Applied AI Engineer: Edge & Vision Systems

Specter; San Francisco, Northern (CA, KY); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Specter is building a software-defined control plane for the physical world and is hiring an applied AI software engineer to take models from research to production across edge devices and cloud inference services. You will own the evaluation infrastructure, data pipelines, and deployment mechanics that determine customer-visible improvements.

You will work between research and product surfaces, shaping metrics and feedback loops, and collaborating to scale model iterations across a distributed

Qualifications

• Experience taking ML models from research code to production systems that real users depend on.
• Strong Python skills, with working knowledge of PyTorch and modern inference runtimes (ONNX, TensorRT, or similar).
• Track record building evaluation pipelines and defining metrics for model performance in deployment, including handling distribution shift and unlabeled production data.
• Experience with data pipelines and annotation workflows for computer vision or other perception domains.
• Comfort with infrastructure: Docker, Kubernetes, cloud GPU services, and CI/CD for model artifacts.
• Familiarity with computer vision (object detection, multi-object tracking, re-identification) or vision-language models.
• Experience with streaming or event-driven data systems (Kafka, Redpanda, or equivalent).
• Product instinct — you can reason about which model improvements will actually change what a customer experiences.
• Interest in physical AI, IoT, or large-scale distributed sensing systems.

Responsibilities

• Productionize models from the research team, spanning containerization, inference optimization, and deployment to edge devices and cloud GPU infrastructure.
• Build and own offline and online evaluation systems that measure model quality against production traffic, not just benchmark datasets.
• Design data feedback loops that surface failure cases, route them to labeling, and turn them into training and evaluation sets.
• Define and instrument the metrics that matter for perception quality in deployment (false positives per camera per day, identity switch rate, latency budgets, drift over time).
• Ship product features that depend on new model capabilities, working end to end from inference service to API to the behavior a customer sees.
• Run experiments and staged rollouts across the fleet, including shadow deployments and A/B comparisons between model versions.
• Manage inference cost and performance tradeoffs across hardware targets, from constrained edge compute to serverless GPU providers.
• Collaborate with research and platform to make model iteration a routine deployment rather than a bespoke project.

Skills

Python

PyTorch

Evaluation pipelines

Data pipelines

Deployment

Tools

Docker

Kubernetes

CI/CD

ONNX

TensorRT

Kafka

Job description

Specter is building a software-defined control plane for the physical world and is hiring an applied AI software engineer to take models from research to production across edge devices and cloud inference services. You will own the evaluation infrastructure, data pipelines, and deployment mechanics that determine customer-visible improvements.

You will work between research and product surfaces, shaping metrics and feedback loops, and collaborating to scale model iterations across a distributed