjacent to ML-nativeIf you want to lead ML engineering that actually ships, in a domain where accuracy and reliability genuinely matter, press Easy Apply now.