Machine Learning Engineer - Algorithm & Data Pipeline (7-12 yrs)

Responsibilities :

- Design, develop, and implement machine learning models using appropriate algorithms and techniques.

- This includes data preprocessing, feature engineering, model selection, training, evaluation, and hyperparameter tuning.

- Develop and maintain robust and scalable backend systems to support ML model deployment and integration.

- This includes designing APIs, building data pipelines, and optimizing performance. (Must clear a backend engineering bar/assessment).

- Stay up-to-date with the latest research in machine learning and related fields.

- Be able to read and understand research papers and translate them into practical applications.

- Contribute to the product development process, working closely with product managers and other stakeholders to identify opportunities for applying machine learning to solve real-world problems.

- Tackle challenging technical problems related to machine learning and backend engineering.

- Develop creative and effective solutions.

- Optimize model performance, scalability, and efficiency.

- Identify and address bottlenecks in the system.

- Develop and execute unit tests, integration tests, and end-to-end tests for ML models and backend systems.

- Deploy models to production environments.

- Collaborate effectively with other engineers, data scientists, and product managers.

- Communicate technical concepts clearly and concisely.

Requirements :

- 7+ years of experience in software engineering, with a focus on machine learning.

- Strong mathematical foundation and the ability to understand and implement research papers.

- Proven backend engineering skills (must clear a backend engineering assessment).

- Experience in ML/quantitative domains (excluding traditional CV/NLP).

- Proven ability to work independently and as a high-velocity individual contributor.

- Product-oriented mindset with strong problem-solving skills.

- Degree from a Tier 1 college/university.

Nice-to-Have :

- Experience in both startup and big tech environments.

- Exposure to building and scaling data-driven products.

- Background in quantitative finance, optimization, or similar fields.

- Experience with cloud computing platforms (AWS, GCP, Azure).

- Familiarity with MLOps tools and practices