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Software Engineer - Machine Learning at Weekday AI. This role is for one of the Weekday's clients. Min Experience: 4 years. JobType: full-time. We are rebuilding our . ML Workspaces. , the core of our enterprise-grade Predictions platform — where users can create forecasting and predictive models with zero code. To scale to . tens of millions of time series. , we’re looking for engineers who can design and optimize large-scale distributed systems, build robust data pipelines, and deliver a seamless ML experience end-to-end.. What You’ll Do (First 3–6 Months). Build and scale . distributed workflows. for predictive modeling. . Enhance the . reliability, scalability, and throughput. of ML Workspaces. . Optimize . ETL and storage pipelines. to handle 25M+ time series efficiently. . Work hands-on with a . modern ML stack. — Spark/Dask/Ray, Airflow, Docker/K8s, CI/CD, and MLOps tooling. . Take . product ownership. of key components driving the ML Workspace evolution. . Why You’ll Love This Role. Contribute to building one of the . most advanced ML platforms. in enterprise AI. . Operate at the intersection of . software engineering, ML infrastructure, and product UX. . . Solve . high-impact, real-world challenges. that shape global supply chains. . Thrive in a . high-ownership, high-autonomy environment. alongside world-class engineers. . What We’re Looking For. Must-Haves:. 4–8 years of experience in software or product engineering. . Strong foundation in . software engineering principles. : abstraction, modularity, scalability, reliability, and fault tolerance. . Proficiency in . Python, SQL, and REST APIs. . . Solid understanding of . data structures, algorithms, and design patterns. . . Hands-on experience with . Docker, CI/CD pipelines, and MLOps tooling. . . Exposure to . distributed computing frameworks. (Spark, Dask, Ray). . Conceptual understanding of . end-to-end ML pipelines. — from data ingestion to post-processing. . Nice-to-Haves:. Experience building . ETL or feature engineering pipelines. . . Familiarity with . Kubernetes. and cloud platforms.. Company Location: India.