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Machine Learning Intern

Machine Learning Intern
San Francisco, US
72,000 - 108,000
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Job Description

About the role:

We are looking for talented data science and machine learning engineers to generate insights and draw value from rich customer interactions, as well as from the 2T+ data points our sensors and cameras collect on an annual basis. 

Our AI team focuses on analyzing Samsara’s data at scale to build compelling features for our customers and improve the foundations of our platform by applying state-of-the-art machine learning and computer vision techniques to our unique mix of IoT data and applications. Our Data Science team looks across all data sets and customer interactions to distill key insights and build models that measure and predict customer behavior. We are looking for interns to join the teams to work on high impact projects for the summer. These Summer 2023 roles will be based in our San Francisco office.

You should apply if:

  • You want to impact the industries that run our world: The software, firmware, and hardware you build will result in real-world impact—helping to keep the lights on, get food into grocery stores, and most importantly, ensure workers return home safely.
  • You want to build for scale: With over 2.3 million IoT devices deployed to our global customers, you will work on a range of new and mature technologies driving scalable innovation for customers across industries driving the world's physical operations.
  • You are a life-long learner: We have ambitious goals. Every Samsarian has a growth mindset as we work with a wide range of technologies, challenges, and customers that push us to learn on the go.
  • You believe customers are more than a number: Samsara engineers enjoy a rare closeness to the end user and you will have the opportunity to participate in customer interviews, collaborate with customer success and product managers, and use metrics to ensure our work is translating into better customer outcomes.
  • You are a team player: Working on our Samsara Engineering teams requires a mix of independent effort and collaboration. Motivated by our mission, we’re all racing toward our connected operations vision, and we intend to win—together.

Click here to learn about what we value at Samsara.

In this role, you will: 

  • Leverage SQL or Python to shape and structure large, complex data sets for analysis
  • Build statistical and machine learning models to identify patterns, anomalies, and root causes (e.g., analyzing sensor temperatures to spot unhealthy devices)
  • Meet with technical and non-technical stakeholders to understand data structures and distill business priorities
  • Work with product and full-stack engineering teams to build data-powered features in our cloud dashboard
  • Create data visualizations and other summary output to support decision-making from operational and leadership teams
  • Champion, role model, and embed Samsara’s cultural principles (Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team) as we scale globally and across new offices

Minimum requirements for the role:

  • MS or PhD candidate in engineering or a quantitative discipline (e.g., Statistics, Mathematics, Computer Science, etc.)
  • Experience with data manipulation and processing, preferably in SQL or Python (e.g., using PySpark)
  • Experience building statistical, machine learning, and other analytical models, preferably in Python (e.g., using TensorFlow, PyTorch, etc.)
  • Familiarity managing data processing and machine learning code via GitHub
  • Comfortable reading and understanding full-stack development code (e.g., Go, Java, ReactJs) to understand data logging / instrumentation, or to build front-end data components

An ideal candidate also has:

  • Some experience with data visualization (e.g., using Javascript or Python libraries)
  • Some prior internship experience in data science or data engineering role
  • Experience implementing and optimizing machine learning models on very large datasets (1B+ records)
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