Top Senior Machine Learning Jobs in Omaha, NE

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.

Design, build, deploy, and support scalable Java microservices on AWS. Provide technical leadership, architectural direction, code reviews, troubleshooting, proof-of-concept development, and cross-functional collaboration. Work with high-throughput data, messaging systems, distributed architectures, observability, and AI-assisted development tools. Participate in on-call support, mentor engineers, lead architectural initiatives, and deliver reliable SaaS platform features.

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.

Lead product-focused quantitative analyses and machine learning efforts to generate actionable insights, drive product decisions, and optimize features. Collaborate with cross-functional teams to design metrics, build scalable statistical solutions, create dashboards, and communicate findings while using AI tools responsibly.

Leads technical customer engagements for strategic enterprise accounts, combining solution engineering, software implementation, consulting, and sales support. Responsibilities include discovering requirements, designing and documenting SailPoint solutions, conducting foundational implementations and proof-of-value engagements, educating customers on architecture and best practices, and developing AI-enabled demonstrations and agentic solutions. The role collaborates across sales, product, professional services, partners, and customer success, and may require up to 15% travel.

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.

Owns the lifecycle of AI-powered analytics products supporting clinicians, from requirements and discovery through production and maintenance. Partners with data scientists, engineers, clinicians, and executives to define roadmaps, prioritize trade-offs, establish data contracts and SLAs, validate model results, track performance, and demonstrate ROI. Leads Agile scrum teams and drives cross-functional delivery in complex healthcare environments.

Own and improve Headway’s privacy program across HIPAA, 42 CFR Part 2, state privacy laws, individual rights, incidents, vendor governance, and AI use. Maintain controls, data maps, metrics, training, and operational workflows. Conduct product, vendor, and AI privacy assessments; manage rights requests and incident investigations; coordinate notifications and regulatory submissions; and translate complex requirements into practical guidance for cross-functional teams.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Define architecture and metrics for autonomous driving evaluation, lead cross-functional integration across autonomy and simulation teams, develop statistical and machine learning methods, analyze large-scale time-series data, manage evaluation KPIs and release gates, and communicate safety and readiness insights through dashboards and stakeholder presentations.

Serves as the technical bridge between customers, Sales, and technology teams. Leads technical discovery, solution architecture, demonstrations, workshops, proofs of concept, and evaluations using HERE data, APIs, mapping, routing, spatial analytics, cloud, AI, and enterprise integration. Partners with Sales to qualify opportunities, reduce technical risk, develop proposals and reusable presales assets, and guide customers toward location-intelligence solutions.

Design and lead the technical strategy for search across Vannevar’s products. Build and improve full-text, vector, and LLM-powered retrieval systems, optimizing latency, relevance, index quality, freshness, and performance. Collaborate with ML, product, design, and mission strategy teams to translate user needs into search innovations. Apply expertise in information retrieval, NLP, machine learning, indexing, ranking, query parsing, tokenization, vector search, and evaluation frameworks.

Lead Runpod’s Americas sales organization and revenue number across new business, expansion, and renewals. Manage and coach Account Executives and Account Managers, oversee forecasting and pipeline, lead complex enterprise deals and contract negotiations, build regional go-to-market strategy and sales playbooks, and convert product-led demand into revenue. Partner with Marketing, Product, Legal, and Finance while improving sales efficiency through AI and developing a remote-first team.

Develops and validates machine learning and statistical models for anti-money laundering, transaction monitoring, customer screening, and risk rating. The role evaluates AML data quality, integrates vendor solutions, maintains governance and regulatory documentation, leads model lifecycle quality control, and reports performance through management dashboards. It also supports AML risk assessments, audits, regulatory examinations, and cross-functional stakeholder coordination.

The role involves deploying and supporting CrowdStrike's AI capabilities in cybersecurity, optimizing threat detection, and mentoring teams on AI solutions.

Design and operate large-scale, AI-native cloud infrastructure (especially Kubernetes/AKS). Improve service quality, automate incident prevention/remediation, accelerate engineering productivity, and integrate AI tools into development workflows.

Leads the design, development, and implementation of advanced Power BI business intelligence solutions. Builds reports, dashboards, semantic models, and data transformations using Power Query and DAX; analyzes complex datasets; applies AI and analytics technologies; provides technical leadership, governance, stakeholder guidance, and mentoring across multiple projects.

Design and implement automated testing frameworks for data platforms, pipelines, distributed processing systems, and machine learning workflows. Develop strategies for data quality, schema integrity, drift, model accuracy, bias, regression, performance, and resilience. Investigate production failures, conduct exploratory testing, embed continuous validation into delivery processes, collaborate across engineering and data teams, and mentor junior SDETs.