This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description Black Hawk College seeks an Adjunct/part-time instructor to teach online non-western art history courses for the Art Discipline. This position can be taught fully remote. - Courses may include ART 285 Survey of Asian Art and ART 286 Survey of Non-Western Art. - These courses are offered each semester and meet institutional non-western requirement for all transfer degrees. - Interested applicants should have experience in one or more areas of non-western art history and can teach social, historical, and philosophical contexts of artworks. Qualifications - ART HISTORY - M.A., M.F.A. or Ph.D. in Art History required. - Emphasis on Non-Western Art and studio art preferred. - Teaching experience in art appreciation and art history courses preferred. - Must possess demonstrated oral English proficiency for classroom instruction. Requirements - Faculty members are expected to encourage learning by preparing appropriate syllabi. - Develop lectures, discussions, and other presentations or activities to enhance the student’s educational experience. - Develop and execute appropriate methods of evaluating student performance. - Develop, secure, and maintain the equipment and other instructional materials essential to the presentation of the classroom material. - Other duties may be assigned as required. Benefits - Please upload a cover letter, resume/CV, and teaching philosophy. - Letter of recommendations may be included but not required. - Any instructor hired at Black Hawk College will be required to submit official transcripts at his/her own expense. - Transcripts, once submitted, become property of Black Hawk College.

• Design and operate ML platforms that support end-to-end workflows: data ingestion, feature engineering, training, evaluation, deployment, and monitoring. • Build and maintain CI/CD for ML (testing, packaging, versioning, reproducibility, automated rollbacks, approvals). • Implement MLOps best practices: model registry, experiment tracking, lineage, governance, and reproducible training environments. • Develop scalable training infrastructure (distributed training, GPU scheduling, cost controls, auto-scaling). • Create and maintain feature pipelines / feature stores, ensuring consistency between training and inference (training-serving skew prevention). • Establish model monitoring and observability: performance, drift, bias/fairness signals (where relevant), latency, throughput, and data quality. • Build and own end-to-end LLM delivery pipelines: prompt/versioning, retrieval, orchestration, evaluation, deployment, monitoring, and iterative improvement. • Create robust LLM evaluation harnesses (offline + online): golden datasets, automated regression testing, human-in-the-loop review workflows, and risk scoring. • Build cost controls: token/cost budgeting, caching strategies, autoscaling, and performance tuning. • Productionize ML Models on GCP using containers and orchestration (e.g., GKE, Cloud Run), and build CI/CD for ML/LLM systems with automated tests and safe rollouts. • Implement observability: tracing, metrics, logs, dashboards, alerting for model/system health (latency, token usage, error rates, retrieval quality, hallucination indicators, drift where relevant). • Design systems with security and privacy by default: IAM, least privilege, secrets management, audit logs, encryption, data retention, and PHI/PII handling. • Implement governance: model/prompt lineage, dataset provenance, evaluation traceability, and approval workflows aligned with healthcare compliance expectations. • Integrate guardrails: content filters, policy checks, prompt injection defenses, structured output validation, and fallback strategies.

• The task involves the development and upkeep of data pipelines for training and evaluating models • Exploratory data analysis will be conducted to enhance model accuracy and performance • Experiments and evaluations of machine learning models will be carried out to identify and address any issues

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are currently seeking a Deep Learning Researcher. A place to put to use your hard-earned experience and show some state-of-the-art work in a highly competitive environment where all your skills will be put to test. The team you’ll join is working on developing solutions based on DL with state-of-the-art performance in terms of accuracy/speed/scalability. - Proficient skills in ML, in particular in one of Deep Learning domains. Qualifications - Proficient knowledge in one of Deep Learning domains (CV, NLP, TimeSeries, Audio, etc.) - Experience in developing scalable and reproducible ML pipelines - Competency in conducting experiments to facilitate rapid iterations and validate hypotheses Requirements - Top ranks in competitions with prizes and objective metrics (Kaggle, TopCoder, DrivenData, etc.) - Experience in Time Series tasks Benefits - Great challenges with fast feedback loops - A welcoming group of highly qualified international professionals - Cutting-edge hardware and technology - Work remotely from anywhere in the world - Access any of our global offices anytime - Flexible schedule - 40 paid days off - Competitive salary