Senior Manager, Safety AI

Samsara; United States; Remote; USD 180,000 - 280,000; Full time

Job summary

United States Digital Space LLC is seeking a Senior Manager of Machine Learning to lead end-to-end safety AI initiatives for on-device and cloud platforms. You will fuse sensor data, guide model lifecycle, and partner with firmware, full-stack, and platform teams to deliver high-precision, low-latency safety insights across millions of vehicles.

Expect to own architecture, oversee experiments, and drive measurable reductions in risk.

Qualifications

• Proven experience leading ML initiatives in production.
• Strong background in on-device and cloud AI systems.
• Experience integrating sensor data (IMU, GPS, telemetry) for safety applications.

Responsibilities

• Define and own the Safety AI platform architecture across edge and cloud.
• Lead a cross-functional team to ship low-latency safety features.
• Drive transitions from prototyping to fleet-scale production.
• Evaluate emerging AI capabilities and determine readiness for deployment.
• Measure impact via safety metrics and fleet outcomes.

Skills

ML leadership

Edge computing

Computer vision

Data fusion (IMU/GPS)

Model lifecycle management

Experiment design

Tools

Cloud platforms

VLMs / foundation models

Job description

Who we are

the company (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 the company, 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 the company 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 Safety AI team builds end-to-end computer vision and machine learning for the company’s AI dash cameras – delivering real-time driver safety insights and in-cab alerts.

As a Sr. Manager of Machine Learning, you will lead teams that fuse on-device computer vision with IMU, GPS, and vehicle diagnostic data to detect, interpret, and surface actionable safety events and continuous risk signals.

You’ll own the model lifecycle across edge and cloud – data curation, training, evaluation (including shadow/online experiments), deployment, monitoring, and iteration—and develop the core AI platform that powers these capabilities at fleet scale. You’ll partner closely with firmware, full-stack, and Platform teams to ship reliable, low-latency features, grounding detections in the “rules of the road” (e.g., stop signs and speed limits) to maximize precision and customer impact.

Your success will be measured by real outcomes: safety events caught, accident rates reduced, model reliability at production scale, and the velocity at which your team can move from research idea to deployed feature on real vehicles driven by real people.

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.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: the company 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 the company 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.

In this role, you will:Drive the Safety AI Platform Architecture

• Own the technical direction for the company's on-device and cloud safety AI systems — making architectural decisions about what runs on constrained edge hardware vs. cloud, how models are packaged and deployed across more than 2M dashcams, and how the data flywheel is built to drive continuous improvement.

• Lead the transition from individual model deployments to a coherent, system-level platform — one that fuses camera, IMU, GPS, and vehicle telemetry data to detect, interpret, and surface safety events with high precision and low latency.

• Evaluate and integrate emerging AI capabilities (e.g. VLMs, foundation models, multi-task architectures) and determine when they are ready to move from research to production at fleet scale.

Ship Safety AI Features with Measurable Customer Impact

• Partner with product and full-stack teams to define, scope, and ship safety features that reduce accident rates, improve driver coaching outcomes, and deliver quantifiable value to fleet operators.

• Lead experiment design, offline/online evaluation, and go/no-go decisions for new ML-powered features — grounding every launch in real safety metrics, not just model performance benchmarks.

• Own the full model lifecycle: data curation, training, evaluation (shadow an