{"id":159936,"url":"https://alion.io/job/buzz-solutions-senior-computer-vision-machine-learning-engineer-2","title":"Senior Computer Vision & Machine Learning Engineer","company":{"id":64333,"name":"Buzzsolutions","domain":"buzzsolutions.com","url":"https://alion.io/company/buzzsolutions","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":91,"open_postings":7,"ghost_share":0,"stale_share":0.143,"repost_share":0,"time_to_fill_p50_days":172,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":135000,"max_usd":244000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":314},"experience_years_min":5,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Computer Vision","optional":false},{"name":"Docker","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Git","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NumPy","optional":false},{"name":"OpenCV","optional":false},{"name":"Pydantic","optional":false},{"name":"Pytest","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"Scikit-learn","optional":false}],"status":"live","first_seen_at":"2026-08-28T23:17:37Z","employer_posted_date":"2026-09-01","last_verified_at":"2026-10-17T15:31:46Z","board_verified":true,"closed_at":null,"days_open":49,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":49},"description":"Job Description \nBuzz is revolutionizing the analytics and maintenance of power grid infrastructure through our advanced AI solutions. Our computer vision systems analyze critical infrastructure to enhance safety, reliability, and operational efficiency across the power grid network.\nWe're looking for a Machine Learning Engineer to advance our computer vision initiatives and help build our foundational model capabilities. You'll bridge the gap between cutting-edge research and production systems, reading papers, adapting novel algorithms, and turning them into reliable, deployed models for power grid analysis. You'll work within a team of experienced ML engineers, with the autonomy to drive your own projects and the support to keep growing. You'll operate with a high degree of autonomy.\nResponsibilities\nProject delivery \nOwn and deliver end-to-end computer vision projects focused on: Equipment defect detection \nThermal anomaly identification\nVegetation encroachment monitoring\nSurveillance of closed areas for human and animal intrusion \n\nScope, plan, and execute your own projects from problem framing through production deployment and monitoring. \nDeliver on client projects, translating client requirements and raw data into working computer vision solutions. \nContribute to shared team projects, coordinating with other engineers to deliver against common milestones. \nResearch and experimentation \nStay current with ML/CV research, identify promising methods, and evaluate their applicability to our domain. \nAdapt and implement algorithms from papers, validating against baselines and benchmarking for production viability. \nBring the latest advances in deep learning and generative AI to bear on model training, accuracy, and reliability. \nDesign and execute experiments with systematic hyperparameter tuning, ablation studies, and appropriate baselines. \nPerform structured error analysis: categorize failure modes (false positives, missed detections, localization errors, misclassifications) and break down performance by data slices (object size, occlusion, image quality). \nSelect and justify model architectures based on task requirements, latency, and accuracy tradeoffs. \nEngineering and production \nDevelop production-grade Python libraries for the complete ML lifecycle. \nDesign and implement data pipelines including ingestion, preprocessing, annotation workflows, and quality monitoring. \nOwn experiment tracking and model versioning: configurations, random seeds, dataset versions, environment specs, and model checkpoints. \nBuild model serving pipelines that meet latency and throughput requirements. \nConduct thorough code reviews and write integration tests for ML pipelines. \nCollaboration and craft \nShare knowledge with teammates and contribute to best practices for model development, evaluation, deployment, and monitoring. \nAdvocate for and uphold software quality standards within the ML team. \nCommunicate research findings, technical decisions, and model limitations clearly to stakeholders and clients. \nQualifications & Experience\n5-10 years of industry experience in computer vision and machine learning. \nDeep expertise in modern computer vision and deep neural networks, including:Object detection\nSemantic segmentation\nImage classification\nVision transformers and foundation models\nVision language models\nSimilarity search \n\nProven track record of deploying and maintaining ML models in production. \nExperience selecting, fine-tuning, and adapting model architectures (CNNs, transformers, foundation models) for specific use cases. \nDemonstrated ability to read ML research papers, extract the key ideas, and implement them. \nAbility to debug training instabilities and conduct systematic error analysis. \nProficiency in Python and the core ML stack:PyTorch and Lightning\nOpenCV\nNumPy and pandas\nScikit-Learn\nFastAPI and Pydantic \n\nStrong software engineering practices, including:Git version control \nUnit and integration testing (Pytest)\nCI/CD pipelines (GitHub Actions)\nDocker and reproducible environments\nExperiment tracking and model versioning\nML DevOps\nPython type hinting \n\nProven ability to own technical projects independently, from problem framing through production deployment. \nDesired Additional Experience\nMulti-modal computer vision \nCustom object detection model development\nGenerative models for data augmentation\nML deployment on edge devices\nExtracting measurements from GIS and/or drone metadata enriched imagery\nModel quantization\nSystematic hyperparameter tuning\nAdditional information:\nThis position does not include sponsorship for United States work 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