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Weekday AI (YC W21)
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Software Engineer - Machine Learning

Software Engineer - Machine Learning

Weekday AI (YC W21)

4 years

0 Lacs

india

Posted:2 months ago| Platform:

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Skills Required

software learning ml forecasting code design data reliability scalability etl storage stack spark dask airflow docker tooling ai engineering ux python sql rest algorithms processing kubernetes

Work Mode

On-site

Job Type

Full Time

Job Description

This role is for one of the Weekday's clients

Min Experience: 4 yearsJobType: full-timeWe 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.

Requirements

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

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