Top Machine Learning Jobs in Pensacola, FL

Identifies business opportunities for AI and automation, gathers and documents requirements, develops business cases, supports solution design and delivery, facilitates stakeholder collaboration, assists with testing and change management, and tracks project outcomes, reporting, and benefits realization.

Owns AI product backlogs, priorities, requirements, user stories, and acceptance criteria. Translates business needs into technical solutions, coordinates stakeholders and delivery teams, supports Agile ceremonies and product releases, monitors adoption and performance, and drives continuous improvement. The role also identifies AI-driven business opportunities, supports responsible AI practices, and contributes to AI product strategy.

Tests and validates AI-powered products, machine learning models, and Generative AI applications. Designs test strategies, executes functional, regression, and user acceptance testing, validates model outputs, identifies defects, analyzes quality metrics, documents evidence, and supports compliance and release readiness. Collaborates with AI engineers, product managers, and business teams to improve testing methodologies, quality frameworks, and AI evaluation practices.

Lead the design and implementation of critical Safety AI initiatives, focusing on real-time perception systems for edge devices and optimizing models for production deployment in safety-critical environments.

Leads analytical and scientific strategy for Fetch’s Play and Rewarded Apps businesses. Develops predictive and machine learning models, experimentation frameworks, causal analyses, feature pipelines, and measurement systems using Python and SQL. Partners with Product, Marketing, Engineering, and business leaders to improve monetization, engagement, retention, personalization, and marketplace outcomes. Translates complex findings into strategic recommendations, mentors data scientists and analysts, and establishes analytical best practices.

Own the full machine learning lifecycle for search ranking and retrieval, including model training, deployment, serving, feature pipelines, evaluation frameworks, hybrid lexical-vector retrieval, large-scale embedding inference, query understanding, and permissions-aware multi-tenant search. Collaborate with infrastructure, AI, and backend teams to improve search relevance across ClickUp’s platform.

Leads the enterprise analytics organization, managing Analytics Engineers, Data Analysts, and BI Developers. Defines analytics strategy; delivers executive dashboards, KPI scorecards, operational reporting, semantic models, and self-service BI. Establishes data governance and metric standards, ensures data quality, partners with Engineering, Product, Finance, Operations, and executives, and translates business questions into actionable analytical solutions. Mentors analytics talent and drives prioritization, adoption, and measurable business outcomes.

Senior backend engineer responsible for setting technical direction, designing scalable distributed systems, building customer-facing web applications, mentoring engineers, and applying machine learning to large-scale healthcare data. The role requires Java, Scala, or Rust expertise, cloud systems experience, technical design leadership, cross-functional collaboration, and adoption of agentic engineering practices.

Leads Alkami’s enterprise and technology risk management program, including risk assessments, control evaluations, mitigation, monitoring, reporting, and escalation. Owns AI, machine learning, and generative AI governance, translating regulatory and risk expectations into practical policies, controls, and review processes. Partners with cross-functional risk owners and supports compliance initiatives involving business continuity, disaster recovery, regulatory management, and incidents. Reports to the Chief Compliance Officer in a fully remote role.

Build and operate production retrieval-augmented generation systems, including embeddings, vector retrieval, grounded language-model generation, citation validation, prompt and schema design, model packaging, serving, rollback, and telemetry. Develop bounded model assistance for narrative extraction with provenance and rule-context tracking. Maintain resilient serving paths in secure government cloud environments, including restricted or air-gapped deployments.

Build and productionize machine learning systems that convert complex documents into executable, verifiable logic. Responsibilities include developing parsing and transformation pipelines, experimenting with language models, designing datasets and evaluation metrics, creating adversarial tests, analyzing model failures, and implementing rule-based checks using logical reasoning and program analysis. The role requires close collaboration with founders and subject-matter experts to validate system outputs.

Leads large-scale AI transformation programs for Cresta’s strategic enterprise customers. Serves as executive sponsor and trusted advisor to C-suite stakeholders, manages matrixed implementation and engineering teams, oversees complex deployments, standardizes delivery governance, and drives measurable customer value, adoption, expansion, and advocacy. Partners with Sales, Customer Success, Product, and Engineering while connecting technical outcomes to business ROI.

Provides strategic and hands-on biostatistical leadership across pharmaceutical discovery, translational, biomarker, and early research programs. Designs studies, analyzes complex biological datasets, develops advanced statistical methods and reproducible R workflows, and translates evidence into portfolio decisions. The role collaborates with quantitative and scientific functions, supports publications and external partnerships, mentors statisticians, and advances data-driven analytical practices.

Own the full enterprise sales cycle for Oversee’s leisure travel business, from prospecting through closing. Build relationships with OTAs, agencies, tour operators, and vacation providers; develop pipeline; identify target segments and use cases; refine positioning and sales strategy; lead complex stakeholder processes; and partner with Product and Engineering to convert market insights into a repeatable leisure sales motion.

Own AgentSee’s sales and go-to-market strategy as the first dedicated sales leader. Personally close early enterprise opportunities, refine the ideal customer profile, positioning, pricing, packaging, and sales process, and translate AI capabilities into measurable business value. Build repeatable sales operations and playbooks, provide market feedback to Product and Engineering, and eventually hire and lead the AgentSee sales organization.

Leads the design, development, deployment, and operation of scalable machine learning solutions and high-performance microservices. Builds real-time inference systems using Triton and TensorRT, guides distributed cloud architectures, participates hands-on in coding and reviews, and mentors engineers. Drives technical strategy, operational excellence, system resilience, and business-wide innovation across generative AI, graph machine learning, and big data initiatives.

Lead a global product team building automation-first, AI-assisted investigation and agent-facing tooling for AML, KYC, EDD, and sanctions screening. Define roadmap, metrics, vendor strategy, and regulatory controls to improve investigation quality, cycle time, and operational efficiency while ensuring security, privacy, and auditability.

Lead exploration and application of large language models and generative AI for enterprise contact-center automation. Translate research into high-performing, deployable ML systems; contribute across experimentation, implementation, and deployment; collaborate with engineering and product teams; optimize performance; and help shape team strategy, innovation, and professional growth.

Lead exploration and application of large language models and generative AI, translate research into deployable ML systems, and guide strategic direction. Develop solutions across experimentation, implementation, and deployment while collaborating with engineering and product teams to optimize performance and customer outcomes in contact center automation.

Lead production-grade ML systems: build data pipelines, training and evaluation workflows, and scalable GPU inference. Fine-tune and adapt large models (LoRA, QLoRA, SFT, DPO, distillation), optimize deployments for latency/cost/reliability, and collaborate with product and engineering to ship robust, observable model-driven features.

Leads the end-to-end execution layer for machine learning, including data pipelines, large-model training and fine-tuning, evaluation, inference, deployment, monitoring, and reliability. Designs scalable GPU-based systems, optimizes performance and cost, partners with research and application engineering, and translates model capabilities into measurable production improvements. Also provides technical leadership, judgment, and support for high-impact ML delivery.

Lead the end-to-end ML execution: build and operate data pipelines, training and evaluation systems, and scalable inference. Fine-tune and optimize large models (LoRA/QLoRA/SFT/DPO/distillation), ensure GPU efficiency, monitor production quality, collaborate with product and application engineering, and rapidly ship reliable, safe ML solutions that translate research into production.

Design, build, and operate large-scale ML systems across data, training, evaluation, inference, and deployment. Lead GPU-optimized training and inference pipelines, ensure reliability, performance, safety, and seamless integration into products. Own production deployments, monitoring, and cross-functional best practices to drive measurable model improvements and efficient research-to-production cycles.

Partner with healthcare clients to discover, design, and deliver production-grade AI/LLM solutions. Architect cloud-native systems, build and deploy ML/LLM applications, prototype rapidly, troubleshoot distributed systems, and lead client engagements to drive measurable business outcomes while collaborating across product, platform, and engineering teams.

Analyze business processes, data quality, and datasets to identify AI and automation opportunities. Translate business requirements into AI/ML use cases, collaborate on model development, evaluate model performance, and deliver dashboards and reports. The role also supports data validation, AI solution monitoring, emerging technology research, governance, compliance, responsible AI, prompt engineering, and generative AI initiatives while partnering with business, data science, engineering, and product teams.