Who we are
Samsara (NYSE: IOT) is the pioneer of the Connected Operations™ Cloud, which is a platform that enables organizations that depend on physical operations to harness Internet of Things (IoT) data to develop actionable insights and improve their operations. At Samsara, we are helping improve the safety, efficiency and sustainability of the physical operations that power our global economy. Representing more than 40% of global GDP, these industries are the infrastructure of our planet, including agriculture, construction, field services, transportation, and manufacturing — and we are excited to help digitally transform their operations at scale.
Working at Samsara means you’ll help define the future of physical operations and be on a team that’s shaping an exciting array of product solutions, including Video-Based Safety, Vehicle Telematics, Apps and Driver Workflows, and Equipment Monitoring. As part of a recently public company, you’ll have the autonomy and support to make an impact as we build for the long term.
About the role
The Samsara AI team builds end-to-end AI solutions for our customers as well as core ML infrastructure for Samsara. As a Staff Machine Learning Engineer, you will be working with petabyte-scale sensor, diagnostic, video, and text data to solve critical problems for Physical Operations customers, globally. You will work closely with ML Engineers and Scientists, as well as full-stack and firmware engineers to deliver core product features, services, and optimizations.
In this role, you will:
- Lead design and implementation of critical AI product initiatives on Edge devices.
- Develop both tactical AI solutions as well as more strategic and longer term research.
- Work with petabyte-scale data from customer operations including text, transactions, diagnostics, sensor, camera, and location data.
- Partner across business units to explore and prototype new AI experiences and optimize the ML model performance on edge devices.
- Stay connected to industry and academic research and adopt novel technology that suits Samsara’s needs.
- 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.
Total Rewards
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. The range of annual base salary for full-time employees for this position is $170,400—$234,300 CAD. 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.
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.
Minimum requirements for the role:
- 8+ years experience as a Machine Learning Engineer.
- Profound experience in optimizing ML models and systems for Edge compute constraints.
- Coding in python or similar.
- Coding in C++ or Rust.
- Strong functional knowledge of the iterative machine learning product development process.
- Experienced in developing and shipping production code at large scale.
- Ability to distill informal or ambiguous customer and business requirements into crisp problem definitions.
- Proven ability to communicate verbally and in writing to technical peers and leadership teams with various levels of technical knowledge.
- Experience coaching and mentoring ML Engineers.
An ideal candidate also has:
- Proficiency in self-serving with data for experiments and model training at scale.
- An established record of successful high impact deliveries in AI.
- Deep knowledge in state of the art Computer Vision and multi-model models.
