Top Remote Machine Learning Jobs in Huntsville, AL

Own and scale the customer entry-to-purchase lifecycle: design acquisition journeys, build and optimize welcome/abandon/lead conversion automations, implement personalization and gamification, partner cross-functionally, steward list health and deliverability, and run A/B and multivariate testing to improve conversion and engagement metrics.

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

Lead sales of Akamai's enterprise security portfolio across assigned Midwest accounts, develop and manage pipeline, position Zero Trust architectures, collaborate with internal security engineers and researchers, engage C-level stakeholders, and close new and expansion business.

Advise clients on antitrust eDiscovery compliance using machine learning and generative AI. Build and validate TAR/AI models (Epiq AI, Relativity, Reveal, Brainspace), craft LLM prompts, draft defensible protocols, train/transfer workflows to review teams, and support client-facing, pre-sales, and thought-leadership activities. Must manage multiple projects and meet deadline-driven schedules.

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.

Proactively source and engage passive technical talent across the Adobe Experience Cloud and AI ecosystems. Build pipelines, conduct candidate qualification, maintain ATS records, deliver recruiter-ready shortlists, and provide market intelligence to support consulting hires and hiring strategy.

Own end-to-end lifecycle for AI features: define requirements, run model evaluation and experimentation, maintain capability-level AI roadmap, apply responsible AI principles, measure success metrics, iterate with engineering, UX, security, and go-to-market to deliver customer-ready AI capabilities.

Lead strategy and execution to prevent, detect, and disrupt digital fraud, scams, and money mule activity. Build and mentor data science and analytics teams, develop ML predictive models, partner with product, cyber, vendors, and risk teams, and drive prevention-first controls, compliance, and operational efficiencies across payments, onboarding, and authentication channels.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; partner with product, engineering, and marketing; mentor data scientists; build experimentation frameworks; extract and analyze data using SQL and Python; communicate results to stakeholders.

Design, productionize, and deploy machine learning forecasting models for ad inventory and campaign performance in low-latency, real-time advertising environments. Build scalable simulation, budgeting, and pacing systems; collaborate with cross-functional teams to deploy models at scale and optimize ad monetization.

Lead research and experimentation to measure ad campaign performance and effectiveness. Design evaluation frameworks, build measurement solutions, develop models for lift, conversion, and attribution, run experiments and analyses, partner with product/engineering/design/sales, present insights, mentor teammates, and foster technical community and culture.

Lead integration of GenAI into game engines and production pipelines. Prototype AI-native gameplay with designers, optimize inference latency and memory to preserve framerate, build automation tools for asset and design workflows, mentor engineers, and translate research and open-source models into production-ready systems aligned with player experience.

Build and operate scalable, highly available ML/LLM serving and compute infrastructure. Own long-term platform vision, deliver zero-to-one projects, partner cross-functionally, optimize performance and capacity, and support production ML models at scale.

Lead design and execution of scalable experimentation frameworks for Netflix Ads. Develop auction-based, biddable media models, solve cold-start measurement problems globally, apply causal inference and ML, partner with product and engineering, and translate analytic results into strategic recommendations to optimize marketplace dynamics and member experience.

Lead design of Netflix's ads consumer experiences, building GenAI-powered pipelines, prototyping AI-driven ad formats, ensuring privacy/regulatory compliance, collaborating with product, engineering, and data science to scale personalized advertising across surfaces.

Lead large, cross-functional programs for Netflix Cloud Infrastructure and AI Platform. Shape multi-year strategy, drive migrations and platform adoption, structure ambiguous technical problems into executable plans, execute end-to-end delivery, manage dependencies and risks, influence investment and architecture, and enable ML practitioners to build, train, deploy, and operate models at scale.

Lead integration of state-of-the-art AI/ML into Netflixs content platform and publishing tools. Build roadmaps, incubate ML systems from idea to production, partner with researchers and engineers, automate promotional asset creation, optimize distribution and localization, and measure impact with data-driven evaluation.

Lead design, execution, and optimization of scaled lifecycle campaigns across email and in-product channels. Use marketing automation, behavioral data, and ML techniques to personalize journeys, drive activation, revenue, retention, and cross-sell. Analyze performance, generate insights, and collaborate with Growth, Product, Engineering, Data Science, Analytics, Sales, and Marketing stakeholders.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

Lead the machine learning strategy, design scalable ML systems, drive MLOps, mentor engineers, and comply with AI governance.

As a Principal Software Engineer, you'll architect and code backend services and ML models, integrate them with Salesforce, and mentor junior engineers.

Lead and build a team that drafts and submits state-specific rating rules, underwriting guidelines, and forms; manage filing execution via SERFF, handle objection responses, coordinate cross-functional stakeholders, establish operating rhythms, improve documentation and controls, and coach staff to scale filing expertise and regulatory execution.

Contribute to building and scaling ML development infrastructure for Copilot: design and optimize CI/CD for large-scale training, build developer tools, maintain multi-cloud infrastructure, and deploy/host models for inference and data generation while collaborating with cross-functional teams to improve reliability, performance, and model development workflows.

Lead and build Adelphi's end-to-end talent acquisition function, focusing initially on sourcing and closing Forward Deployed AI Engineers for defense and intelligence customers. Own full-cycle recruiting, structured scorecard-based interviewing, candidate experience, offer negotiation, recruiting infrastructure, and vendor/tool management while partnering with executive leadership.