Top Remote Machine Learning Jobs in Atlanta, GA

Designs and builds highly scalable, fault-tolerant Azure compute services, microservices, REST APIs, billing pipelines, and distributed systems. Responsibilities include improving availability, security, scalability, performance, disaster recovery, and compute utilization; using telemetry and data-driven insights; developing cloud-native solutions with containers, NoSQL, and Big Data technologies; and leveraging AI-assisted development in an Agile environment.

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.

Develop fraud-detection and identity-risk machine learning models from problem definition through deployment. Investigate fraud patterns, engineer predictive features, evaluate model performance, and monitor drift across batch and real-time decisioning environments. Collaborate with senior data scientists and engineers using Python and PySpark while supporting model documentation, explainability, privacy, validation, and governance.

Write technically accurate case studies, articles, product explainers, launch materials, and social content for AI engineers, researchers, and technical leaders. Interview customers, partners, and internal experts; learn HUD’s platform and workflows; explain technical products and research clearly; support founder communications; manage content from ideation through publication; and improve content using reader and go-to-market feedback.

Support analytics by cleaning, validating, and transforming data; build dashboards and KPI reports; perform trend, cohort, and A/B testing analyses; improve data pipelines and forecasting; present insights to cross-functional teams.

Own a new enterprise sales territory, build pipeline from zero, and close advisory-style security software deals with large organizations. Develop relationships with senior security leaders, engineers, and data science teams; tailor sales engagements over lengthy cycles; coordinate demonstrations with pre-sales specialists; and involve company leadership in strategic opportunities. The role requires enterprise security software sales experience, AI and machine learning fluency, startup experience, and proximity to New York City or northern New Jersey.

Own a new enterprise sales territory, build pipeline from scratch, and close complex security software deals through long consultative sales cycles. The role involves prospecting large organizations, engaging senior security executives and technical users, tailoring solutions, coordinating technical demos with sales engineers, and re-engaging prospects over time. Candidates need at least five years of enterprise sales experience, a record of closing security software deals with large buyers, startup experience launching emerging offerings, and credible knowledge of AI and machine learning.

Product Counsel will advise product, engineering, data science, marketing, compliance, and sales teams throughout the product lifecycle. Responsibilities include counseling on regulatory compliance, privacy, intellectual property, consumer protection, AI and machine learning, data rights, platform architecture, APIs, identity and account security, and marketing. The role translates legal requirements into technical guidance, supports AI governance, monitors evolving laws, and develops scalable legal processes for a fast-paced technology company.

Leads DISCO’s machine learning research strategy, directs advanced algorithm and methodology development, oversees large-scale research programs, and manages research teams. Collaborates with industry partners and cross-functional stakeholders to apply machine learning to real-world challenges. Develops long-term research plans, mentors researchers, optimizes research processes, and contributes to the broader research community through publications, presentations, and industry engagement.

Develops predictive models, machine learning solutions, data pipelines, statistical analyses, and internal data applications. The role uses Python and SQL Server to analyze structured and unstructured data, applying NLP, classification, regression, clustering, automation, and AI/LLM techniques. Responsibilities include maintaining data science platforms, supporting cross-functional decision-making, and communicating technical findings to non-technical audiences.

Develops and deploys production machine learning models and data products for enterprise use. Responsibilities include data mining, feature engineering, model evaluation, NLP and LLM/RAG solutions, API and pipeline integration, cloud-based deployment, model validation, A/B testing, documentation, and lifecycle management. The role translates business needs into scalable data science solutions and collaborates with product, engineering, and business stakeholders throughout the SDLC.

Leads enterprise-wide technical and transformation initiatives across Product, Technology, Operations, Data, Security, and Innovation. Responsibilities include program governance, requirements gathering, product documentation, architecture and process modeling, delivery coordination, risk and dependency management, AI use-case evaluation, vendor assessment, and executive stakeholder communication. The role establishes documentation standards and supports modernization through Agile practices, integrations, automation, and responsible AI governance.

Build end-to-end features for marketing and growth SaaS products, including interactive user interfaces, CRUD applications, backend APIs, analytics platforms, observability tools, and ML integrations. Collaborate with customers, designers, product managers, and ML engineers to translate real-world needs into intuitive products and experiments. Independently architect frontend systems while contributing to backend data models and API design.

Leads financial strategy, planning, forecasting, and operating cadence across Sales, Marketing, Customer Success, and GTM Operations. Owns GTM economics, sales compensation, commissions forecasting, executive reporting, driver-based modeling, resource allocation, and profitability analysis. Modernizes Finance through AI, automation, advanced analytics, improved controls, and scalable processes while developing the GTM Finance team and advising senior executives.

Leads automated scoring and natural language application research for educational assessment products. Responsibilities include setting team priorities, mentoring scientists, designing and analyzing studies, developing machine learning applications, collaborating with stakeholders, presenting technical findings, writing reports, publishing applied research, and supporting products involving automated scoring, crisis-content detection, and formative feedback.

Lead and grow a data science team building production machine learning models. Oversee the full model lifecycle, including data sourcing, feature engineering, training, deployment, and monitoring. Coach technical staff, manage planning and executive communication, investigate fraud patterns, improve model performance, and deliver dependable production code for real-time decisions. Present analyses that guide data strategy, product direction, operations, marketing, and sales.

Leads customer-facing technical delivery from demonstrations and requirements discovery through architecture, implementation, testing, and production launch. Builds AI-driven agents, analytical products, predictive modeling workflows, and data pipelines; integrates solutions with client CRMs, consumer applications, and varied data sources. Partners with client technical teams, presents to senior stakeholders, and feeds field learnings back to the core platform team. Requires occasional travel and strong Python, TypeScript, architecture, cloud, and communication skills.

Design, develop, secure, modernize, and support scalable omnichannel APIs for customer-facing web, mobile, contact center, partner, and enterprise applications. Improve API security, resiliency, performance, observability, testing, and reliability. Collaborate with architecture, security, cloud, product, operations, and engineering teams; lead technical delivery, mentor engineers, develop reusable patterns, and provide Tier 3 production support. Apply AI-assisted engineering practices and contribute to API lifecycle management and continuous improvement.

Lead the architecture, development, integration, and validation of AI-enabled autonomy for UAS, drone swarms, robotic platforms, and C2 systems. Develop real-time decision-making, sensor fusion, navigation, multi-agent coordination, and edge AI capabilities. Build simulation and digital-twin environments, conduct hardware-in-the-loop and flight testing, integrate tactical systems, and improve resilience against contested environments. Lead engineering teams, manage AI/ML governance and deployment, support DevSecOps and cybersecurity compliance, and advise mission stakeholders.

As a Research Engineer, you'll develop and manage data systems for text-to-speech projects and enhance machine learning models using innovative strategies.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

Early-career Data Scientist supporting supply chain transformation through machine learning, forecasting, statistical modeling, data preparation, dashboards, and AI solutions. The role addresses inventory optimization, demand forecasting, production planning, delivery performance, and cost reduction. Responsibilities include developing analytical datasets and workflows with Python and SQL, evaluating models, integrating enterprise data, supporting cloud-based analytics, and communicating insights to business stakeholders in a manufacturing environment.

Own end-to-end SaaS implementations from solution alignment through configuration, data preparation, training, UAT, go-live, and handoff. Serve as the primary customer and project lead, managing timelines, risks, dependencies, stakeholders, and readiness. Configure workflows and platform settings, troubleshoot data and configuration issues, coordinate cross-functional teams, and identify implementation improvements. The role requires strong customer communication, project management, SaaS configuration, data handling, and business workflow analysis skills.

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.

Own complex post-sale technical relationships from onboarding through adoption and renewal. Lead implementations, integrations, configurations, and security reviews; advise strategic customers on identity, access, device trust, and SaaS security. Partner with Customer Success and cross-functional teams to drive customer value, manage risks, support expansion, create scalable resources, and mentor team members while serving as an escalation point.