{"id":3580147,"url":"https://alion.io/job/raydar-forward-deployed-engineer-29","title":"Forward Deployed Engineer","company":{"id":4478753,"name":"Raydar","domain":"raydar.com","url":"https://alion.io/company/raydar-com","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":null},"role":"Solutions","role_family":"Solutions","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":144000,"max":200000,"currency":"USD","period":"year","gross":null,"usd_annual":200000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Computer Vision","optional":false},{"name":"Docker","optional":false},{"name":"Kubernetes","optional":false},{"name":"Linux","optional":false},{"name":"Physical AI","optional":false},{"name":"Python","optional":false}],"status":"live","first_seen_at":"2026-08-21T00:00:00Z","employer_posted_date":"2026-08-21","last_verified_at":"2026-11-03T03:24:24Z","board_verified":true,"closed_at":null,"days_open":74,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":74},"description":"Our client is building the leading platform for computer vision and physical AI. More than one million developers use the platform to manage image data, annotate datasets, train models, and deploy computer vision systems through APIs and edge devices. The company works with 65% of the Fortune 100, particularly across global manufacturing, logistics, automotive, robotics, and other physical industries. Founded in 2019, the approximately 70-person company has raised more than $99 million, including a $77.5 million Series B.\nThis Forward Deployed Engineer will take customer computer vision deployments from validated proof of concept to reliable production operation. You will embed with strategic customers, build the technical system around their use case, and solve problems that appear only in factories, warehouses, construction sites, and other physical environments.\nWhat you will do:\nOwn customer-facing technical deployments from zero-to-one build through adoption, production launch, and post-deployment maintenance.\nEmbed on-site with customers approximately 40% to 50% of the time to move validated proofs of concept into production.\nBuild and configure data pipelines, edge devices, computer vision models, and supporting infrastructure.\nWrite production-grade Python and solve issues involving lighting variability, camera calibration, model drift, networking, and edge-hardware constraints.\nWork across Docker, Kubernetes, Linux, NVIDIA Jetson, industrial cameras, computer vision, MLOps, and edge-computing systems.\nBuild trust with customer executives, engineers, and floor operators.\nSurface field insights to Product and Engineering and improve the core platform.\nDocument deployment architectures, create runbooks, and hand successful customers to implementation teams.\nRequirements\nApproximately 1 to 8 years of experience in forward deployed engineering, field engineering, solutions architecture, customer-facing software engineering, or a closely related role.\nDemonstrated ownership of a full customer-facing technical deployment from initial build through production adoption and maintenance.\nStrong Python software engineering skills and experience shipping production systems.\nHands-on experience with systems-level work using Docker, Kubernetes, networking, and Linux.\nExperience with computer vision, MLOps, edge computing, robotics, automation, industrial software, IoT, or related physical systems.\nAbility to communicate and build trust with executives, engineers, and front-line operators.\nHighly motivated, coachable, low-ego, eager to learn, responsive to feedback, and collaborative.\nComfort working independently in ambiguous field environments and taking responsibility for follow-through.\nWillingness and ability to travel approximately 40% to 50% for customer deployments.\nBachelor's degree in computer science, engineering, or a related technical field.\nUnrestricted authorization to work in the United States. Visa sponsorship and transfers are not available.\nNice to have: experience in manufacturing, logistics, automotive, robotics, automation, NVIDIA Jetson, industrial cameras, model monitoring, retraining pipelines, personal hardware projects, or other hands-on physical-system deployments.\nBenefits\n$144,000 to $200,000 base salary and $180,000 to $250,000 on-target earnings with uncapped variable compensation, plus competitive equity. Benefits include full family health-insurance coverage, a $4,000 annual travel stipend, a $350 monthly productivity stipend, and a $350 monthly AI-tools budget. Remote-first within the United States, aligned with U.S. daytime hours, with approximately 40% to 50% travel. 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