Top Remote Machine Learning Jobs in Huntsville, AL

Lead and manage the underwriting and credit risk model validation team, perform hands-on independent validations of complex ML models, assess data integrity and performance, identify and remediate model weaknesses, partner on MRM framework improvements, and coordinate with audit, compliance, and controls to meet regulatory standards.

Drive new enterprise revenue and customer expansion by selling AI-powered SaaS solutions. Manage complex 6–12 month sales cycles, build executive relationships, develop value propositions, maintain pipeline accuracy, lead virtual account teams, and close high-value deals. Translate technical AI capabilities into measurable business outcomes, guide customer adoption, collaborate with marketing, product, and customer success teams, and exceed quarterly pipeline and revenue targets.

Lead product vision, strategy, discovery, and Agile delivery for insurance platforms. Partner with engineering, data science, design, QA, and insurance domain teams to prioritize opportunities, manage backlogs, evaluate build-versus-buy decisions, guide technical design, coordinate delivery, and measure outcomes. The role requires strong knowledge of insurance policy lifecycles, integrations, data modeling, pricing, underwriting, and AI-enabled workflows.

Leads product strategy, roadmap prioritization, AI and automation initiatives, client engagement, and cross-functional delivery for SPINS Intelligence and Portal. The role combines hands-on product management with PM team leadership, including writing user stories, defining acceptance criteria, triaging issues, reviewing sprint output, managing product economics, and developing business cases. Requires strong CPG, SaaS analytics, data product, AI/ML, and technical platform expertise.

Own Sophos EDR strategy, roadmap, technical product development, AI-powered experiences, and go-to-market execution. Translate customer needs, threat research, telemetry, competitive intelligence, and business goals into product priorities. Partner with engineering, security operations, threat research, marketing, sales, and customer success to deliver endpoint detection, investigation, threat hunting, response, reporting, and XDR/MDR upgrade capabilities while driving adoption, revenue, and customer outcomes.

Serve as the senior AI advisor to the VHA National Artificial Intelligence Institute. Develop AI risk-management playbooks, governance guidance, risk stratification methods, enterprise AI strategies, training, and oversight materials. Advise AI use-case owners and government stakeholders, translate federal AI policy into actionable guidance, support health information and ModelOps initiatives, and research emerging AI governance practices. The role is remote, advisory, full-time, and contingent.

Architect and prototype scalable solutions for Twilio’s emerging technologies experimentation team. Lead AI, machine learning, and LLM-driven initiatives; guide technical direction; mentor engineers across the US and India; align prototypes with business goals; and ensure enterprise architecture, scalability, extensibility, and production readiness. The role requires extensive architecture or principal engineering experience, cloud expertise across AWS and Azure, full-stack development knowledge, database expertise, and strong technical leadership.

Run Discovered Labs’ founder-led content operation across LinkedIn and YouTube, from topic selection and editorial development through publication and performance analysis. Responsibilities include shaping founder content, coordinating external partners, repurposing long-form material, improving AI-supported content systems, and using engagement and pipeline data to guide editorial decisions. The role requires strong editorial judgment, hands-on execution, social content expertise, and the ability to work autonomously in an AI-forward, remote environment.

Annotate and label data accurately according to project guidelines, conduct quality checks, provide feedback on annotation tools and processes, collaborate with project teams, maintain efficiency, and identify issues for resolution. The role requires strong attention to detail, critical thinking, adaptability, communication skills, and comfort with repetitive computer-based work.

Review, label, and QA datasets for AI/ML training; maintain trackers, databases, and dashboards; follow annotation guidelines and SOPs; prepare and clean data; document processes; collaborate to resolve data queries while ensuring confidentiality and timely delivery.

Own the AI product strategy and roadmap for HealthEdge’s HealthRules Payer platform. Identify and prioritize AI use cases across claims, member management, enrollment, benefits, and contracts; translate strategy into product requirements; and align product, engineering, data science, UX, marketing, and sales teams. Lead AI governance, regulatory compliance, customer presentations, go-to-market messaging, pilots, and feature commercialization while ensuring responsible AI use in healthcare payer workflows.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

Own the design vision and execution for experimental AI-powered learning tools co-created with teachers and researchers. Design teacher- and student-facing products, create flows, wireframes, interaction designs, and functional prototypes, and facilitate collaboration across educators, researchers, engineers, and data scientists. Conduct usability testing and validate research hypotheses while navigating ambiguity, data-heavy workflows, and uncertain AI/ML-enabled features.

Leads enterprise AI enablement and intelligent automation, overseeing use-case intake, prioritization, delivery, governance, adoption, and value realization. Establishes the AI operating model, builds literacy and training programs, manages automation and AIM teams, sets responsible-AI guardrails, tracks adoption metrics, and aligns investment with enterprise transformation priorities. Requires executive stakeholder leadership, portfolio governance, team development, and experience scaling AI or automation capabilities in complex organizations.

Architect and scale cloud-based ML infrastructure supporting large-scale training, massive datasets, model development, and customer services. Own platform design, implementation, deployment, monitoring, reliability, cost optimization, and usability. Collaborate with data science, product, and operations teams, solve complex distributed-systems challenges, and mentor engineers while driving technical innovation.

Leads enterprise manufacturing AI, machine learning, and Industry 4.0 initiatives across multiple plants. Develops and deploys solutions for predictive maintenance, process and quality optimization, energy efficiency, throughput, and OEE improvement. Establishes scalable data and AI platforms, governance, and MLOps standards while integrating industrial data sources. Leads and mentors data science teams, prioritizes projects, allocates resources, partners with cross-functional manufacturing stakeholders, and presents strategic recommendations and business cases to executives.

Manage paid social campaigns and full-funnel strategies across Meta, LinkedIn, TikTok, Pinterest, Snapchat, and other paid channels. Oversee client accounts, analyze performance, identify growth opportunities, build client relationships, and collaborate with SEO, creative, and analytics teams. Contribute to thought leadership, coach associates, maintain quality standards, and explore automation, AI, and LLM search visibility initiatives.

Own the strategy, execution, customer success, and go-to-market for Oscilar’s Device and Behavior product. Lead development of SDKs, signal pipelines, behavioral biometrics, agentic identity signals, data networks, foundation-model capabilities, and analyst experiences. Partner with sales and customers to drive adoption, shape product positioning, and deliver a device intelligence and digital identity platform for fraud, onboarding, authentication, and risk decisions.

Supervise actuarial predictive modeling initiatives for insurance pricing, portfolio management, and planning. Collaborate with actuaries, data scientists, underwriters, and management to develop, validate, govern, and implement models. Establish assumptions, loss adjustments, usage guardrails, monitoring practices, and model documentation. Support regulatory filings, improve team workflows, contribute to strategic projects, and mentor staff in actuarial and technical skills.

Develop and maintain pricing and predictive models for credit insurance products. Analyze claims, loss ratios, portfolio performance, profitability, credit risk, and economic scenarios. Recommend pricing actions, support forecasting and reserves, evaluate underwriting strategies, and partner with underwriting, finance, claims, data science, and technology teams. Ensure model governance, documentation, and actuarial standards compliance.

Staff-level ML and agent systems engineer responsible for production model lifecycles, multi-step agentic systems, evaluations, retrieval and context engineering, and model cost and latency optimization. The role owns technical direction for Code Understanding products, improves model and agent quality, establishes evaluation and monitoring standards, mentors engineers, collaborates with customers, and delivers reliable AI-powered developer experiences at enterprise scale.

Designs, builds, and operationalizes enterprise AI, generative AI, machine learning, and agentic AI solutions. Develops integrations, orchestration services, reusable tooling, deployment pipelines, monitoring, and governance controls. Partners with architecture, platform, data, and product teams to deliver scalable, secure, compliant AI capabilities across the organization.

Designs and governs enterprise AI architecture, including generative AI, agentic workflows, RAG, orchestration, integrations, governance, security, compliance, and AI operations. Establishes scalable reference architectures, platform strategies, responsible AI guardrails, lifecycle management, monitoring, and cost controls. Partners with enterprise architecture, engineering, data, and product teams to drive secure AI adoption across cloud services, APIs, and enterprise platforms.

Owns the strategy, roadmap, requirements, backlog, positioning, and lifecycle execution for proprietary software products. Partners with engineering, UX, QA, architecture, data operations, and project management teams in an Agile environment. Conducts market and competitive analysis, supports product launches, creates AI prototypes, develops client-facing content, and contributes to RFP responses. Requires strong technical, product leadership, communication, prioritization, and stakeholder-management skills.

Lead and transform the Fraud Intelligence Team by designing scalable operating models, QA and measurement frameworks, and talent programs. Drive labeling quality, operational metrics, and fraud prevention impact; convert frontline fraud observations into insights for Product, R&D, Analytics, and GTM; leverage AI, automation, and tooling; and serve as a cross-functional fraud subject-matter expert while developing early-career talent and measurable business outcomes.