Top Remote Machine Learning Jobs in Huntsville, AL

Source, develop, and close enterprise health system clients across a US territory. Build relationships with healthcare executives, launch and expand pilot programs, create account plans, meet revenue targets, and transition customers to Client Success. Collaborate with product, legal, marketing, support, and leadership; provide sales forecasts and pipeline reporting in HubSpot; influence the product roadmap; and represent Nabla at industry events.

Develop and launch scalable, user-facing features for AI-powered video and multimedia tools. Architect end-to-end solutions across backend and frontend systems, ensure quality and reliability, collaborate with design, product, and ML teams, and mentor engineers. The role involves rapid prototyping, experimentation, technical leadership, and shaping Pika’s core creative product.

Lead teams building computer vision and machine learning for AI dash cameras, managing model lifecycle, coordinating with cross-functional teams, and driving AI product development.

Lead Tilt’s product design function across consumer financial products. Set design vision and quality standards, manage and grow a team of six designers and one researcher, build research capabilities, influence product strategy, scale the design system, advocate for users, and partner with Product, Engineering, Marketing, and executive leadership. Drive AI-enabled design practices and ensure product experiences deliver measurable user and business impact.

Manage strategic enterprise accounts, building executive relationships, developing multi-year account plans, driving retention, expansion, upsell and cross-sell opportunities, and closing complex technology deals. Coordinate cross-functional Basis teams, lead executive business reviews, align customer priorities with company objectives, and represent Basis at customer and industry events.

The Principal Data Scientist will design, develop, and deploy machine learning models, innovating methodologies and collaborating across teams to enhance financial decision-making and risk management.

Serve as a customer-facing fraud and identity expert across pre-sales, onboarding, and expansion. Evaluate customer risk profiles, design fraud strategies and identity workflows, advise on risk mitigation and verification optimization, and coordinate technical, regulatory, and operational launch readiness. Lead implementation milestones, go-live planning, and cross-functional resolution of deployment bottlenecks while helping enterprise customers maximize adoption and long-term value.

Own the strategy, roadmap, and growth of Ottimate’s new product initiatives across procurement, budgeting, spend planning, and contract management. Lead customer discovery, validation, MVP development, launches, and portfolio metrics. Manage product managers and designers, oversee Design and UX, and partner with engineering, data science, sales, marketing, and customer success to deliver coherent, market-ready products.

Owns and grows strategic Financial Services accounts by prospecting, developing channel and alliance opportunities, presenting cybersecurity solutions, closing new business, and expanding customer relationships. The role requires enterprise technology and security sales expertise, executive engagement, account planning, forecasting, reseller collaboration, and the ability to explain NDR, Agentic SOC, AI security, and network visibility solutions. Travel is primarily remote and on the road, with at least 50% travel required.

Leads technical support for VA healthcare AI citizen developers, developing and validating supervised, unsupervised, and reinforcement learning models. Builds synthetic healthcare data cohorts, designs secure cloud-based data science workflows, collaborates with federal stakeholders, interprets analytical results, mentors developers, and ensures model quality, documentation, privacy, governance, and regulatory compliance.

Build and scale production AI systems for Prophetic’s real estate intelligence platform. Responsibilities include developing agentic systems, data pipelines, inference workflows, evaluation benchmarks, geospatial processing, and machine learning applications across hundreds of millions of records. The role requires shipping reliable Python services, optimizing throughput and cost, integrating research into products, and collaborating with engineering and product teams.

Supports enterprise sales by serving as the technical and product expert for Wavelytics, an AI-powered analytics suite. Responsibilities include delivering technical demonstrations, positioning analytics and AI capabilities, translating complex solutions into business value, addressing security and governance questions, supporting customer adoption, creating sales playbooks, and collaborating with Product and Marketing to improve roadmap alignment, retention, and revenue growth.

Designs, deploys, and optimizes GenAI data solutions for enterprise clients. Responsibilities include translating client needs into scalable workflows, creating annotation guidelines, delivering high-quality datasets, building validation and automation pipelines, and ensuring data accuracy, reliability, observability, and readiness for model development.

Own end-to-end analytical lifecycle to develop and optimize business, risk, and growth strategies. Monitor portfolio and macro trends, build and interpret ML-driven models, lead cross-functional launches, mentor junior analysts, and build AI-assisted workflows to improve decisioning and measurement across products and geographies.

Oversee global analytics and algorithm design initiatives for a nonprofit: identify high-impact projects, coordinate with student directors, collect feedback, design and run training for developers and technical advisors, and form partnerships to support implementation. Volunteer commitment ~5-6 hours/week.

Supports federal, state, local, tribal, and territorial emergency management planning through geospatial and data analytics. Develops emergency operations, hazard mitigation, and continuity plans; applies FEMA planning processes; facilitates stakeholder coordination; supports exercises, disaster activations, and deployments. Performs quantitative and qualitative analysis, develops database and modeling solutions, applies AI and machine learning techniques, and ensures compliance with government regulations. This is an on-call, non-full-time role requiring frequent travel and extended deployments.

Lead design, build, and deploy scalable, reliable Java-based microservices in a cloud-native, event-driven AWS environment. Own backend feature design, code quality, automated testing, and FedRAMP release processes. Mentor engineers, participate in on-call/TechOps, integrate AI tools into development, and drive architecture, observability, and cross-team initiatives.

As a Customer Success Engineer, you'll drive adoption and satisfaction for enterprise customers using Deepgram's voice AI technology, fostering strong relationships and aligning technical solutions with business objectives.

Own product areas for an autonomous banking platform, translating backend capabilities (APIs, async workflows, ML) into user-facing features. Conduct customer discovery, define technical requirements and acceptance criteria, prioritize backlog, partner with engineering on scoping and de-risking, and validate outcomes using feedback loops and analytics.

Lead the Product and Design organization, define product vision and roadmaps, prioritize strategic investments, and align cross-functional teams to deliver safe, scalable robotic delivery experiences. Build and develop high-performing product and design teams, champion user-centered design, guide data-driven decisions, and translate complex robotics, hardware, software, and operational capabilities into customer value. Partner with executive leadership across Engineering, Operations, Safety, Policy, and Business Development to drive commercialization and growth.

Lead performance marketing data science initiatives focused on lead segmentation, bidding optimization, targeting, and marketing spend allocation. Develop and deploy production-ready machine learning systems, establish research and ML operations roadmaps, build monitoring frameworks, and identify high-impact opportunities. Partner with engineering, marketing, vendors, and executives while mentoring data scientists and machine learning engineers and promoting rigorous, maintainable, and reproducible practices.

Build and deploy AI agents, intelligent applications, scalable backend services, APIs, and full-stack solutions. Integrate large language models, AI services, data platforms, and external APIs into customer-focused products. Collaborate with engineering, product, and cross-functional teams while contributing to cloud-native applications, data pipelines, distributed systems, testing, deployment, and production operations.

Leads AI presales strategy, customer advisory, technical solution architecture, and team development. Translates business needs into AI use cases, prototypes, business cases, roadmaps, and repeatable offerings. Partners with sales, engineering, consultants, and technology vendors to support complex pursuits, executive briefings, demonstrations, proofs of concept, and AI business growth. Promotes responsible AI governance, tracks pipeline performance, develops thought leadership, and remains hands-on in high-impact customer engagements.

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.