Senior Machine Learning Engineer at Samsara

Job description

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: Safety AI builds the ML and computer vision systems behind Samsara's AI dash cameras which enable real-time driver alerts, risk signals, and coaching insights running on millions of edge devices and the cloud. This role owns what happens after training: building the resilient, low-latency ML backend systems that turn static model artifacts into high-throughput, cloud-scale safety features. You will partner closely with applied scientists, firmware and full-stack engineers, and product managers and you will build the ML APIs, data pipelines, and evaluation infrastructure that let Safety AI models run efficiently at fleet scale, closing the loop from initial integration through rollout monitoring and iteration to a trustworthy, customer-facing signal. This is ML engineering where the stakes are real: rare, high-consequence events, millions of vehicles, and a product where "it works" means someone got home safely. Kindly refer to this video . This is a remote role open to candidates residing in the US or Canada. Technical Charter and Impact: Own the cloud-side path from model artifact to production system for Safety AI's ML applications. Establish practical standards for productionizing models — how they're served, evaluated, versioned, and monitored once they leave applied science. Set a high bar for reliability: rigorous evaluation, measurable rollout health, and systems that degrade predictably rather than silently. Act as a technical partner to applied scientists, helping translate research outputs into systems that are debuggable, scalable, and cost-efficient in production. In this role, you will: Design Production ML APIs: Architect and maintain reliable, low-latency APIs to integrate Safety AI model outputs directly into cloud applications. Build Data Flywheels: Construct scalable pipelines to power continuous model iteration, backtesting, shadow and online evaluation, enabling fast and safe deployments. Optimize & Serve Artifacts: Productionize model artifacts handed off by applied scientists, optimizing serving logic and fine-tuning models for platform-specific workloads. Petabyte-Scale Operations: Process high-volume camera and sensor telematics data to support model execution, backtesting, and automated dataset curation. Monitor & Maintain Rollout Health: Build systems to track model drift, precision/recall, and latency regressions in production, ensuring predictable failure modes and closed-loop data feedback. Cross-Functional System Integration: Partner with firmware and platform teams to optimize edge-to-cloud model execution, balancing latency, throughput, and infrastructure cost. Product Collaboration: Work with product managers to translate safety requirements into scalable technical architectures. 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: 6+ years of experience as a Machine Learning Engineer or similar role, with a track record of shipping models in production. Strong proficiency in one or more common languages (e.g., C++, Golang, Java, Python, Scala). Proficiency with common ML tools (e.g. Ray/Ray Serve, MLflow, Grafana, Pytorch, Spark, etc). Experience deploying and iteratively refining models using real customer feedback loops. Comfort with full-stack/backend development — you understand the data structures and dependencies underneath your models. BS or MS in Computer Science or a related quantitative field. An ideal candidate also has: Ph.D. in Computer Science or a quantitative discipline (e.g., Applied Math, Physics, Statistics). Experience with containerization (Docker, Kubernetes), CI/CD pipelines, and infrastructure-as-code frameworks. Experience deploying and managing ML applications in cloud environments (AWS/GCP/Azure), including cloud-based storage, processing, and inference. Experience shipping end-to-end ML applications from artifact to production, ideally in safety-critical or high-scale domains. Expertise optimizing distributed model training with GPUs. The compensation range shown spans approved pay zones for this role. Your primary work location determines the range applicable to you, which may be narrower than the full range displayed. 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 — $286,000 USD Total Rewards 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. Flexible Working At Samsara, we embrace a flexible working model that caters to the diverse needs of our teams. Our offices are open for those who prefer to work in-person and we also support remote work where it aligns with our operational requirements. For certain positions, being close to one of our offices or within a specific geographic area is important to facilitate collaboration, access to resources, or alignment with our service regions. In these cases, the job description will clearly indicate any working location requirements. Our goal is to ensure that all members of our team can contribute effectively, whether they are working on-site, in a hybrid model, or fully remotely. All offers of employment are contingent upon an individual’s ability to secure and maintain the legal right to work at the company and in the specified work location, if applicable. Belonging at Samsara At Samsara, we welcome everyone regardless of their background. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, gender, gender identity, sexual orientation, protected veteran status, disability, age, and other characteristics protected by law. We depend on the unique approaches of our team members to help us solve complex problems and want to ensure that Samsara is a place where people from all backgrounds can make an impact. Accommodations Samsara is an inclusive work environment, and we are committed to ensuring equal opportunity in employment for qualified persons with disabilities. Please email accessibleinterviewing@samsara.com or click here if you require any reasonable accommodations throughout the recruiting process. Our Commitment to Authenticity We use Tofu , a fraud detection tool, to validate the authenticity of applications and protect against identity fraud. This ensures we are connecting with real people and allows us to prioritize genuine candidates. Please see Samsara’s Candidate Privacy Notice for more information. Fraudulent Employment Offers Samsara is aware of scams involving fake job interviews and offers. Please know we do not charge fees to applicants at any stage of the hiring process. Official communication about your application will only come from emails ending in @samsara.com, @us-greenhouse-mail.io or @mail3.guide.co. For more information regarding fraudulent employment offers, please visit our blog post here .

Requirements

Must-haves

Own the cloud-side path from model artifact to production system for Safety AI's ML applications.
Establish practical standards for productionizing models — how they're served, evaluated, versioned, and monitored once they leave applied science.
Set a high bar for reliability: rigorous evaluation, measurable rollout health, and systems that degrade predictably rather than silently.
Act as a technical partner to applied scientists, helping translate research outputs into systems that are debuggable, scalable, and cost-efficient in production.
Design Production ML APIs: Architect and maintain reliable, low-latency APIs to integrate Safety AI model outputs directly into cloud applications.
Build Data Flywheels: Construct scalable pipelines to power continuous model iteration, backtesting, shadow and online evaluation, enabling fast and safe deployments.
Optimize & Serve Artifacts: Productionize model artifacts handed off by applied scientists, optimizing serving logic and fine-tuning models for platform-specific workloads.
Petabyte-Scale Operations: Process high-volume camera and sensor telematics data to support model execution, backtesting, and automated dataset curation.
Monitor & Maintain Rollout Health: Build systems to track model drift, precision/recall, and latency regressions in production, ensuring predictable failure modes and closed-loop data feedback.
Cross-Functional System Integration: Partner with firmware and platform teams to optimize edge-to-cloud model execution, balancing latency, throughput, and infrastructure cost.
Product Collaboration: Work with product managers to translate safety requirements into scalable technical architectures.
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.
6+ years of experience as a Machine Learning Engineer or similar role, with a track record of shipping models in production.
Strong proficiency in one or more common languages (e.g., C++, Golang, Java, Python, Scala).
Proficiency with common ML tools (e.g. Ray/Ray Serve, MLflow, Grafana, Pytorch, Spark, etc).
Experience deploying and iteratively refining models using real customer feedback loops.
Comfort with full-stack/backend development — you understand the data structures and dependencies underneath your models.
BS or MS in Computer Science or a related quantitative field.
Ph.D. in Computer Science or a quantitative discipline (e.g., Applied Math, Physics, Statistics).
Experience with containerization (Docker, Kubernetes), CI/CD pipelines, and infrastructure-as-code frameworks.
Experience deploying and managing ML applications in cloud environments (AWS/GCP/Azure), including cloud-based storage, processing, and inference.
Experience shipping end-to-end ML applications from artifact to production, ideally in safety-critical or high-scale domains.
Expertise optimizing distributed model training with GPUs.
The compensation range shown spans approved pay zones for this role. Your primary work location determines the range applicable to you, which may be narrower than the full range displayed.

Nice-to-haves

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Keywords to mirror

aimlmachine learninggolangjavapythondockerkubernetesci/cdawsgcpazure

Platform: greenhouse
Effort: quick

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Vision

Parental Leave

Professional Development