Top Senior Machine Learning Jobs in Dayton, OH

Develops compliance analytics for AML, sanctions, and consumer compliance programs. Builds data pipelines, dashboards, scorecards, automated reporting, and data quality processes using dbt, GitLab, Python, SQL, Tableau, and Snowflake. Translates regulatory requirements into actionable insights, risk indicators, and metrics while communicating findings to senior management and regulatory stakeholders.

Leads control management for JPMorganChase’s Consumer and Community Banking Operations portfolio. Designs and strengthens risk and control frameworks, identifies emerging compliance and operational risks, drives governance and issue management, and promotes consistent agile control practices. Partners with Product, Technology, Legal, Compliance, Audit, and Risk stakeholders to support transformation initiatives, regulatory compliance, control effectiveness, and operational resilience. Requires strong risk-based decision-making, change management, stakeholder influence, executive communication, and data literacy.

Lead technical teams developing, testing, integrating, and demonstrating signal processing, optimization, and machine learning algorithms for radar, electronic warfare, and advanced sensor systems. Responsibilities include RF system modeling and simulation, algorithm test and evaluation, performance analysis in laboratory and field settings, systems integration, customer communication, and translating mission requirements into research solutions.

Lead go-to-market strategy for Sprout Social’s AI portfolio, including the Trellis AI agent. Develop enterprise launch playbooks, positioning, executive messaging, campaigns, and enablement programs. Analyze market and competitive dynamics, influence product roadmaps, improve launch processes, and partner with Product, Sales, Revenue, Brand, and executive leadership to drive adoption, revenue growth, deal expansion, and category leadership. The role also mentors marketing team members and operates with substantial autonomy.

Lead agile design and development projects for mobile apps and digital products. Coordinate product, design, engineering, marketing, leadership, and client stakeholders; manage project plans, scope, requirements, backlogs, acceptance criteria, deadlines, workflows, and deliverables. Support scoping and client relationship management while ensuring successful end-to-end software launches and alignment with business objectives.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Own the strategic vision and roadmap for Coursera’s AI-driven search experience. Lead conversational search, personalization, semantic and multilingual search, ranking, and discovery improvements. Partner with engineering, design, research, data science, ML, marketing, and partnerships teams to deliver scalable platform capabilities. Use experimentation, market analysis, KPIs, and customer insights to improve conversion, merchandising, learner experience, and revenue.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Product Counsel will advise product, engineering, data science, marketing, compliance, and sales teams throughout the product lifecycle. Responsibilities include counseling on regulatory compliance, privacy, intellectual property, consumer protection, AI and machine learning, data rights, platform architecture, APIs, identity and account security, and marketing. The role translates legal requirements into technical guidance, supports AI governance, monitors evolving laws, and develops scalable legal processes for a fast-paced technology company.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

Engineer AI-powered threat detections, automated response playbooks, and continuous adversary emulation. Apply machine learning and LLMs to security operations, conduct threat hunting and AI red teaming, map coverage to MITRE ATT&CK, and develop detection-as-code through CI/CD. Build production-grade Python integrations, measurable containment capabilities, and safe autonomous defenses with guardrails. Mentor security professionals and collaborate across threat response, cyber defense engineering, and platform teams.

Design, build, and operate high-throughput, low-latency ad-serving infrastructure processing millions of requests per second. Optimize GPU-powered TensorRT inference, feature stores, model serving, benchmarking, experimentation tooling, and system reliability. Partner with machine learning engineers to productionize models and own systems through design, deployment, observability, capacity planning, incident response, and optimization.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Develop and transition advanced OPIR data-processing algorithms for infrared sensing and space-based missile warning systems. Responsibilities include evaluating processing pipelines, troubleshooting anomalies, supporting technical documentation and transition plans, and collaborating with government, intelligence, research, and industry partners. The role requires expertise in EO/IR signal processing, infrared phenomenology, OPIR data formats, Python development, and AI/ML applications.

Develops and deploys AI/ML and intelligent automation workflows for complex intelligence datasets. Builds statistical models, data pipelines, dashboards, and scalable software outputs within secure cloud infrastructure. Integrates data strategies with Zero Trust Architecture, supports reporting and technology prototyping, and collaborates with engineers, database administrators, and product owners. Requires active TS/SCI clearance and onsite support at Wright-Patterson AFB.

Lead development and transition of advanced OPIR data-processing algorithms for infrared sensing and space-based missile warning systems. Evaluate pipelines, troubleshoot sensor and software issues, analyze data anomalies, and develop technical documentation and transition plans. Collaborate with NGA, Department of Defense, Intelligence Community, research organizations, and external agencies on advanced sensing technologies.

Lead Client Legal Services analytics strategy and team development. Own Power BI reporting products, Microsoft Fabric data solutions, governance, predictive modeling, responsible AI adoption, ROI measurement, and executive insights. Partner cross-functionally to improve legal operations, litigation outcomes, staffing, capacity, cost management, quality, and workflow optimization while building scalable analytics capabilities and institutional knowledge.

Build and lead scalable frontend products for Airbnb’s Community Support Engineering organization. Responsibilities include architecting web applications, developing reusable frontend components, designing supporting server-side APIs, implementing testing and experimentation, optimizing accessibility and performance, collaborating with product, design, machine learning, and generative AI teams, shaping technical direction, and mentoring junior engineers.

Lead end-to-end product design for an AI-powered women’s health platform. Responsibilities include defining UX vision, creating interaction models, information architecture, wireframes, high-fidelity interfaces, prototypes, design systems, and user research. Collaborate with product, engineering, ML, marketing, and leadership teams to translate business needs into customer-focused solutions, manage design initiatives, and measure product success through user feedback and metrics.

Build and operate high-scale internationalization infrastructure, including translation workflows, APIs, UIs, libraries, and platform tooling. Improve legacy systems through migrations while enhancing reliability, observability, latency, security, and cost efficiency. Prototype and productionize machine-learning and large-language-model capabilities, collaborate across engineering and business teams, and deliver measurable improvements in quality, adoption, developer productivity, and operational efficiency.

Leads the transformation of Sophos’s pricing operations by building a centralized function, establishing pricing governance and review cadences, developing AI-assisted competitive and discount analysis capabilities, and aligning pricing across SecOps and Network Security. The role provides executive market visibility, defines performance metrics, supports organizational change, and builds a high-performing team serving sales, finance, and customer-facing stakeholders.

Build and deploy production machine learning systems for personalized recommendations, fraud detection, risk scoring, ranking, classification, and anomaly detection. Own the full ML lifecycle, including mathematical formulation, feature engineering, training, evaluation, A/B testing, deployment, monitoring, and reliability. Develop low-latency real-time inference services using large-scale donor and transaction data, collaborate with Platform and DevOps teams, and mentor engineers on ML best practices and scientific rigor.