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.

Own the technical side of deal progression for airline deployments: qualify opportunities, run demos and POCs, author integration and deployment architectures (PSS, APIs, data feeds, DNS, identity, networking), respond to RFPs/security questionnaires, scope SOWs and NFRs, and build reusable demo/reference assets to drive adoption and expansion while liaising between sales, engineering, and customers.

Lead data science projects from ideation through enterprise impact, building scalable and well-tested solutions that improve healthcare outcomes. Apply probability, statistics, machine learning, SQL, and Python to develop data-driven products and insights. Partner with stakeholders, assess product performance, identify strategic improvements, mitigate risks, and mentor teammates while aligning initiatives with business objectives.

Own and build Nectar Social’s React Native mobile platform for iOS and Android. Responsibilities include establishing architecture, shared components, testing, CI/CD, performance standards, monitoring, and error tracking. The engineer will develop AI-powered experiences, integrate social and e-commerce APIs, support real-time data-intensive features, and collaborate with product, design, and company leadership to shape the product and technical direction.

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 ecosystem-level data science and econometric analyses across Xbox, Game Pass, Console, PC, and Store experiences. Evaluate growth, engagement, monetization, experiments, incrementality, and investment ROI using statistical and causal inference methods. Build reusable analytical products and self-service capabilities, influence executive decisions, and mentor team members.

Leads deployment and ongoing technical support for Direct-to-Store Delivery technology solutions. Manages implementation schedules, SLAs, technical issue resolution, integrations, documentation, customer satisfaction, and continuous improvement. Coaches support teams, coordinates with product and operations groups, oversees user acceptance testing and machine learning data integrity, and partners with customers to improve solution adoption, performance, and retention. Requires technical expertise across RFID, IoT, APIs, databases, and Linux environments, plus up to 30% travel.

Own end-to-end strategy, roadmap, delivery, adoption, safety, and commercial readiness for AI-native cybersecurity products. Conduct customer and partner discovery, lead 0-to-1 launches, define experimentation and evaluation frameworks, and collaborate with engineering, design, sales, finance, and executives. The role requires hands-on LLM product expertise, strong judgment in ambiguous environments, and a focus on trusted AI experiences for security professionals and MSP/MSSP partners.