Swiggy Off-Campus drive 2021 hiring for Machine Learning Engineer I
Swiggy is hiring for Machine Learning Engineer I. For insights and updates related to off-campus drives and internship drives be active on our website.
About the Team
- The Customer ML team builds data science products to power experiences beginning from the time you launch the app until the order is placed.
- On the storefront side, these include low-latency, high-throughput algos powering recommendations, personalization and monetization across the purchase funnel, Search relevance and ranking and extracting intelligence from our multi-entity catalog.
- On the revenue & growth side, we work on ML for computational advertising, intelligent discounting, customer lifecycle management, fraud/abuse prevention and new/incubating initiatives.
- We own or co-own several initiatives with a direct impact on the business. Our work involves the intersection of ML/DL, optimization, statistics, data mining, econometrics, graph theory and large-scale distributed systems. We are a group of data scientists, ML engineers and analysts.
|Profile||Machine Learning Engineer I|
- Build and deploy ML models on various platforms. We use a combination of AWS, in-house ML platform, directly integrating with services of other engineering teams
- Work with data science team to enable robust decision-making in terms of thinking about scale, latency, throughput requirements. Create tools/systems to speed-up ML lifecycle
- Play a significant role in enabling adoption of sophisticated algorithms and data mining strategies
- Contribute to the development of data-related products and services
- Solid understanding of data structures and algorithms (expected to be fluent in at least one OO or functional language)
- Minimum 1+ years of software engineering experience particularly related to productionizing ML models and scaling them in low-latency settings
- Strong knowledge of ML fundamentals and algorithms
- Proficient in Scala or Java. Exposure to Python (esp. numpy, pandas)
Nice To have
- Experience with AWS, docker/kubernetes, airflow, JIRA, confluence, etc.
- Knowledge and/or interest in deep learningnowledge and/or interest in deep learning
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