Top Machine Learning Jobs in Lincoln, NE

Leads enterprise security reviews, threat modeling, architecture assessments, governance, and control development for AI, generative AI, machine learning, LLM, RAG, and AI-agent solutions. Defines security standards and guardrails, evaluates platforms and vendors, advises cross-functional stakeholders, and supports responsible AI adoption while managing cybersecurity, privacy, regulatory, and operational risks.

Leads Helium 10’s global product management function across the United States and China. Owns multi-quarter roadmaps, new-product discovery and delivery, direct-to-consumer and omnichannel expansion, AI-native product development, customer discovery, commercialization, analytics, and cross-functional execution. Manages and develops product managers, establishes operating standards, ensures timely high-quality releases, and communicates business outcomes to executive stakeholders.

Drive new business revenue across the Western Territory by selling complex healthcare technology and services, including AI-enabled EHR and revenue cycle management solutions. Manage the complete enterprise sales cycle from prospecting, discovery, demonstrations, proposals, negotiations, and closing through accurate forecasting and CRM updates. Build relationships with healthcare decision-makers, manage RFPs, develop territory plans, and travel 50–75% of the time.

Build and lead North’s Applied AI engineering team, taking AI solutions from prototype to production in regulated financial-services workflows. Responsibilities include architecting agentic systems, chatbots, copilots, and ML models; establishing secure enterprise integrations; managing evaluations, monitoring, CI/CD, incident response, and cost controls; and setting technical strategy and responsible-AI governance. The role requires close collaboration with business, product, risk, compliance, legal, and security stakeholders.

Own cloud security across AWS and Azure, including identity, network, application, container, data, and AI security. Build policy and infrastructure controls as code, integrate security into CI/CD, protect regulated health data, and manage detection, incident response, vulnerability remediation, and compliance. The role also develops AI security guardrails and coaches engineering teams while operating production security controls and supporting on-call responsibilities.

Leads enterprise AI solution strategy, architecture, pre-sales, and implementation delivery for customers across the Americas. Defines AI use cases, validates feasibility, designs scalable architectures, guides Professional Services teams, and resolves complex technical challenges. Develops AI service offerings, methodologies, reusable assets, and enablement programs while mentoring architects, consultants, and project leaders. Requires deep experience with enterprise transformations, LLMs, multi-agent systems, generative AI, data architecture, cloud platforms, and production AI deployments.

Own enterprise technical deployment roadmaps, milestones, acceptance criteria, governance, and post-launch adoption. Coordinate Product, Engineering, Delivery, Support, and Sales teams to resolve blockers, manage resources, map workflows, track KPIs, and demonstrate ROI. Partner with account executives on technical expansion opportunities and establish scalable deployment playbooks, PMO templates, and integration patterns.

Leads AI governance, risk assessment, security, privacy, and compliance programs for generative and agentic AI systems. Coordinates cross-functional teams, reviews customer and regulatory requirements, establishes AI agent management structures, automates governance controls, manages technical improvement projects, supports audits, delivers training, and reports risks and metrics to senior leadership. The role requires extensive cybersecurity experience, AI security and threat modeling expertise, framework compliance, project management, and executive communication.

Own the vision, strategy, and roadmap for Root’s pricing platform and machine learning infrastructure. Lead engineering and partner with data science, actuarial, and engineering teams to improve pricing workflows, model development, deployment, monitoring, and regulatory operations. Drive self-service tooling, LLM-enabled data science workflows, platform reliability, and nationwide expansion while translating business needs into product requirements and prioritized investments.

Own technical evaluations, deployments, and production integrations for major government and enterprise customers. Build Python, SQL, agents, tools, and REST integrations within customer environments; lead proofs of concept, deployments, executive briefings, and technical account relationships. Triage bespoke versus reusable capabilities, influence the product roadmap, and review other engineers’ work. The role requires strong cloud, containerization, data, communication, and ambiguous problem-solving skills.

Partner with Account Executives on discovery, demos, proof-of-value engagements, RFPs, and enterprise security reviews. Translate on-chain risk into business terms for executives and technical stakeholders, develop customized demonstrations, advise customers on integrations and compliance, and represent the company at industry events. Collaborate with Product, R&D, Security Engineering, and Customer Success to influence product direction, improve sales processes, and support customer onboarding and adoption.

Develop data-driven solutions to complex real-world problems using data science, machine learning, statistical modeling, and analytical reasoning. Candidates should currently work in a technical data science or machine learning role at a leading technology company, hold a quantitative undergraduate degree from a top-ranked university, and reside and work in an eligible English-speaking country.

Design and develop scalable backend systems using Kotlin, Micronaut, and Spring. Build RESTful APIs, optimize PostgreSQL and Elasticsearch databases, implement RabbitMQ-based asynchronous processing, integrate AI tools, and create automated tests. Deploy and monitor services on GCP using Docker, Kubernetes, and Terraform. Collaborate cross-functionally, participate in code reviews, troubleshoot performance issues, and deliver reliable backend products.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Design and deploy production-ready LLM pipelines and agentic workflows, automate large-scale multimodal dataset pipelines, monitor and improve agent performance, and integrate AI agents with 3D scene composition. The role also involves process documentation, incident response, and continuous improvement, with collaboration across the engine team and company founders.

Lead technical strategy and architecture for Azure Storage geo-replication, data protection, failover orchestration, and business continuity. Drive cross-team delivery, operational excellence, telemetry, automation, AI-assisted operations, and represent the team in executive and customer engagements.

Leads commercial analytics and business intelligence strategy, including executive dashboards, account reporting, revenue and pricing analytics, operational KPIs, forecasting, segmentation, and predictive modeling. Partners with Sales, Finance, Marketing, Operations, and Product to improve revenue, profitability, customer retention, and marketplace performance. Presents insights to executives and customers while managing analytics teams, budgets, vendors, and BI scalability.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

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.

Provides bioinformatics consultation, computational analysis, statistical support, and production pipeline development for HIV-related genomic and clinical research at NIAID. Responsibilities include sequence and gene expression analysis, variant calling, genome assembly, comparative genomics, proteomics, and epigenomic workflows. The role supports scientific programming, experimental design, data dissemination, scalable research resources, and publications. Candidates need expertise in programming, workflow frameworks, containerization, high-performance computing, and next-generation sequencing, with HIV research experience preferred.

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.

Leads technical direction for GitLab’s Core DevOps services, covering CI, CD, planning, and source control. Defines architecture, quality metrics, migration strategies, and technical roadmaps; addresses systemic risk and complex design decisions; prototypes and writes code when needed; partners with Infrastructure, Security, SRE, and Product; adopts AI capabilities; mentors senior engineers; supports incident response and senior technical hiring.

Own customer deployments of Sayari’s risk screening, graph analytics, investigation workflows, and data integrations. Responsibilities include technical scoping, production Python and SQL development, API integration, bulk data delivery, pipeline onboarding, dashboard creation, monitoring, troubleshooting, documentation, and feeding recurring customer needs back to engineering. The role requires client-facing communication, independent problem-solving, and collaboration across multiple customer accounts, with 25–30% travel and in-office time.

Designs and maintains automated, AI-enabled detection and response capabilities across enterprise security platforms. Integrates SIEM, EDR, SOAR, and threat intelligence technologies; analyzes security telemetry; develops and validates detection content and automation workflows; documents procedures and runbooks; and collaborates with SOC, threat intelligence, and incident response teams. The role also evaluates emerging security capabilities and supports continuous improvement of detection operations for a federal agency.

Paid, part-time Creative Advisor collaborating with AI research and machine learning teams. The role involves sharing creative expertise, defining quality standards, developing work, reviewing model outputs, identifying limitations, and helping fellow creators engage with the program. Applicants should be leading creators with strong portfolios and interest in shaping AI’s impact on creative practice; no AI experience is required.