{"schemaVersion":"jobsearcher.job.v1","id":"ff4f5b5bbc0e4c05baa1dfb1","url":"https://jobsearcher.com/jobs/ff4f5b5bbc0e4c05baa1dfb1","canonicalUrl":"https://jobsearcher.com/jobs/ff4f5b5bbc0e4c05baa1dfb1","title":"Software Engineer - Applied AI","description":"Company Background:\n\nSpecter is creating a software-defined \"control plane\" for the physical world. We are starting with providing ubiquitous perception over physical assets.\n\nTo do so, we are creating a connected hardware-software ecosystem on top of multi-modal wireless mesh sensing technology. This allows us to drive down the cost and time of deploying sensors by 10x. Our platform will ultimately become the perception engine for a company's physical footprint, enabling real-time perimeter visibility and autonomous operations management.\n\nOur co-founders Xerxes and Philip are passionate about empowering our partners in the fast approaching world of physical AI and robotics. We are a small, fast growing team who hail from Anduril, Tesla, Uber, and the U.S. Special Forces.\n\nThe Role\n\nSpecter is hiring an applied AI software engineer to own the path from model to product. You'll take detection, tracking, and vision-language models from research prototypes to reliable production systems running across a distributed fleet of edge devices and cloud inference services, then close the loop by measuring how those models actually perform in the field and feeding that signal back into the next iteration. This role sits between our research team and our product surfaces, owning the evaluation infrastructure, data pipelines, and deployment mechanics that determine whether a model improvement becomes a customer-visible improvement.\n\nResponsibilities:\n\nProductionize models from the research team, spanning containerization, inference optimization, and deployment to edge devices and cloud GPU infrastructure.\n\nBuild and own offline and online evaluation systems that measure model quality against production traffic, not just benchmark datasets.\n\nDesign data feedback loops that surface failure cases, route them to labeling, and turn them into training and evaluation sets.\n\nDefine 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).\n\nShip product features that depend on new model capabilities, working end to end from inference service to API to the behavior a customer sees.\n\nRun experiments and staged rollouts across the fleet, including shadow deployments and A/B comparisons between model versions.\n\nManage inference cost and performance tradeoffs across hardware targets, from constrained edge compute to serverless GPU providers.\n\nCollaborate with research and platform to make model iteration a routine deployment rather than a bespoke project.\n\nQualifications:\n\nExperience taking ML models from research code to production systems that real users depend on.\n\nStrong Python skills, with working knowledge of PyTorch and modern inference runtimes (ONNX, TensorRT, or similar).\n\nTrack record building evaluation pipelines and defining metrics for model performance in deployment, including handling distribution shift and unlabeled production data.\n\nExperience with data pipelines and annotation workflows for computer vision or other perception domains.\n\nComfort with infrastructure: Docker, Kubernetes, cloud GPU services, and CI/CD for model artifacts.\n\nFamiliarity with computer vision (object detection, multi-object tracking, re-identification) or vision-language models.\n\nExperience with streaming or event-driven data systems (Kafka, Redpanda, or equivalent).\n\nProduct instinct — you can reason about which model improvements will actually change what a customer experiences.\n\nInterest in physical AI, IoT, or large-scale distributed sensing systems.","company":"Specter","rawCompany":"specter","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-08-31T11:00:11.044Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Software Engineer - Applied AI","description":"Company Background:\n\nSpecter is creating a software-defined \"control plane\" for the physical world. We are starting with providing ubiquitous perception over physical assets.\n\nTo do so, we are creating a connected hardware-software ecosystem on top of multi-modal wireless mesh sensing technology. This allows us to drive down the cost and time of deploying sensors by 10x. Our platform will ultimately become the perception engine for a company's physical footprint, enabling real-time perimeter visibility and autonomous operations management.\n\nOur co-founders Xerxes and Philip are passionate about empowering our partners in the fast approaching world of physical AI and robotics. We are a small, fast growing team who hail from Anduril, Tesla, Uber, and the U.S. Special Forces.\n\nThe Role\n\nSpecter is hiring an applied AI software engineer to own the path from model to product. You'll take detection, tracking, and vision-language models from research prototypes to reliable production systems running across a distributed fleet of edge devices and cloud inference services, then close the loop by measuring how those models actually perform in the field and feeding that signal back into the next iteration. This role sits between our research team and our product surfaces, owning the evaluation infrastructure, data pipelines, and deployment mechanics that determine whether a model improvement becomes a customer-visible improvement.\n\nResponsibilities:\n\nProductionize models from the research team, spanning containerization, inference optimization, and deployment to edge devices and cloud GPU infrastructure.\n\nBuild and own offline and online evaluation systems that measure model quality against production traffic, not just benchmark datasets.\n\nDesign data feedback loops that surface failure cases, route them to labeling, and turn them into training and evaluation sets.\n\nDefine 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).\n\nShip product features that depend on new model capabilities, working end to end from inference service to API to the behavior a customer sees.\n\nRun experiments and staged rollouts across the fleet, including shadow deployments and A/B comparisons between model versions.\n\nManage inference cost and performance tradeoffs across hardware targets, from constrained edge compute to serverless GPU providers.\n\nCollaborate with research and platform to make model iteration a routine deployment rather than a bespoke project.\n\nQualifications:\n\nExperience taking ML models from research code to production systems that real users depend on.\n\nStrong Python skills, with working knowledge of PyTorch and modern inference runtimes (ONNX, TensorRT, or similar).\n\nTrack record building evaluation pipelines and defining metrics for model performance in deployment, including handling distribution shift and unlabeled production data.\n\nExperience with data pipelines and annotation workflows for computer vision or other perception domains.\n\nComfort with infrastructure: Docker, Kubernetes, cloud GPU services, and CI/CD for model artifacts.\n\nFamiliarity with computer vision (object detection, multi-object tracking, re-identification) or vision-language models.\n\nExperience with streaming or event-driven data systems (Kafka, Redpanda, or equivalent).\n\nProduct instinct — you can reason about which model improvements will actually change what a customer experiences.\n\nInterest in physical AI, IoT, or large-scale distributed sensing systems.","datePosted":"2026-08-31T11:00:11.044Z","dateModified":"2026-08-31T11:00:11.044Z","hiringOrganization":{"@type":"Organization","name":"Specter","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"ff4f5b5bbc0e4c05baa1dfb1"},"url":"https://jobsearcher.com/jobs/ff4f5b5bbc0e4c05baa1dfb1"}}