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Weekday AI (YC W21)
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Machine Learning Engineer

Machine Learning Engineer

Weekday AI (YC W21)

4 years

0 Lacs

india

Posted:14 hours ago| Platform:

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Skills Required

learning ml data training deployment design architecture parallelism drive ux etl storage workflow engineering evaluation monitoring reliability latency tracking collaboration code development software dataset integration orchestration sql nosql tooling programming python rest api docker kubernetes aws gcp azure model collaborative

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're looking for a Machine Learning Engineer who can own ML data, training, and deployment pipelines end-to-end—making them faster, more reliable, and scalable for enterprise use. You won't just deliver pipelines—you'll design the architecture, optimize for performance and parallelism, and drive improvements in UX and MLOps across the ML lifecycle.

Requirements

Key Responsibilities

Scale data & training: Optimize ETL and storage workflows to handle very large datasets (up to :25M time series).
End-to-end workflow ownership: Manage ingestion, feature engineering, training, evaluation, deployment, and monitoring.
Enhance parallelism & reliability: Build distributed compute solutions that minimize latency and reduce job failure rates.
Strengthen MLOps: Develop CI/CD pipelines for models and data; ensure robust experiment tracking, observability, and reproducibility.
Product collaboration: Work closely with design and product teams to make complex ML tasks simple within a no-/low-code environment.

🧠 What We're Looking For

Must-Haves

3-6 years of product development experience in data- or ML-focused systems
Strong computer science and software engineering fundamentals
Expertise in:

Data engineering and ETL pipelines
Dataset integration and lifecycle orchestration with SQL/NoSQL stores
CI/CD pipelines and MLOps tooling
Programming with Python, SQL, and REST API development

Nice-to-Haves

Experience with Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure)
Exposure to Ray or other distributed-compute frameworks
Familiarity with model monitoring, experiment tracking, and data lineage

🧬 Traits for Success

High ownership and collaborative mindset
Strong systems thinking; comfortable with ambiguity
Clear communicator who thrives in cross-functional settings
Product-driven engineer who deeply values user experience

Skills:

Machine Learning | Data Engineering | MLOps | Python | SQL

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Machine Learning Engineer