{"id":1303370,"url":"https://alion.io/job/sourceability-principal-computer-vision-scientist-2","title":"Principal Computer Vision Scientist","company":{"id":1880443,"name":"Sourceability","domain":"sourceability.com","url":"https://alion.io/company/sourceability","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":103000,"max_usd":246000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":636},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"CI/CD","optional":false},{"name":"CLIP","optional":false},{"name":"CNN","optional":false},{"name":"Computer Vision","optional":false},{"name":"Docker","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"OCR","optional":false},{"name":"OpenCV","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Rest API","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false},{"name":"Transformers","optional":false},{"name":"YOLO","optional":false},{"name":"Azure","optional":true},{"name":"Azure DevOps","optional":true},{"name":"Core ML","optional":true},{"name":"Git","optional":true},{"name":"LiteRT","optional":true},{"name":"Multimodal AI","optional":true},{"name":"ONNX","optional":true},{"name":"OpenAI","optional":true},{"name":"OpenVINO","optional":true},{"name":"Prompt Engineering","optional":true},{"name":"Synthetic Data","optional":true},{"name":"TensorRT","optional":true},{"name":"TFLite","optional":true}],"status":"live","first_seen_at":"2026-07-16T15:45:47Z","employer_posted_date":"2026-08-18","last_verified_at":"2026-09-26T12:39:37Z","board_verified":true,"closed_at":null,"days_open":72,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":72},"description":"Sourceability® is a global digital distributor of electronic components transforming how modern businesses bring products to market. With innovation, qualityandlogisticsas the backbone of the company, Sourceability’scutting-edgeproducts and services expeditethe procurement process across a wide range of industries, including communications/cellular, consumer electronics, and auto manufacturing. \nSourceability is building a new Global Engineering Organization (GEO) to strengthen internal software delivery, improve production ownership, and build long-term engineering capability inside the company.\nWe are looking for a Principal Computer Vision Scientist to lead advanced Computer Vision and AI / ML work inside GEO. This role will be responsible for research direction, model architecture, experimentation, model quality, production readiness, and practical implementation of computer vision solutions used in company products.\nThis is a senior technical leadership role for a highly experienced specialist who can work across research, engineering, product, and production systems. The right candidate should be able to evaluate new approaches, design model architectures, run experiments, improve model quality, and help engineering teams bring AI / ML capabilities into real production workflows.\nThis role requires PhD-level education and strong hands-on experience in applied Computer Vision, Machine Learning, and Deep Learning. The person in this role should be comfortable working with business-critical systems, practical production constraints, imperfect datasets, and evolving product requirements.\nAssigned Product Group\nThis role will be primarily aligned with the Computer Vision product group inside GEO.\nThe role may also support other internal product groups or AI / ML initiatives where computer vision, image processing, visual search, object detection, segmentation, classification, or model evaluation expertise is needed.\nProduct Group Focus Areas\nDepending on business priorities, the role may focus on one or more of the following areas:\nMobile App / Computer Vision: Image capture workflows, mobile application integration, computer vision model development, model inference, warehouse / field usability, user feedback loops, production model quality, and UAT support.\nAI / ML Product Features: Applied machine learning features, model evaluation workflows, proof-of-concepts, model-assisted automation, AI-assisted business tools, and integration of AI / ML capabilities into existing business workflows.\nData and Annotation Workflows: Image datasets, data quality, annotation requirements, labeling guidelines, model training datasets, validation datasets, failure case analysis, and continuous improvement of model performance.\nProduction ML Systems: Model deployment, model versioning, inference performance, monitoring, reproducibility, scalability, reliability, and MLOps practices.\nInsight on Your Impact\nIn this role, you will:\nLead research, design, development, and implementation of Computer Vision and AI / ML solutions.\nDefine model architecture, technical approach, experiment strategy, validation methodology, and production readiness criteria.\nTrain, fine-tune, evaluate, optimize, and deploy models for object detection, semantic segmentation, image classification, feature matching, OCR, visual search, and image understanding.\nOwn the full model lifecycle, including data analysis, dataset quality, annotation requirements, model training, experiment tracking, evaluation, deployment, monitoring, and continuous improvement.\nBuild prototypes, proof-of-concepts, demos, and technical experiments to validate new ideas before full product implementation.\nAnalyze model performance, identify failure cases, and recommend practical improvements based on data, user behavior, and business needs.\nReview and improve existing Computer Vision pipelines, model quality, inference performance, scalability, and production reliability.\nWork with software engineers to integrate ML models into production applications and services.\nDefine standards for model evaluation, model versioning, dataset management, reproducibility, and MLOps practices.\nEvaluate research papers, open-source models, AI platforms, and new technologies for potential use in company products.\nProvide technical guidance and mentoring to engineers working on AI / ML and computer vision features.\nSupport planning and estimation for AI / ML work by clarifying technical complexity, risks, dependencies, and realistic delivery assumptions.\nCreate technical documentation, model evaluation reports, architecture notes, and recommendations for engineering and product teams.\nPartner with Product / Delivery Managers to translate business needs into practical AI / ML implementation plans.\nPartner with Engineering Managers, Team Leads / Architects, QA, DevOps, Data, and business stakeholders to make sure AI / ML work can be delivered and supported in production.\nYour Qualifications, Your Influence\nTo be successful in this role, you should have:\nPhD in Computer Science, Computer Vision, Machine Learning, Artificial Intelligence, Applied Mathematics, Electrical Engineering, Robotics, or closely related technical field.\n7+ years of hands-on experience in Machine Learning / Deep Learning, with strong focus on Computer Vision.\nStrong practical experience with PyTorch and / or TensorFlow.\nStrong Python development skills.\nExperience with OpenCV, NumPy, Pandas, scikit-learn, and modern Python ML ecosystem.\nDeep understanding of classical Computer Vision algorithms and modern deep learning approaches.\nStrong experience with object detection, semantic segmentation, image classification, feature matching, image retrieval, and model evaluation.\nExperience with modern Computer Vision architectures and techniques, including CNNs, Transformers, Vision Transformers, YOLO, Mask R-CNN, CLIP-like models, SAM-like models, or similar.\nExperience bringing ML models into production environments.\nExperience with model optimization for inference speed, latency, memory usage, scalability, and reliability.\nExperience with REST APIs, Docker, CI / CD, model versioning, experiment tracking, and MLOps practices.\nStrong understanding of datasets, data quality, annotation processes, labeling requirements, and model error analysis.\nAbility to read, understand, and evaluate technical documentation and research papers in English.\nAbility to explain complex technical topics to engineering, product, and business stakeholders.\nExperience working in Agile software development environment.\nStrong ownership mindset, good judgment, and ability to make practical technical decisions under uncertainty.\nComfortable working in distributed teams across multiple locations and time zones.\nPreferred Skills and Technical Familiarity\nThe following experience will be helpful:\nPost-PhD research or industry experience in applied Computer Vision.\nPublications, patents, or strong applied research record in Computer Vision, Machine Learning, or AI.\nExperience leading technical direction for AI / ML projects.\nExperience mentoring ML engineers, software engineers, or data annotation teams.\nExperience with edge or mobile inference technologies, including ONNX, TensorRT, OpenVINO, TFLite, CoreML, or similar.\nExperience with large-scale image processing pipelines.\nExperience with synthetic data generation, active learning, weak supervision, or dataset quality improvement.\nExperience with multimodal models, vision-language models, prompt engineering, OpenAI, or similar AI platforms.\nExperience with cloud ML platforms and production monitoring of ML models.\nFamiliarity with mobile applications, warehouse workflows, field operations systems, or image capture workflows.\nFamiliarity with Azure DevOps, Git, CI / CD tooling, documentation systems, and practical software delivery processes.\nExperience in electronic components, technology distribution, supply chain, logistics, manufacturing, e-commerce, or similar B2B environments.\nSuccess in the First 90 Days\nWithin the first 90 days, the Principal Computer Vision Scientist should be able to:\nUnderstand the relevant product areas, users, business workflows, image capture workflows, datasets, model use cases, and current technical risks.\nEstablish working relationships with Engineering Managers, Team Leads / Architects, Product / Delivery Managers, engineers, QA, DevOps, Data, and business stakeholders.\nReview current Computer Vision and AI / ML work, including models, datasets, evaluation methods, annotation process, production integration, and known quality issues.\nIdentify the most important model quality risks, data quality gaps, technical debt items, production risks, and maintainability concerns.\nDefine or improve model evaluation criteria, validation process, dataset requirements, and model readiness expectations.\nHelp improve technical clarity of the active AI / ML backlog by adding design notes, technical breakdown, dependencies, estimates, and risks.\nLead at least one meaningful model improvement, prototype, evaluation effort, or production risk-reduction activity.\nImprove documentation around model architecture, data flows, evaluation results, known limitations, and production behavior.\nCreate an initial technical roadmap or remediation plan for Computer Vision work aligned with product priorities and engineering capacity.\nHelp onboard or mentor engineers working on Computer Vision, AI / ML, or related product features.\nWhat This Role Does Not Own\nThis role does not own formal people management for engineers. Engineering Managers remain responsible for hiring, performance management, compensation input, team structure, and capacity planning.\nThis role does not own business prioritization or user acceptance. Product / Delivery Managers and business stakeholders remain responsible for intake, priority alignment, backlog readiness, UAT coordination, and business acceptance.\nThis role does not independently commit delivery dates without alignment with Engineering Managers and Product / Delivery Managers.\nThis role does not own all AI / ML work across the company unless specifically assigned by GEO leadership. The primary responsibility is Computer Vision technical leadership and production-quality AI / ML implementation for assigned product areas.\nThis role does not replace Software Architects, DevOps, Data, QA, or Infrastructure ownership. The role will work closely with those teams to make sure Computer Vision solutions are technically sound, production-ready, and supportable.\nEQUAL OPPORTUNITY EMPLOYER. \nIt is our policy to abide by all federal, state and local laws prohibiting employment discrimination based on a person’s race, color, religious creed, sex, national origin, ancestry, citizenship status, pregnancy, childbirth, physical disability, mental and/or intellectual disability, age, military status, veteran status (including protected veterans), marital status, registered domestic partner or civil union status, familial status, gender (including sex stereotyping and gender identity or expression), medical condition (including, but not limited to, cancer related or HIV/AIDS related), genetic information, sexual orientation, or any other protected status.","description_format":"text","description_chars":11283,"description_truncated":false,"requirements":{"experience_years_min":7,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Robotic Software & Control Systems","Transportation & Logistics","Supply Chain","Electronic Components Distribution"],"lifecycle":[{"event":"open","at":"2026-09-26T12:39:37Z"}],"liveness":{"score":21,"band":"cold","label":"Long shot","p_open":1,"p_active":0.583,"p_room":0.36,"age_days":72,"expected_fill_days":42,"reasons":["conf:13","win:tail","crowd:"],"computed_at":"2026-09-27T02:08:06Z"},"pay":null,"html_url":"https://alion.io/job/sourceability-principal-computer-vision-scientist-2","json_url":"https://alion.io/job/sourceability-principal-computer-vision-scientist-2.json","meta":{"generated_at":"2026-09-27T02:08:06Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":1892,"day_limit":5000,"remaining_today":3108,"minute_limit":60,"resets_at":"2026-09-28T00:00:00Z"}}}