Top Machine Learning Jobs in Lincoln, NE

Lead end-to-end technical strategy for customer accounts, design scalable data and AI architectures, and drive Databricks adoption and consumption. Advise senior technical stakeholders, lead architecture discussions, build custom solutions for competitive opportunities, coordinate cross-functional resources, and provide product feedback. The role requires strong Python and SQL skills, distributed data systems expertise, public-cloud deployment experience, and the ability to translate technical concepts into business value.

Own the roadmap and product decisions for data science products powered by NEXUS. Partner with data scientists, engineering, research, design, and applied AI teams to build self-service workflows, AI agents, MLOps capabilities, and predictive model monitoring. Conduct customer discovery, define requirements and success metrics, prioritize roadmap decisions using data and user evidence, and guide products from discovery through launch and iteration.

Lead product strategy for integrated hardware and software ecosystems serving the public sector. Oversee cameras, IoT, robotics, software, and AI/ML products across their full lifecycle, from concept and NPI through manufacturing, deployment, and end-of-life. Manage and scale the product organization, guide roadmap and P&L decisions with data, develop go-to-market strategies, and align engineering, supply chain, commercial teams, executives, and the Board.

Leads Kaseya’s market and competitive intelligence function, including competitor analysis, market sizing, customer segmentation, Voice of Customer, win/loss research, marketing effectiveness, and predictive analytics. Advises executives on market trends, customer behavior, competitive threats, and growth opportunities. Partners with product, sales, marketing, data science, and revenue operations, embeds insights into sales enablement, and builds a high-performing intelligence team.

Leads company-wide data science priorities, KPI frameworks, measurement standards, causal modeling, scenario planning, and agentic analytical automation. Builds reproducible SQL and Python workflows, validates predictive and causal relationships, establishes trusted metrics, partners with Finance, Product, Commercial, Engineering, and Data Engineering, and mentors scientists. The role drives adoption of governed self-service analytics and communicates assumptions, uncertainty, and business trade-offs to leadership.

Lead architecture and technical strategy for a remote engineering organization building a React, Node.js, PostgreSQL, and AWS platform. Mentor engineers, establish engineering standards, drive JavaScript-to-TypeScript migration, design ETL and AI/LLM integration systems, resolve technical debt, and oversee technical design reviews, production support, incident response, hiring, and delivery. Partner with product, clinical, and business stakeholders to build scalable, reliable software.

Lead technical discovery and solution design for U.S. Government customers adopting trusted AI platforms. Develop architectures, demonstrations, prototypes, and proof-of-concepts; translate mission needs into actionable implementation approaches; present to technical and executive stakeholders; collaborate with engineering, data science, and product teams; support sales, proposals, and capture efforts; and manage concurrent customer engagements.

Develop and deploy machine learning, AI, causal inference, and statistical solutions for advertising measurement products. Own projects from prototyping through production, conduct experiments, explore large-scale data, and collaborate with product, engineering, analysts, and account teams to improve attribution and marketing ROI.

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.

Secure software and cloud environments through threat modeling, code review, vulnerability remediation, security automation, detection engineering, and attack simulations. The role partners closely with Engineering, manages Snyk and Docker Scout findings, integrates security into CI/CD, coordinates third-party penetration testing for SOC 2 and HITRUST, and helps define secure architecture for mobile, data lake, AI, and production cloud systems.

Manage technology operations across three global studios, including systems administration, infrastructure, technical support, equipment management, business and creative tool administration, vendor coordination, and troubleshooting. Partner with global IT, information security, and privacy teams on policies, compliance, and content security. Drive AI and machine-learning integrations to improve workflows and business efficiency while supporting clients and global technology initiatives.

Own and advance Wonderlic’s AI-powered Jobs Engine, using production machine learning and NLP to analyze labor-market data and infer job characteristics at scale. Partner with product, engineering, and I-O psychology teams to integrate responsible AI into assessment and workforce solutions. Apply fairness, explainability, legal defensibility, and rigorous evaluation practices to employment-related AI systems, including generative AI outputs and assessment interpretation.

Leads regional growth across Eastern USA and Canadian retail, wholesale, and distribution markets. Responsibilities include setting sales strategy, managing and coaching enterprise sellers, building C-level relationships, guiding value-based selling, developing ROI cases, improving pipeline forecasting, leveraging AI-enabled sales tools, maintaining Salesforce data, and collaborating with presales, alliances, marketing, customer success, and sales operations.

Leads client-facing management consulting and transformation programs spanning revenue operations, marketing, sales, customer growth, data, analytics, and AI. Advises senior executives, develops operating models and transformation roadmaps, leads solutioning and proposals, oversees delivery governance, and translates business needs into implementable data and technology solutions. Builds thought leadership, reusable consulting assets, and high-performing teams while supporting strategic growth and client relationships.

Leads and develops a fraud analyst team responsible for investigating emerging threats, responding to incidents, and improving fraud-risk strategies. Owns investigative standards, playbooks, workflows, and tools while serving as an escalation resource for complex cases. Partners with Data Science, Customer Success, Product, Engineering, and business leaders to convert fraud insights into scalable signals, model improvements, and better customer outcomes. Supports customer conversations as a fraud subject-matter expert and coaches analysts on communicating risk decisions.

Oversee global analytics and algorithm design initiatives for a nonprofit: identify high-impact projects, coordinate with student directors, collect feedback, design and run training for developers and technical advisors, and form partnerships to support implementation. Volunteer commitment ~5-6 hours/week.

Conduct research in fraud, voice, video, authentication, and deepfake detection. Design, implement, and evaluate deep learning models, analyze complex datasets, investigate detection gaps, and translate research prototypes into production-ready cloud or edge systems. Collaborate with research and engineering teams, contribute to code reviews, and publish patents or peer-reviewed papers.

Design, scale, and optimize ClickUp's search infrastructure using OpenSearch/Elasticsearch. Improve relevance, speed, and real-time indexing, build vector search capabilities, create evaluation frameworks, and collaborate with AI/backend/product teams to integrate and troubleshoot search at scale.

Prototype and develop generative AI applications for business software, including automated model evaluation, synthetic video, and consistent image and brand asset generation. Own projects from experimentation through product delivery, combining system architecture, quantitative reasoning, product judgment, and creative LLM application development.

Lead analytics for client engagements across data onboarding, study planning, predictive modeling, production deployment, and ongoing optimization. Develop models for audience targeting, segmentation, propensity scoring, and campaign optimization, primarily for commercial pharmaceutical applications. Partner with business, analytics, and IT stakeholders, lead technical workshops, present findings to senior audiences, and manage client relationships. Build machine learning pipelines using Python, SQL, and cloud technologies, while contributing client insights to internal product development.

Develop and maintain Power BI reports, dashboards, visualizations, semantic models, and analytics solutions. Translate business requirements into scalable technical solutions, optimize queries and data models, ensure reporting accuracy and performance, and support data governance. Serve as the primary liaison for business users, provide training and troubleshooting, manage tickets, and collaborate with Data and QA teams. The role also advances AI and machine learning capabilities, mentors colleagues, and contributes to automation, documentation, and code reviews.

Owns strategy for a defined market segment, shaping go-to-market plans, coordinating cross-functional teams, driving product adoption and revenue, and building executive customer relationships. The role uses customer and industry insights to guide product priorities, sales and services strategies, retention, and market positioning for AI-driven clinical trial software.

Design, deploy, and scale production AI applications, agentic workflows, LLM integrations, and intelligent automation across enterprise business functions. Build cloud-native services, retrieval and vector database solutions, evaluation and monitoring frameworks, AI guardrails, and reusable engineering components. Integrate AI with enterprise systems, optimize model performance and infrastructure, and support secure, governed, reliable production systems while collaborating with technical and business stakeholders.

Conduct applied AI and machine learning research focused on multimodal data, foundation models, agents, evaluation, and data enrichment. Monitor research trends, prototype emerging techniques, design experiments, validate opportunities, and translate findings into briefs and recommendations. Partner with product and engineering teams to guide data strategy, training corpus generation, and longer-term opportunity prioritization.

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.