Discover and apply for Senior Platform Machine Learning Engineer jobs, a pivotal role at the intersection of cutting-edge artificial intelligence and robust software engineering. This profession is dedicated to building and maintaining the foundational infrastructure that enables data scientists and machine learning practitioners to innovate at scale. Unlike ML researchers who focus on novel algorithms or applied ML engineers who build end-user models, the Senior Platform ML Engineer architects the systems, tools, and platforms that empower entire organizations to efficiently develop, deploy, and manage machine learning models throughout their lifecycle. Professionals in these roles are the backbone of the ML ecosystem within a company. Their core responsibility is to design, implement, and evolve a reliable, scalable, and user-friendly machine learning platform. This involves creating solutions for the entire ML workflow: data ingestion and versioning, feature store management, distributed model training pipelines, efficient model deployment (MLOps), robust model monitoring, and governance. They work to abstract away infrastructure complexity, allowing other teams to focus on model development rather than operational hurdles. A key objective is ensuring reproducibility, cost-efficiency, and high availability of ML services. Typical day-to-day tasks include collaborating with data science teams to understand pain points in their workflow, automating manual processes, selecting and integrating best-in-class tools, and writing production-grade code to glue together various components of the ML stack. They are deeply involved in ensuring the platform's scalability to handle massive datasets and thousands of model experiments, as well as its reliability for serving critical predictions in real-time. To excel in Senior Platform Machine Learning Engineer jobs, a unique blend of skills is required. Candidates typically possess a strong software engineering background with expertise in languages like Python and sometimes Go or Java. Proficiency with cloud platforms (AWS, GCP, Azure) and their ML services (e.g., SageMaker, Vertex AI) is standard. Deep knowledge of ML frameworks like TensorFlow or PyTorch is essential, not necessarily to build models, but to understand their operational needs. Experience with containerization (Docker, Kubernetes), workflow orchestration (Airflow, Kubeflow), and infrastructure-as-code is crucial. Furthermore, familiarity with data engineering tools and concepts, along with a solid grasp of MLOps principles, is expected. Soft skills like cross-functional collaboration, strategic thinking, and a passion for mentoring are highly valued, as these senior roles often set technical direction and best practices. For those seeking a career that builds the engine of AI innovation, Senior Platform Machine Learning Engineer jobs offer a challenging and impactful path, combining deep technical architecture with the goal of accelerating enterprise-wide AI capabilities.
