Top Remote Machine Learning Jobs in Atlanta, GA

Own customer outcomes from discovery through production deployment. Build decision systems using ontologies, reasoning, graph analytics, optimization, and machine learning over enterprise data. Write Python, SQL, and PyRel; debug unfamiliar systems; tune performance; harden deployments; document reusable solutions; and upstream platform improvements. Lead technical workshops and proofs of concept while partnering closely with customers, Product, and Engineering.

Drive new-logo sales and revenue growth for Wonderlic’s talent assessment and employee development solutions. Identify and prospect target clients, build relationships with HR and recruiting decision-makers, manage opportunities through the full sales cycle, close new business, and transition customers to Customer Success. Maintain expertise in products, market trends, and competitive conditions while providing strategic insights. The role requires B2B SaaS and HR technology sales experience, quota achievement, pipeline generation, and experience selling to SMB, mid-market, and enterprise buyers.

Own strategic partnerships with CPG customers, driving measurable outcomes, platform adoption, user expansion, and retention. Analyze usage data and business context to identify risks and opportunities, develop success plans, lead executive and operational relationships, and advocate for customers internally. Collaborate with product, sales, marketing, and support teams while contributing to repeatable playbooks, thought leadership, and customer best practices.

Build and maintain Quora’s machine learning platform and distributed model-serving infrastructure. Develop scalable systems for recommendation and language models, optimize GPU serving latency, throughput, and cost, improve ML tooling and feature stores, and contribute to platform modernization. The role includes production deployment, collaboration with senior engineers, mentoring, and participation in an on-call rotation.

Conduct applied research on scalable optimization and resource-allocation algorithms for large communication networks. Apply machine learning, reinforcement learning, integer optimization, graph-based methods, and metaheuristics to improve solution quality and efficiency. Collaborate with software engineers to integrate algorithms into production aerospace networking systems, evaluate performance at realistic scale, publish research, support patent applications, and document novel methods.

Build and maintain computer vision and machine learning pipelines for automated device inspection, grading, and quality assessment. Responsibilities include data preparation, feature engineering, model training and evaluation, production Python development, monitoring, annotation workflows, documentation, code reviews, and collaboration with product and operations teams. The role supports the full ML lifecycle and offers increasing ownership under senior engineering guidance.

Senior individual-contributor product manager owning agentic and platform capabilities: define API/MCP/headless surfaces, non-functional requirements (scale, performance, reliability, security), and agentic workflows. Set product strategy and roadmap, write specs for engineering, partner with AI/ML, Data Science, Professional Services, and customer teams, and ensure auditability and regulatory readiness for enterprise financial customers.

Lead AI agent optimization efforts by designing systems that improve token efficiency, response time, reliability, and output quality. Build agentic coding systems, develop AI application patterns, own features end to end, collaborate with engineering and field teams, support the developer community, and create public technical documentation. Work across Python, TypeScript, Java, and Go while serving as a domain expert in agent design and optimization.

Design and productionize machine learning, deep learning, LLM, personalization, recommendation, search, and retrieval systems. Collaborate with product, engineering, and analytics teams to deploy AI solutions that improve customer experiences, business growth, and operational efficiency. The role requires driving projects from ideation through deployment and delivering measurable business impact in a fast-moving fintech environment.

Own the full lifecycle of production classification and detection models, including transformer fine-tuning, entity detection, behavioral risk scoring, training, serving, versioning, evaluation, drift detection, and retraining. Build SageMaker-based ML systems and define output contracts supporting audit and policy enforcement. Lead the evaluation harness and mentor a co-op while operating as the company’s sole ML engineer.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Own LanceDB’s product direction by shaping the roadmap with technical leadership, researching users and competitors, gathering continuous user insight, and driving cross-functional prioritization. The role partners closely with engineering as a technically respected peer, defines product changes for AI and data-platform users, uses metrics and experiments to guide decisions, and operates with high ownership in an ambiguous environment.

Build and deploy generative AI solutions for creative enterprise workflows across image, video, 3D, vector graphics, and design. Partner with customers to identify opportunities, prototype integrations, adapt multimodal models, integrate AI into production systems, diagnose technical issues, evaluate solution quality, and develop reusable deployment patterns. Collaborate with research, product, engineering, and design teams to translate customer needs into scalable technical solutions.

Lead the architecture and implementation of AWS cloud-native platforms for fraud detection, risk analysis, and digital intelligence. Design scalable, resilient, secure, low-latency REST and event-driven systems using Java, Kubernetes, Kafka, and SQL/NoSQL databases. Set technical strategy and architecture standards, mentor engineers, conduct reviews, and guide multiple teams. Partner with product, data science, security, and business leaders to embed AI and machine learning capabilities, while improving delivery, observability, compliance, and cost efficiency.

Advise enterprise clients on AI and data strategies, transformation roadmaps, operating models, governance, privacy, technology rationalization, and business cases. Lead discovery workshops, executive engagements, and art-of-the-possible sessions; prioritize use cases and connect technical recommendations to business outcomes. Build the advisory practice, develop offerings, manage multidisciplinary teams, mentor staff, strengthen client and partner relationships, and support successful distributed project delivery.

Develop and integrate production AI/ML capabilities into Oracle E-Business Suite on OCI for federal financial management. Build NLP and document-processing solutions, REST API integrations, ML lifecycle workflows, cognitive performance metrics, and data-mining capabilities. Participate in SAFe Agile delivery, author user stories, support DevSecOps integration, resolve technical impediments, and contribute to transition and knowledge-transfer materials. The role requires collaboration with architecture, product, and government teams in a cleared defense environment.

Tune AML and fraud detection models using customers’ live transaction data. Investigate missed cases, alert quality, pattern drift, and false positives; validate changes through backtesting and sample review; document regulatory rationale; and present model performance and business value to compliance, risk, and data science stakeholders. Partner with customer teams on account strategy and relay customer-specific or systemic findings to product and model functions.

Reviews and evaluates engineering, infrastructure, and platform documents and work products for technical accuracy, completeness, clarity, consistency, and relevance. Provides structured feedback, identifies errors and gaps, compares technical approaches, and follows defined evaluation frameworks. Requires independent work, strong communication, analytical ability, and professional engineering judgment. The role is fully remote and supports varied technical evaluation projects as an independent contractor.

Drive net-new enterprise customer acquisition from lead generation through contract closure. Build C-level relationships, develop account strategies, manage CRM pipelines and forecasts, negotiate complex contracts, and close large AI, machine learning, and data talent engagements. Partner with marketing, customer success, and talent teams to deliver customer outcomes and expand Factored AI’s enterprise footprint.

Own the production lifecycle for machine learning and LLM-backed systems, including training and post-training pipelines, inference serving, model releases, monitoring, retrieval infrastructure, and cost and latency optimization. Partner with AI researchers and engineering teams to move prototypes into reliable production capabilities across customer tenants. Develop ETL and GraphQL features, enforce tenant isolation, and support deployments in regulated or customer-controlled environments.

Designs, deploys, and scales production AI agents and agentic workflows within enterprise Java and Spring applications. Establishes evaluation, monitoring, guardrail, safety, testing, observability, deployment, and operational standards for LLM systems. Leads architectural decisions across AI services and microservices, productionizes models, evaluates emerging technologies, mentors engineers, and influences technical strategy across teams.

Build and deploy production AI agents, agentic workflows, and ML-enabled enterprise features within Java/Spring microservices. Implement evaluation, monitoring, guardrails, safety, testing, and observability for LLM applications. Collaborate with AI scientists, engineers, product managers, and technical specialists to productionize models, integrate RAG and tool calling, prototype solutions, and improve AI capabilities. The role also involves design reviews, agile planning, software quality ownership, and staying current with emerging AI technologies.

Own end-to-end analytics delivery for a client POD, translating business priorities into workplans, coordinating cross-functional teams, governing delivery and revenue KPIs, and managing risks through resolution. Perform hands-on data profiling, validation, reconciliation, exploratory analysis, automation, and quality reviews using SQL, Python, Databricks, and Excel. Create executive-ready KPI, revenue, and business-impact narratives, prioritize initiatives, support business cases, and identify opportunities for analytics automation and AI-enabled improvements.

Lead the architecture and development of Root’s machine learning platform for insurance pricing. Build infrastructure for feature pipelines and stores, reproducible training and orchestration, model registries, serving, validation, observability, and research-to-production workflows. Establish versioned contracts and technical standards, automate workflows using LLMs, write and review critical code, influence cross-team strategy, and mentor senior engineers. Collaborate closely with data scientists and researchers to create scalable, reliable, and reproducible ML capabilities.

Lead the design and implementation of large-scale AI and machine learning systems for Yahoo Search across multiple teams. Define data strategies, evaluation frameworks, and scalable production-serving approaches. Drive technical alignment among engineering, product, design, and research teams; guide adoption of large language models, vision models, generative AI, and agentic AI; and oversee data analysis informing strategic product decisions.