• Develop user-facing APIs, SDKs, and tools that streamline access to Mindbeam’s AI infrastructure. • Partner with research and product teams to translate complex ML workflows into clear, usable abstractions. • Optimize interfaces for scalability, security, and performance. • Advocate for the developer experience by gathering feedback and iterating rapidly. • Collaborate cross-functionally to ensure interfaces integrate seamlessly with enterprise environments.

Role Description The School of Nursing at Eastern Kentucky University invites applications for a part-time online facilitator position. Applicants should hold a minimum of a master's degree in nursing or a related field from a regionally accredited institution and an unencumbered nursing license. The position may serve the online RN-BSN, MSN (Family Nurse Practitioner or Psychiatric Mental Health Nurse Practitioner) or Doctorate in Nursing programs and requires a minimum of two years nursing experience. If applying to facilitate in FNP or PMHNP specific courses, then the applicant must hold an unencumbered licensed as a FNP or PMHNP. Qualifications - Master's of Science in Nursing or a related field from a regionally accredited institution. - Unencumbered Registered Nurse license from the state of Kentucky or compact state. - Minimum 2 years experience as a licensed nurse. Requirements - Unencumbered license as a registered nurse in Kentucky or a compact state. Preferred Qualifications - Master's or doctoral degree in nursing or a related field from a regionally accredited institution. - Unencumbered nursing license. - Minimum of two years nursing experience. - If applying to facilitate in FNP or PMHNP specific courses, must hold an unencumbered license as a FNP or PMHNP. Job Duties - This position is not eligible for visa sponsorship. EEO Statement Eastern Kentucky University is an Equal Opportunity employer and educational institution and does not discriminate on the basis of age (40 and over), race, color, religion, sex, sexual orientation, gender identity, gender expression, pregnancy, ethnicity, disability, national origin, veteran status, or genetic information in the admission to, or participation in, any educational program or activity (e.g., athletics, academics and housing) which it conducts or any employment policy or practice. Background Check Statement Offers of employment are contingent upon a satisfactory background check.

• Work with partner ML and Annotation engineers and TPMs to spec out infrastructure and training requirements • Design and maintain robust CI/CD and CT (Continuous Training) pipelines for complex multimodal models • Implement versioning and storage strategies for massive 2D/3D datasets to ensure reproducibility and high-throughput access • Deploy and manage systems for monitoring model performance and data drift in production environments

Role Description We are looking for a senior machine learning expert and data analyst to help us design, extend, and operate financial risk scoring systems at scale. You’ll work on models and pipelines that process hundreds of terabytes of data and power decisions where accuracy, explainability, and robustness matter. This role sits at the intersection of machine learning, fintech analytics, and big-data engineering. You’ll help evolve our scoring algorithms, improve signal quality, and ensure our models remain reliable and interpretable in production environments. We’re especially interested in someone who can combine strong ML theory, hands-on data engineering, and pragmatic fintech experience. What You’ll Do - Design and improve financial risk scoring algorithms and models. - Analyze large-scale datasets (hundreds of TBs in Elasticsearch and related systems). - Build and maintain data processing pipelines for feature generation, training, and evaluation. - Develop ML models for anomaly detection, fraud detection, credit/risk scoring, and behavioral analysis. - Validate models for accuracy, bias, stability, and drift over time. - Ensure models are explainable, auditable, and production-ready. - Work closely with engineering teams to deploy models into production systems. - Optimize performance and cost across large-scale data infrastructure. - Define metrics, dashboards, and monitoring for model performance. - Investigate edge cases and failure modes in scoring systems. Qualifications - Senior-level experience in machine learning and data analysis (5+ years). - Strong background in financial risk, fintech analytics, or fraud detection. - Experience building and deploying production ML models. - Strong Python ecosystem skills (NumPy, pandas, scikit-learn, PyTorch/TensorFlow, etc.). - Experience with large-scale data processing (100s of TBs). - Deep experience with Elasticsearch or similar distributed data stores. - Experience designing data pipelines (batch and/or streaming). - Strong statistical reasoning and experimentation skills. - Ability to translate business risk concepts into measurable model features. - Experience evaluating model drift, bias, and long-term stability. Requirements - Experience with real-time scoring systems. - Experience with distributed compute frameworks (Spark, Beam, Flink, etc.). - Familiarity with regulatory or compliance-sensitive environments. - Experience with graph-based risk models or transaction network analysis. - Experience building internal analytics tools or dashboards. - Knowledge of feature stores and model versioning systems. Benefits - Competitive salary + performance incentives. - Equity aligned with long-term growth. - High ownership and direct exposure to leadership. - Remote-first with global team. - Health and sports benefits. - Yearly international team off-sites. - Work on high-impact financial risk systems. - Tackle real-world ML challenges at large scale. - Influence the architecture of our scoring and analytics platform. - Collaborate with experienced engineers and data specialists. - Own meaningful parts of our data and ML strategy. How to Apply Send us: - A short introduction and relevant experience. - Examples of ML or risk models you’ve worked on. - Links to projects, papers, or code (if available). We’re particularly interested in candidates who can demonstrate experience building robust financial risk models on very large datasets and bringing them successfully into production.