Top Senior Machine Learning Jobs in Atlanta, GA

Lead strategic sales of AI Workforce solutions, including Microsoft Copilot, AI agents, Viva, Windows 365, and Azure Virtual Desktop. Develop customer strategies, identify opportunities, build pipeline, conduct consultative business and technical conversations, and orchestrate internal teams and partners to close complex deals. The role requires expertise in technology sales, AI, forecasting, demand generation, stakeholder engagement, competitive positioning, and cross-selling across enterprise and regulated customers.

Lead analyst relations programs to improve Databricks’ positioning in industry evaluations, including Gartner, Forrester, and IDC. Develop differentiated narratives, submissions, demonstrations, customer evidence, and executive briefings. Scale analyst relations through AI-assisted workflows, convert analyst feedback into product and go-to-market recommendations, and influence product, marketing, sales, and executive teams. Manage multiple deadline-driven workstreams while improving analyst advocacy, market visibility, and evaluation outcomes.

Develop and support scalable Python services, APIs, and event-driven systems for digital freight pricing and booking. Build experimentation frameworks, AI-assisted pricing harnesses, validation guardrails, real-time platforms, and machine-learning integrations. Lead architecture decisions, ensure security and reliability, mentor engineers, and collaborate across product, operations, revenue management, data science, and engineering teams.

Own the commercial success of a portfolio of strategic enterprise accounts, including renewals, retention, expansion revenue, forecasting, and multi-year account strategies. Build executive relationships, lead business reviews and complex negotiations, coordinate cross-functional resolution efforts, and represent client needs internally. Mentor other Account Managers and use generative AI tools for account planning and communications while protecting confidential information.

Coordinate and govern release cycles for the HIGLAS Product Suite within an IT service management environment. Responsibilities include managing release phases and tasks, maintaining release calendars, conducting quality checks, tracking risks and dependencies, presenting status to review boards, coordinating outage notifications, completing post-release reviews, facilitating cross-functional meetings, and maintaining procedures, reports, dashboards, and KPIs. The role also supports process improvement and AI-enabled governance workflows.

Owns strategy, roadmaps, backlogs, and end-to-end delivery for business automation and AI products. Partners with engineering and executives to define requirements, launch reliable features, evaluate build-versus-buy decisions, manage budgets and cloud/API costs, measure ROI, and maintain product performance. Requires strong technical, agile delivery, analytics, and healthcare privacy expertise, with potential progression into product management leadership.

Leads end-to-end delivery of AI and software deployments for customers, coordinating engineering, product, UX, business, vendors, and customer stakeholders. Develops project plans, budgets, schedules, resource plans, risks, and status reporting; manages integrations, testing, deployment readiness, dependencies, and post-deployment follow-up. Serves as the bridge between technical teams and customer delivery while improving project management practices and supporting AI program growth.

Leads end-to-end delivery of AI and software solutions for customer deployments. Coordinates engineering, product, UX, business, customer, and vendor stakeholders; manages plans, milestones, budgets, risks, dependencies, testing, integration, deployment readiness, and handover. Translates technical requirements into executable plans, resolves blockers, communicates project status, and improves delivery practices while developing expertise in AI platforms and technical program leadership.

Provide production support for SAP Finance, troubleshoot and configure SAP FI, perform root cause analysis, and deliver functional enhancements across the SAP lifecycle. Collaborate with business and technical teams on requirements, solution design, testing, deployment, documentation, and knowledge transfer. The role requires SAP S/4HANA experience, manufacturing finance expertise, and at least two full-cycle implementations.

Define and scale autonomous agricultural machinery products through customer research, experimentation, roadmap prioritization, system requirements, and cross-functional delivery. Partner with engineering and commercial teams on hardware, embedded software, computer vision, machine learning, navigation, controls, and mobile solutions. Use data, field feedback, and performance metrics to improve adoption, customer confidence, mission quality, and product impact.

Own enterprise sales across new and existing accounts, leading consultative, ROI-driven sales cycles from discovery through close. Develop account plans, build executive relationships, collaborate with Solutions and Forward Deployed Engineers, forecast pipeline, maintain CRM discipline, lead business reviews, represent the company at industry events, and relay customer insights to shape the product roadmap.

Develop graduate-level Applied AI course materials, including lessons, labs, case studies, assignments, rubrics, projects, readings, templates, and videos. Design practical activities using LLMs, RAG, AI agents, automation, APIs, and evaluation tools. Collaborate with learning designers and university faculty, explain complex concepts to nontechnical learners, maintain academic quality, and update content as AI tools and regulations evolve.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Leads Fenergo product consulting engagements focused on AML, sanctions, data analytics, and regulatory risk solutions. Manages client accounts, oversees project planning and execution, guides analytics initiatives, and develops data-driven business strategies. Uses predictive analytics, machine learning, statistical modeling, and scenario analysis to generate insights. Manages and mentors teams, engages stakeholders, resolves conflicts, promotes innovation, and ensures high-quality deliverables and adherence to professional standards.

Drives business transformation and strategic change initiatives by developing transformation strategies, improving processes and organizational design, managing change adoption, engaging senior stakeholders, coordinating cross-functional projects, delivering training, and tracking benefits realization. The role manages transformation projects from planning through post-implementation review while ensuring alignment with business objectives, timely delivery, budget adherence, and successful organizational adoption.

Lead end-to-end yield optimization for Azure infrastructure by analyzing revenue, hardware, and capacity data. Develop statistical and machine-learning models for pricing, resource allocation, and demand elasticity; design experiments and causal inference analyses; build metrics and dashboards; and communicate actionable recommendations to technical, business, finance, and executive stakeholders. Mentor peers and guide cross-functional decisions involving cloud economics, pricing, customer behavior, utilization, and cost efficiency.

Develop fraud detection and identity-risk machine learning models from problem definition through production deployment. Responsibilities include investigating fraud patterns, exploratory analysis, feature engineering, model training and evaluation, production-quality Python and PySpark development, monitoring model and feature drift, and presenting analytical recommendations. The role requires collaboration with data scientists and engineers while addressing privacy, explainability, validation, governance, and real-world fraud-modeling challenges such as class imbalance and delayed labels.

Leads AML transaction monitoring optimization, including rule testing, threshold tuning, scenario design, and alert reduction. Drives FIU automation and workflow improvements, evaluates AI-enabled compliance tools, and partners with Product, Engineering, Data, Operations, and vendors on monitoring infrastructure and integrations. Supports scalable compliance controls across new products, jurisdictions, and transaction volumes while maintaining regulatory alignment and detection effectiveness.

Manage enterprise metadata, business glossaries, data catalogs, and end-to-end data lineage for a federal case management modernization program. Configure governance and metadata platforms, integrate data sources and pipelines, establish standards and workflows, monitor metadata quality, support compliant data sharing, develop governance metrics, document procedures, and train data stewards and business users.

Develops data quality checks, metadata standards, and AI use-case inventories for HHS. Cleans and integrates complex datasets, applies machine learning and NLP for automated tagging and classification, evaluates AI governance risks, and produces reporting datasets, dashboards, and recommendations. The role requires translating ambiguous federal policy and reporting requirements into repeatable analytical workflows while collaborating with product, design, engineering, and government stakeholders.

Develop and deploy machine learning and LLM-based solutions for cybersecurity challenges. Responsibilities include analyzing complex datasets, building classification and NLP models, optimizing prompting, embeddings, RAG, and fine-tuning techniques, designing evaluation methodologies, and integrating models into scalable production systems. The role requires collaboration with cybersecurity researchers, software engineers, data engineers, and product managers.

Own the authorization optimization product roadmap, improving legitimate transaction approval rates and recovered merchant revenue. Define technical requirements, APIs, data models, integrations, and issuer-specific decisioning strategies. Partner with engineering, data science, sales, solutions, and customer success to deliver fraud-aware authorization improvements involving retries, 3DS, decline recovery, and payment routing. Manage partnerships with payment ecosystem providers, quantify opportunities using issuer and BIN intelligence, and communicate product strategy and performance to stakeholders.

Leads the design, implementation, and support of complex, scalable software components and distributed systems. Provides technical leadership through architecture decisions, mentoring, code and design reviews, troubleshooting, and engineering standards. Partners with product, quality, architecture, and DevOps teams on secure cloud-native solutions, CI/CD, observability, performance, releases, and platform modernization. Participates in hiring and communicates technical risks and tradeoffs to stakeholders.

Leads biostatistical activities for Phase II–III clinical trials, from protocol design through reporting and regulatory submission support. Responsibilities include statistical analysis planning, sample size calculations, SAP development, TLF review, database lock support, safety reporting, regulatory responses, and oversight of programming or CRO work. Collaborates with cross-functional clinical teams and uses SAS or R for analyses, modeling, simulation, exploratory work, and quality control. Coordinates statistical activities across studies and applies advanced statistical and machine learning methods.

Leads statistical activities for Phase II–III clinical trials from protocol design through reporting and regulatory submissions. Develops statistical analysis plans, sample-size calculations, and reviews tables, listings, and figures. Oversees internal programmers and CRO contributions, applies SAS and R for analyses and quality control, and collaborates with clinical, data management, programming, and regulatory stakeholders. Coordinates statistical activities across studies and applies advanced statistical, machine learning, and modeling methods to support drug development.