At Samsara, we embrace a flexible working model that caters to the diverse needs of our teams.
Key Responsibilities
The Samsara Machine Learning / Computer Vision team builds end-to-end applications to power different product pillars at Samsara. We are looking for a talented applied scientist 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.
This is a remote position open to candidates residing in the United States. This position requires travel up to 5% of the time. Relocation assistance will not be provided for this role.
Build and improve the accuracy of ML / CV models, including retraining and optimizing models to solve Samsara-specific problems.
This will contribute to our R&D roadmap across our four product categories including: Video-based safety - Key focus. Vehicle telematics. Equipment monitoring. Workforce apps and Site Visibility.
Work with petabyte-scale data from Samsara camera and sensor devices to develop new models.
Optimize models for inference on the backend and/or on edge devices.
Partner with firmware and full-stack teams to deploy model for optimal performance and cost.
Stay connected to industry and academic research and adopt novel technology that suits Samsara’s needs.
Collaborate with product team to translate customer needs to ML / CV solutions.
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.
Requirements
BS or MS in Computer Science or other technical degree with 5+ years of experience as an Applied Scientist, Machine Learning Engineer, or similar role working on Computer Vision; or
Ph.D. in Computer Science or quantitative discipline (e.g., Applied Math, Physics, Statistics) with 3+ years of experience working on Computer Vision.
Strong proficiency in one or more common languages (e.g., C++, Golang, Java, Python).
Proficiency with common ML tools (e.g., Spark, TensorFlow, PyTorch).
Familiarity with managing data processing and machine learning code via GitHub.
Excellent problem-solving skills and the ability to work alone and collaboratively.
Experience implementing ML models on large datasets 100K to 1M and more on the edge.
Experience building, deploying, and optimizing ML models on the edge.
Experience in state-of-the-art models for road segmentation, road object detection and tracking.
Experience working with cross-functional teams on a project.
Comfortable with full-stack/backend development code to build a strong understanding of underlying data structures and other dependencies.
The range of annual base salary for full-time employees for this position is below. Please note that base pay offered may vary depending on factors including your city of residence, job-related knowledge, skills, and experience. This role is also eligible for an initial RSU grant with no vesting cliff, and ongoing refresh opportunities tied to performance, subject to plan terms and conditions. Learn more about our total rewards and benefits below.
Annual Base Salary
$170,170
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$286,000 USD
Benefits & Perks
At Samsara, we build for the people who keep the global economy moving. We want owners, not passengers, which is why our rewards are designed to fuel high-impact builders. Our compensation program delivers above-market total compensation through a combination of base salary, performance-based bonus/variable pay, and equity (for eligible roles) in a high-growth public company. We meaningfully differentiate pay for our top performers, who have the opportunity to earn above-market compensation that can outpace the broader market over time.
Beyond compensation, we provide the foundations that enable long-term success: a flexible, employee-led remote model, a professional development stipend, comprehensive health and parental leave plans, and more. If you’re ready to build for the long term and own the outcome, your journey starts here.
Ready to Apply?
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