{"id":2254651,"url":"https://alion.io/job/platesmart-senior-applied-ml-engineer-computer-vision-rd","title":"Senior Applied ML Engineer, Computer Vision R&D","company":{"id":4401471,"name":"Platesmart","domain":"platesmart.com","url":"https://alion.io/company/platesmart","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Romania"],"countries":["RO"],"hiring_countries":["RO"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":43000,"max_usd":110000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1986},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"CLIP","optional":false},{"name":"Computer Vision","optional":false},{"name":"Data Augmentation","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Model Distillation","optional":false},{"name":"Multimodal AI","optional":false},{"name":"OCR","optional":false},{"name":"ONNX","optional":false},{"name":"ONNX Runtime","optional":false},{"name":"OpenCV","optional":false},{"name":"OpenVINO","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"Qwen","optional":false},{"name":"YOLO","optional":false}],"status":"live","first_seen_at":"2026-10-11T03:17:53Z","employer_posted_date":null,"last_verified_at":"2026-10-11T03:17:53Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"Position Overview\nWe are seeking a highly experienced computer vision expert to design, improve, optimize, and productionize advanced computer vision models and inference engines.\n\nThis role sits at the intersection of deep learning research, neural network architecture, computer vision, and high-performance inference engineering. The ideal candidate is comfortable moving well beyond simply training an existing model: they can analyze network architecture at the layer and node level, experiment with model structure and activation functions, diagnose accuracy and performance issues, optimize inference pipelines, and translate research models into production-ready computer vision engines.\nYou will work on computer vision problems involving object detection, image classification, OCR, visual embeddings, image retrieval, image similarity, attribute recognition, and multimodal / vision-language models.\nThis position requires hands-on expertise with technologies such as PyTorch, ONNX, OpenCV, OpenVINO, CNNs, Transformers, CLIP-style models, and Vision-Language Models (VLMs). \nJob is full time, remote.\nJob Responsibilities\nNeural Network Research & Architecture \nAnalyze and modify neural networks at the layer, block, channel, node, and tensor level. \nExperiment with network depth, width, feature dimensions, convolutional blocks, normalization strategies, residual connections, attention mechanisms, and output heads. \nEvaluate tradeoffs between accuracy, inference latency, model size, and memory/CPU utilization.\nExperiment with activation functions such as ReLU, Leaky ReLU, SiLU/Swish, GELU, and others.\nInvestigate the effect of input resolution, feature-map resolution, receptive field, stride, padding, kernel size, and network capacity on model performance. \nModel Training & PyTorch Development \nModify models and implement custom layers, heads, loss functions, and training strategies. \nAnalyze training behavior including convergence, overfitting, underfitting, class imbalance, hard-negative examples, and ambiguous classifications. \nDevelop effective data augmentation, normalization, preprocessing, and sampling strategies. \nPerform model ablation studies to determine which changes materially improve performance. \nDiagnose model confusion between visually similar classes (such as side-profile GMC Yukon versus Chevy Tahoe) and develop techniques to improve discriminative capability.\nOpenCV & Image Processing \nDevelop high-performance image-processing and computer-vision pipelines using OpenCV. \nImplement preprocessing and post-processing including resizing, cropping, normalization, filtering, geometric transformations, color-space conversion, thresholding, image enhancement. \nUnderstand the effect of interpolation, compression, image format, resolution on model accuracy. \nDiagnose discrepancies caused by differences between training and production image pipelines. \nObject Detection, Classification & OCR \nDevelop and improve models for: object detection, image classification, fine-grained visual classification, OCR / text recognition, attribute classification, region classification \nWork with architectures such as YOLO and other modern CNN/Transformer-based detectors. \nResearch OCR architectures such as CTC-based and sequence-based recognition models.\nAnalyze character-level confusion (such as “O” vs. “0”) and difficult visual cases. \nTune OCR network architecture, feature extraction, sequence length, decoding, and training data.\nDevelop methods for handling ambiguous characters, unusual layouts, stacked characters, low-resolution images, blur, glare, weather conditions, poor illumination, and image compression.\nONNX & Model Interoperability\nExport and convert PyTorch models to ONNX for deployment, considering ONNX computational graphs, operators, tensors, shapes, dynamic/static dimensions, and opset compatibility. \nValidate model outputs across PyTorch, ONNX Runtime, OpenVINO, C++, and other inference environments to diagnose model conversion problems and any differences in inference. \nOptimize ONNX graphs, including operator fusion, constant folding, and graph simplification.\nIdentify unsupported operators and redesign or replace network components when necessary.\nEmbeddings, CLIP & Image Search\nEvaluate embedding architectures and vector databases for visual search/similarity use cases.\nWork with CLIP, SigLIP, MobileCLIP, EVA-CLIP, and related multimodal embedding architectures. \nBenchmark embedding models for retrieval accuracy, mAP, RecallK, F1, ROC/AUC, embedding quality, NDCG, inference latency, model size, and CPU/GPU requirements.\nEvaluate tradeoffs between general-purpose foundation versus domain-specific embeddings. \nDevelop similarity search and nearest neighbor retrieval capable of operating at billion-plus scale.\nVision-Language Models & Foundation Models \nEvaluate emerging Vision-Language Models (VLMs) and multimodal foundation models. \nExperiment with models such as Qwen-VL, Florence, and other sota vision architectures to evaluate whether they can supplement or replace traditional task-specific CV models. \nDevelop approaches for zero-shot / few-shot classification, visual question answering, image understanding, semantic image search, automatic labeling, object and attribute extraction.\nAdapt large pretrained models to PlateSmart use cases through fine-tuning, distillation, adapters.\nTrack emerging research and determine which techniques have practical production value.\nProductionization & Computer Vision Engine Development \nWork with software engineers to integrate nnet models into high-volume production systems. \nDesign reusable computer vision inference components, APIs, and microservices. \nOptimize models where latency, throughput, memory/CPU constraints, and reliability are critical. \nMaintain compatibility across model versions, inference runtimes, and hardware platforms. \nParticipate in model versioning, regression testing, performance benchmarking, and 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now","p_open":1,"p_active":0.86,"p_room":1,"age_days":2,"expected_fill_days":29,"reasons":["seen:2","win:early"],"computed_at":"2026-10-13T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/platesmart-senior-applied-ml-engineer-computer-vision-rd","json_url":"https://alion.io/job/platesmart-senior-applied-ml-engineer-computer-vision-rd.json","meta":{"generated_at":"2026-10-13T12:13:24Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per 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