Top Senior Machine Learning Jobs in Birmingham, AL

Drives operational excellence through data analysis, dashboards, process improvements, and cross-functional project leadership. The role embeds with business units to manage day-to-day operations, investigate trends, answer strategic questions, implement workflows, coordinate stakeholders, track deliverables, and present insights to technical and non-technical audiences. It serves as a strategic partner to operations leadership in a fast-paced, ambiguous environment.

Lead complex R&D and scientific AI engagements from pre-sales through production delivery. Partner with executives, researchers, engineers, and product teams to define use cases, architectures, backlogs, MVPs, simulations, integrations, data pipelines, and success metrics. Assess technical feasibility, coordinate multidisciplinary workstreams, and develop reusable architectures and delivery playbooks. Apply AI, machine learning, cloud computing, scientific workflows, and HPC to transform research processes while influencing product roadmaps and scaling solutions across industries.

Own the technical sales cycle for Databricks consulting engagements, including discovery, architecture design, solution scoping, estimation, proposals, demos, proofs of concept, workshops, and statements of work. Design lakehouse, data engineering, analytics, governance, security, and AI architectures. Partner with sales, Databricks, alliance, and delivery teams while ensuring effective handoffs into implementation.

Develop experimental AI techniques for agentic marketing applications, especially image and video generation. Build proofs of concept, autonomous evaluation and improvement systems, realistic AI-generated video, and brand-aligned creative generation workflows. The role requires strong quantitative and probabilistic thinking, backend architecture skills, creativity with LLM applications, and product intuition.

Lead and own cross-organizational technical programs for ML and robotics platform development, define roadmaps and milestones, remove blockers, align diverse engineering teams, and drive adoption of platform best practices to deliver scalable autonomy and infrastructure outcomes.

Define and execute the market strategy for UserTesting’s AI solutions. Own positioning, messaging, value propositions, go-to-market plans, sales enablement, customer and buyer research, competitive differentiation, thought leadership, and market feedback loops. Translate complex AI workflows and human-judgment applications into compelling solutions for technical enterprise buyers, while influencing product and company strategy through cross-functional leadership.

Leads a team of machine learning engineers and data scientists developing and deploying styling algorithms and personalized client experiences. Owns AI and ML strategy, product roadmap contributions, experimentation, production deployment, code quality, and data practices. Partners with product, design, merchandising, styling, and business teams to optimize personalization outcomes while coaching the team and fostering collaboration, innovation, and technical excellence.

Lead delivery of medium- and large-scale AWS programs, overseeing planning, assurance, reporting, risk mitigation, program health, and customer outcomes. Manage multi-year product and service roadmaps, identify business opportunities, resolve customer issues, and drive continuous process improvement. Lead, recruit, coach, and develop business analysts and engineers while collaborating with technical and business leaders across global enterprise clients.

Own lifecycle marketing performance across patient onboarding, activation, early care, and retention. Design and analyze multichannel experiments, build reporting and dashboards, write SQL, diagnose funnel and segment performance, and integrate AI and machine learning signals into lifecycle journeys. Collaborate with Product, Operations, Data Engineering, Marketing Operations, Analytics, and Creative to improve patient outcomes and measurable business performance.

Leads enterprise cybersecurity architecture for AI, cloud, hybrid, and on-premises environments. Designs secure AI architectures, cloud foundational controls, zero-trust identity models, API and data protections, and secure LLM, RAG, agent, and MCP integrations. Establishes governance, reusable security patterns, model-usage procedures, compliance controls, and audit readiness across AWS and Azure. Advises executives and technical teams, mentors security professionals, and drives secure cloud migrations while addressing AI/ML risks such as adversarial attacks, model poisoning, data drift, bias, and data leakage.

Lead and develop a team of partnership analysts; translate business questions into analytical frameworks, experiments, forecasts, and ROI models. Build dashboards and reporting infrastructure, analyze cohorts, funnels, retention, and partner performance, and own measurement frameworks for growth initiatives. Partner with Business Development, Engineering, Product, and Marketing to shape partnership strategy and communicate data-driven insights to technical and non-technical leaders.

Owns the strategy, roadmap, development, and adoption of financial data products and APIs. Partners with data science and engineering to integrate predictive and machine-learning models into scalable, trustworthy capabilities. Defines quality metrics, requirements, documentation, and support models while ensuring privacy, security, regulatory compliance, and downstream adoption across consumer and business banking products.

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.

Design, develop, and validate functionally safe software for robotic perception systems. Build safety-critical architecture across Linux and real-time operating systems, create testing and validation frameworks for edge cases, collaborate with machine learning, hardware, and core software teams, and mentor junior engineers on safety-critical coding practices.

Drive MSSP partner growth across AMER by recruiting, onboarding, activating, and expanding strategic managed security service provider relationships. Develop territory recruitment plans, executive partnerships, joint business plans, and indirect routes to market. Help partners launch managed security services, generate pipeline, increase revenue, and accelerate time-to-revenue. Collaborate with sales, channel, marketing, services, and technical teams while managing forecasts, opportunities, executive reviews, and revenue objectives.

Lead AI-driven localization product initiatives from concept through production. Build strategic roadmaps, incubate machine-learning capabilities, partner with researchers and engineers, and deliver scalable tools for creators and content operations. Translate user needs into impactful products, communicate strategy to executives and nontechnical stakeholders, and use data, experimentation, and ML evaluation to prioritize work and measure outcomes.

Architect and operate North’s AWS and multi-cloud infrastructure, establishing IaC, CI/CD, security, observability, and disaster-recovery standards. Build AIOps capabilities including anomaly detection, predictive scaling, self-healing remediation, AI-assisted tooling, and cloud cost optimization. Collaborate with DevOps, SRE, Product Engineering, and InfoSec teams to deliver resilient, compliant, highly scalable platforms.

Own the end-to-end product direction and quality of an AI email application. Define AI and system requirements, translate model capabilities into product decisions, balance quality, cost, latency, reliability, safety, and user experience, and partner with ML, backend, and client engineers. Establish evaluation frameworks, quality standards, feedback loops, and failure recovery strategies while driving prioritization, execution, experimentation, and user trust in a zero-to-one environment.

Own the end-to-end product direction and quality of an AI-powered email application. Define AI and system requirements, evaluation frameworks, quality standards, and measurable outcomes. Partner closely with ML, backend, and client engineering teams to make trade-offs across quality, latency, cost, reliability, safety, and user experience. Drive prioritization, specifications, experimentation, failure-mode mitigation, and continuous iteration in a technical zero-to-one environment.

Own the end-to-end lead management lifecycle, including capture, enrichment, qualification, scoring, routing, sales handoff, disposition, and recycling. Establish governance, business rules, SLAs, controls, and performance metrics; optimize workflows and integrations; resolve data quality and lead-flow issues; and partner with Sales, Marketing, Data Science, IT, and Analytics. Manage and develop a small team while overseeing operational priorities, testing, documentation, and continuous improvement.

Lead the technical side of enterprise sales for autonomous systems and physical AI customers. Own discovery, architecture reviews, demonstrations, proofs of concept, evaluations, deployments, and expansions. Translate customer workflows into solutions, address technical objections, and collaborate with Product and Engineering. Build scalable sales engineering processes, technical content, evaluation frameworks, and operating models while supporting strategic automotive OEM and enterprise opportunities.

Lead product strategy, requirements, and roadmap development for Honeywell Forge cloud applications serving utilities. Own AI and analytics capabilities for grid operations, DER management, rate design, and utility programs. Conduct customer and market discovery, competitive analysis, and voice-of-customer research. Partner with solution architects, sales, customers, and engineering to prioritize and deliver B2B SaaS products that generate measurable utility operational value.

Lead product strategy, requirements, roadmap, and AI capabilities for Honeywell Forge cloud applications serving utilities. Conduct customer and competitive discovery, translate utility workflows into product investments, define commercial opportunities, and partner with engineering to prioritize and deliver cloud-native B2B software. The role focuses on AI for grid operations, DER management, rate design, and utility program performance while ensuring measurable operational value.

Own the global consumer engagement and personalization domain across digital channels. Assess current capabilities, define multi-year strategy and roadmap, recommend personalization infrastructure investments, and translate priorities into shipped products. Partner with engineering, data science, design, marketing, legal, and regional teams to deliver scalable experiences. Establish metrics, run cross-market experiments, optimize performance, and make global versus localized decisions. Influence executives, mentor product managers, and apply AI, identity resolution, and generative AI to personalization.

Works with business stakeholders to identify data-driven opportunities, analyze trends, and support business decisions. Designs data models, machine-learning algorithms, and predictive analytics models; preprocesses structured and unstructured data; identifies data sources and automates collection; analyzes large datasets for patterns; and presents insights through data visualization.