Weekday

Weekday

Weekday helps companies hire engineers who are vouched by other software engineers, enabling passive income for engineers. They offer services like drafting outreach messages, shortlisting candidates, and conducting reference checks. Backed by Y Combin...

Construction & Engineering
11-50
Founded 2020

Description

  • Design, develop, train, and deploy machine learning models for prediction, classification, recommendation, or optimization use cases.
  • Perform data preprocessing, feature engineering, and exploratory data analysis on structured and unstructured datasets.
  • Implement and evaluate ML algorithms using appropriate metrics to ensure model accuracy, robustness, and scalability.
  • Collaborate with software engineers to integrate ML models into production systems and applications.
  • Optimize model performance through tuning, experimentation, and continuous monitoring.
  • Build reusable ML pipelines and workflows to support experimentation, deployment, and scaling.
  • Document models, assumptions, and results clearly for technical and non-technical stakeholders.
  • Stay current with emerging trends, tools, and best practices in machine learning and applied AI.

Requirements

  • Minimum 2 years of hands-on experience working on machine learning or applied data science projects.
  • Strong foundation in machine learning concepts, algorithms, and statistical reasoning.
  • Experience with popular ML libraries and frameworks such as scikit-learn, TensorFlow, PyTorch, or similar.
  • Comfortable working with data, including cleaning, transforming, and analyzing large datasets.
  • Programming proficiency in Python (preferred) with the ability to write clean, efficient, and maintainable code.
  • Experience implementing and evaluating ML algorithms and using appropriate metrics for model assessment.
  • Experience with model tuning, experimentation, and continuous performance monitoring.
  • Ability to translate business problems into ML-driven solutions and a strong problem-solving mindset.
  • Strong communication skills to explain model behavior, insights, and trade-offs; proactive learner who thrives in collaborative, fast-evolving environments.

Benefits

  • Salary range: Rs 500,000 - Rs 1,500,000 per year (as listed).
  • Opportunity to work across the full ML lifecycle—from data exploration and model development to deployment and continuous improvement.
  • Fast-paced, growth-oriented environment with close collaboration across engineering, product, and data teams.

Interested in this position?

Apply directly on the company website

Apply Now

Similar Roles

Data Engineering Tech Lead

Lingaro 5K-10K IT Services

Data Engineering Tech Lead at Lingaro (Data Engineering & Management) — lead a Poland-based remote/full-time team to design, deliver, and maintain scalable, secure data engineering solutions while mentoring engineers and ensuring timely, high-quality project delivery.

Azure CI/CD Python Scala SQL
16 hours, 22 minutes ago

Senior Software Engineer - Data Integration & JVM Ecosystem

ClickHouse 51-250 IT Services

Senior Software Engineer (JVM) at ClickHouse joining the Connectors team to own and maintain JVM-based data framework integrations, connectors, and drivers that enable high-performance data ingestion and a seamless developer experience for data engineering workloads.

Apache Airflow Apache Spark ClickHouse dbt Grafana HTTP Java Kafka Metabase Pandas Power BI Python SQL Tableau TCP/IP
1 month ago

Junior Data Engineer (Remote Argentina) / Ingénieur données junior (à distance)

GlobalVision 51-250 Internet Software & Services

Junior Data Engineer at GlobalVision supporting and maintaining the company’s data infrastructure to ensure reliable, accessible, and actionable data that informs business decision-making across the organization.

dbt Domo Machine Learning Power BI Python Salesforce SQL Tableau
1 month ago

Data/Infrastructure Advocate Engineer - EMEA Remote

Hugging Face 51-250 IT Services

Hugging Face is hiring a Data/Infrastructure Advocate Engineer to bridge data infrastructure and the community by championing Xet storage on the Hub and enabling efficient storage, versioning, and collaboration on large-scale datasets.

AWS GitHub Pandas Python
1 month ago

You're on a roll! Sign up now to keep applying.

Sign Up

Already have an account? Log in

Used by 14,729+ remote workers