Top Machine Learning Jobs in Huntsville, AL

Leads Intermedia’s global accounting organization and financial reporting operations, including GAAP compliance, audits, tax, internal controls, revenue accounting, close processes, and business combinations. Partners with executive leadership on strategy, projections, growth initiatives, and operational improvement. Builds and mentors a high-performing global team while advancing technology-enabled automation and process efficiency across the CFO organization.

Leads technical sales and executive evangelism for Verint’s Open CCaaS and CX Automation platform. Acts as a trusted advisor to C-suite customers, articulating ROI, AI, data science, and platform capabilities. Partners with Product, Sales, Solutions, Services, Marketing, and Customer Success teams to drive adoption, expansion, thought leadership, and opportunity closure. Requires deep contact center operations experience, strong data architecture knowledge, executive communication skills, and extensive travel.

Provides strategic and technical leadership for Lilly’s eDiscovery program across the full EDRM lifecycle. Manages legal hold systems, data preservation and collection, processing, review, production, vendor relationships, workflows, and chain-of-custody requirements. Evaluates AI, machine learning, TAR/CAL, predictive coding, and analytics solutions while ensuring defensible legal processes. Partners with Legal, Privacy, Records Management, IT, Compliance, Cybersecurity, outside counsel, and business stakeholders, and mentors team members.

Lead research and engineering of production-grade LLM/GenAI systems for cybersecurity: design models and architectures, drive data labeling and evaluation, collaborate with product and engineering to deploy scalable solutions, mentor data scientists, set technical strategy, and advance AI safety, red-teaming, and applied ML innovations for detection and operational use.

Lead development and deployment of large-scale LLM and GenAI solutions for cybersecurity. Drive research strategy, model training, evaluation, safety/red-teaming, and cross-functional productization. Mentor data scientists, set technical standards, and represent the team through publications and thought leadership.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

As an ML Ops Infrastructure Engineer, you will design CI/CD pipelines for ML, maintain deployment systems, and implement monitoring while collaborating with research teams to ensure model quality and performance.

As a Systems Architect, you'll define and drive the infrastructure architecture for AI/ML workloads, design multi-cloud strategies, manage compute orchestration and storage systems, and lead capacity planning, while also collaborating with engineering leadership.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

Leads advanced data science and analytics initiatives using statistics, modeling, machine learning, and business analysis. Develops scalable policy-adherence metrics and valuation models using complex structured and unstructured healthcare data, including claims and denials. Creates reports, projections, and presentations; advises executives; manages stakeholders; and influences roadmap and implementation decisions across compliance, contracting, Medicaid, and business teams.

Source, engage, and qualify technical candidates using LinkedIn, GitHub, X‑Ray, and other tools. Build pipelines for engineering and technical leadership roles, run outreach campaigns, perform initial screening, document candidate details in ATS/CRM, and partner with recruiters to meet client hiring timelines while improving sourcing strategies.

Lead product analytics to improve user growth, engagement, retention, and revenue. Design and analyze A/B and multivariate experiments, perform funnel/cohort analyses, maintain KPIs and dashboards, ensure event tracking, leverage ML and AI tools for advanced analyses, and translate insights into product recommendations across cross-functional teams.

Lead design and scaling of a unified fraud and risk platform. Define product strategy, own fraud metrics, run SQL-driven analysis and A/B tests, and partner with engineers and data teams to build real-time detection, identity verification, and transaction monitoring. Balance customer experience with risk mitigation and communicate impact to executives, legal, and partners.

The AI/Machine Learning Specialist designs and implements AI solutions, collaborates with cross-functional teams, and continuously improves ML processes.

Design and lead end-to-end AI solution architectures, ensuring scalability, security, and compliance. Translate business and regulatory requirements into technical designs, evaluate tools/platforms, document architecture standards, coordinate technical teams, and support deployment and ongoing improvements.

Lead complex, cross-functional AI programs end-to-end: translate strategy into integrated plans, drive multi-team execution, mitigate risks (data, model, compliance), apply ML lifecycle practices, use Agile/kanban methods, manage budgets/vendors, communicate to senior stakeholders, and mentor other program managers to improve AI program standards.

Lead vision, strategy, and execution for Alkami's marketing products. Define roadmap, embed AI-driven personalization and audience intelligence, partner cross-functionally to drive adoption and revenue, recruit and develop product managers, and deliver scalable SaaS marketing and analytics solutions for financial institutions.

Design and implement machine learning models to solve business problems, analyze data for insights, and optimize model performance.

Lead a team of safety engineers to define and execute AV safety strategy across development, simulation, testing, and deployment. Apply UL4600/ISO standards, develop safety cases, set safety metrics (SPI), manage safety plans and artifacts, train engineers, and coordinate with partners and stakeholders to validate ML models and ADS integration for safe public-road operation.

Lead technical pre-sales for large Oil, Gas & Energy accounts, define and implement Databricks-based data and ML/AI strategies, build technical champions, mentor teammates, and drive enterprise adoption through technical leadership and value selling.

Provide data analytics solutions to a DoD client supporting supply chain excellence: build data pipelines, ETL, visualizations, and predictive models using Python/R/SQL and Palantir Foundry (Workshop/Slate); ensure data integrity and security; apply statistical and machine learning methods; collaborate across teams.

Lead strategy and roadmap for Brain Health & Detection, drive AI/ML-powered detection and anomaly systems, partner with engineering and data science, set success metrics, and lead end-to-end product execution and adoption for scalable service health intelligence.

Manages client relationships and technology and analytics project delivery. Advises data science and engineering teams on business analysis, technology approaches, project scope, schedules, costs, risks, and quality assurance. Develops client engagement strategies, technology roadmaps, proposals, statements of work, and pricing. Partners with executives to identify portfolio growth opportunities and communicates the business value of analytics, statistics, and machine learning.

The Senior Financial Analyst partners with Product Delivery leaders on financial planning, forecasting, reporting, variance analysis, investment evaluation, and resource allocation. The role develops financial models, manages acquisition spend analysis, supports headcount planning, enhances Anaplan, and delivers executive-ready insights. Responsibilities also include evaluating strategic initiatives, improving FP&A processes, and advising cross-functional leaders on risks, opportunities, tradeoffs, and financial performance.

Design and implement machine learning and optimization algorithms for Netflix’s ad marketplace. Build, train, and evaluate models using large-scale production data; develop online and offline evaluation frameworks; and partner with product teams to define objectives and trade-offs. Communicate technical decisions and experiment results to diverse stakeholders while providing technical guidance and aligning ML solutions with business goals.