Position summary:

We are hiring a senior technology leader to own engineering across the company. This person is accountable for the performance of our computer vision products, the reliability of the platform they run on, the cost of delivering them, and the health and output of the engineering organization.

The immediate mandate is to take a platform that works in pilot deployments and make it work predictably at several hundred store locations. That means better detection accuracy in real store conditions, a manageable fleet of edge devices in networks we do not control, a repeatable model improvement cycle, and infrastructure cost that falls per store as volume grows.

The role suits a leader who is still technically hands-on. You will set architecture and hire the team, and you will also review code, join troubleshooting calls, and occasionally stand in a store looking at a camera feed that will not connect.

Key responsibilities:

Technology strategy and architecture:

- Define and own the technical roadmap, and align it with commercial priorities and available capital.

- Make and document architecture decisions across the machine learning, edge, cloud, and application layers, including build versus buy calls.

- Balance short-term pilot commitments against the platform work required to scale, and communicate that tradeoff clearly to the executive team and board.

Computer vision and machine learning delivery:

- Own detection accuracy as a business outcome, measured by true positive rate, false positive load on store staff, and missed events against audited ground truth.

- Establish a repeatable model lifecycle covering data collection, annotation, training, evaluation, deployment, and continuous retraining from customer-verified events.

- Ensure models are optimized to run within the memory, thermal, and latency limits of the edge hardware deployed in stores.

- Set the evaluation standards and release gates that prevent regressions reaching customer sites.

Platform, edge, and reliability:

- Own the reliability of the full delivery chain, from cameras and in-store devices through to the customer-facing application, with published availability targets and an on-call model behind them.

- Build and operate remote management for the edge device fleet, covering provisioning, health monitoring, software updates, rollback, and secure access at scale.

- Own security posture and compliance continuity, including customer security reviews, penetration test remediation, access control, and maintenance of SOC 2 certification.

- Own infrastructure and hardware cost per store as a tracked engineering metric, and reduce it as deployment volume increases.

Product and customer delivery:

- Work with the CEO and commercial team to translate customer requirements into scoped, sequenced engineering work.

- Serve as the senior technical representative in enterprise pilots, integration discussions, architecture reviews, and hardware or camera partner conversations.

- Own the quality of the operator-facing application used daily by loss prevention and store operations teams.

Leadership and organization:

- Lead, mentor, and grow a distributed engineering team, including the managers and technical leads reporting into this role.

- Set hiring standards, run the technical hiring process, and build a bench for critical roles.

- Establish and enforce engineering process: planning cadence, code review, documentation, testing, release management, and incident review.

- Manage the engineering budget, vendor relationships, and cloud commitments.

- Report engineering status, risk, and metrics to the executive team and to the board.

Required qualifications:

- Experience: Ten or more years in software engineering, including at least four years leading engineering teams of twenty or more people, with distributed or offshore team experience.

- Computer vision depth: Hands-on experience building and shipping deep learning based vision systems in production, and the ability to interpret model performance data and translate it into product and business decisions.

- Edge and embedded deployment: Proven experience deploying machine learning models to GPU-accelerated edge or embedded hardware, including optimization for constrained compute, memory, and power.

- Real-time video systems: Working knowledge of streaming architecture, browser-based live video delivery, network traversal in restricted corporate environments, and the associated bandwidth and cost tradeoffs.

- Cloud and platform operations: Production ownership of cloud infrastructure at scale, including container orchestration, event-driven architecture, relational data platforms, monitoring, and CI/CD, with demonstrated cost accountability.

- Security and compliance: Direct experience carrying an organization through SOC 2 or an equivalent framework, and representing engineering in enterprise customer security reviews.

- Leadership: A track record of hiring, developing, and retaining senior engineers, and of operating effectively as part of an executive team.

- Education: Bachelor's degree in computer science, engineering, or a related technical field, or equivalent professional experience.

Preferred qualifications:

- Experience in retail technology, loss prevention, physical security, or store operations systems.

- Experience scaling a product from pilot deployment to large multi-site enterprise rollout.

- Familiarity with privacy-preserving machine learning approaches such as federated learning, on-device training, or data minimization by design.

- Startup experience between seed and Series B stage, including hiring and delivering under budget constraint.

- Experience supporting technical diligence during a fundraise or acquisition.