Top Machine Learning Jobs in Atlanta, GA

Conduct research on frontier AI systems and develop datasets, experiments, prototypes, tooling, evaluation frameworks, and reinforcement learning environments. Investigate synthetic and agentic data generation, post-training, model understanding, benchmarks, and evaluation. Collaborate with research, engineering, product, and operations teams to translate findings into scalable AI applications, while contributing to technical publications, open-source work, mentorship, and the broader AI research community.

Conduct frontier AI research and build research-grade datasets, prototypes, tooling, reinforcement learning environments, benchmarks, and evaluation frameworks. Design rigorous experiments, train and evaluate models, investigate synthetic and agentic data generation, and translate findings into scalable products. Collaborate with research, engineering, product, and operations teams, communicate technical insights, contribute to publications and open-source work, and mentor peers. This is a fully onsite role based in San Francisco, Palo Alto, or Seattle.

Conduct original research to evaluate, train, and improve frontier AI systems. Design benchmarks, synthetic-data methods, reliability and hallucination evaluations, verification techniques, and agentic science workflows. Identify research gaps, formulate hypotheses, build experiments and evaluation pipelines, publish findings, and develop new research directions across STEM and AI. The role requires strong Python skills, quantitative reasoning, technical writing, and expertise with modern LLMs.

Conduct original research to evaluate, train, and improve frontier AI systems. Responsibilities include developing STEM benchmarks, synthetic data methods, reliability and hallucination evaluations, verification techniques, and agentic science workflows. The role involves identifying research gaps, designing rigorous experiments, building evaluation pipelines, publishing findings, and proposing new research directions across reasoning, AI evaluation, data generation, and AI-for-science.

Conduct frontier AI research and build research-grade datasets, prototypes, tooling, reinforcement learning environments, benchmarks, and evaluation frameworks. Investigate synthetic and agentic data generation, post-training, model understanding, and model capabilities. Design rigorous experiments, analyze results, and translate findings into scalable products and AI systems while collaborating across research, engineering, product, and operations teams.

Own the end-to-end architecture, development, deployment, evaluation, and operations of AI and machine learning systems. Build predictive ordering models, agentic workflow copilots, retrieval and evaluation infrastructure, safety guardrails, and responsible AI practices. Prioritize AI initiatives based on business outcomes, establish model governance and production standards, advise product and engineering leadership, and mentor experienced contributors.

Own the strategy, roadmap, packaging, and go-to-market evolution of Pluralsight’s AI, Cloud, and Security learning solutions. Design blended learning programs, define expert perspectives, identify content and lab components, monitor market needs, and use customer and learner data to improve outcomes. Partner with Product, Sales, Marketing, Curriculum, Engineering, and Services teams, support strategic enterprise deals, manage solution benchmarks, and drive adoption, renewals, and revenue growth.

Supports the Data Science and Machine Learning team by analyzing large-scale datasets, identifying anomalies and root causes, developing data validation and normalization methods, and improving the accuracy of derived data products. The intern will evaluate models, translate ambiguous problems into statistical formulations, debug messy data, test hypotheses, and implement maintainable analytical solutions using Python, SQL, and distributed data platforms.

Lead the development and production deployment of applied machine learning and LLM solutions across product, marketing, and operations. Own ranking, recommendation, filtering, personalization, experimentation, and model evaluation systems. Partner with business and technical teams to identify valuable use cases, establish modeling best practices, and ensure reliable production deployment. Manage and mentor applied ML practitioners while maintaining technical rigor, business alignment, and measurable customer and revenue impact.

Develops and validates machine learning and statistical models for anti-money laundering, transaction monitoring, customer screening, and risk rating. The role evaluates AML data quality, integrates vendor solutions, maintains governance and regulatory documentation, leads model lifecycle quality control, and reports performance through management dashboards. It also supports AML risk assessments, audits, regulatory examinations, and cross-functional stakeholder coordination.

Build scientific advisory and technical enablement programs for enterprise and research partners adopting an AI materials platform. Engage computational chemists and materials scientists, provide technical guidance, lead workshops and demonstrations, create documentation, notebooks, APIs, and reference materials, and relay partner feedback to AI Research, Chemistry, Product, Marketing, and PR teams.

Leads strategic enterprise contract negotiations for indirect goods and services, including cloud, software, telecom, data center, colocation, and professional services agreements. Drafts and negotiates complex contracts, develops statements of work, KPIs, remedies, and contracting strategies, and collaborates with legal, finance, security, tax, compliance, and business stakeholders. Improves contract templates, governance, processes, and digital contracting capabilities while managing approvals, signatures, administration, tracking, and reporting.

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 design, development, deployment, and operation of production AI and ML systems. Build high-throughput Python services and Databricks pipelines, support traditional ML and LLM applications, establish testing and deployment standards, and develop monitoring for quality, reliability, latency, throughput, and cost. Partner with product, research, and responsible AI teams on governance and release criteria. Provide architectural leadership, conduct code reviews, mentor engineers, and resolve complex technical dependencies.

Analyze billions of user behavior, fund-flow, and market records using PySpark, Hive, Trino, and SQL. Build feature pipelines, investigate data anomalies, and develop and evaluate machine learning models for risk detection. Translate model performance into business metrics such as detection rate, false-positive rate, and latency while owning a project stream end to end.

Leads client-facing data science and analytics initiatives, translating complex marketing and media data into actionable business narratives. Develops measurement strategies, including test/holdout incrementality, informs acquisition and retention decisions, scales analytics best practices, and manages analyst teams. Partners with Sales, Client Services, Operations, and other stakeholders while supporting predictive and adaptive AI/ML solutions. The role requires strong client communication, strategic leadership, analytics expertise, and proficiency with Python, SQL, Tableau, and related tools.

Build and operationalize machine learning models for large-scale advertising optimization. Responsibilities include designing high-volume services, developing data solutions, training and evaluating models, improving calibration and guardrails, creating offline and online evaluation workflows, and monitoring production systems. The role requires collaboration with product and engineering teams, ownership of reliability and performance, participation in on-call rotations, and deploying scalable, cost-effective ML solutions.

Leads parallel customer AI/ML solution design engagements from discovery through proposal. Architects enterprise solutions involving predictive analytics, NLP, computer vision, and generative AI; provides cost-benefit and TCO analysis; identifies risks and dependencies; and collaborates with Agile, system architecture, and DevSecOps teams to deploy governed, explainable, and monitored AI/ML systems.

Leads data engineering, Palantir Skywise architecture, and AI solution development for Airbus manufacturing and operational environments. Builds scalable pipelines, analytical workflows, machine learning and generative AI applications, and enterprise data foundations. Oversees Alteryx and Tableau platforms, establishes governance, mentors technical teams, translates business challenges into data products, and supports AI Center of Excellence initiatives. The role is onsite 60% across several U.S. locations and requires approximately 10% travel.

Supports enterprise sales by serving as the technical and product expert for Wavelytics, an AI-powered analytics suite. Responsibilities include delivering technical demonstrations, positioning analytics and AI capabilities, translating complex solutions into business value, addressing security and governance questions, supporting customer adoption, creating sales playbooks, and collaborating with Product and Marketing to improve roadmap alignment, retention, and revenue growth.

Leads the bankruptcy risk solutions product portfolio across the Americas, owning product vision, strategy, roadmap, execution, customer discovery, and commercialization. Partners with Engineering, Data Science, Sales, Marketing, Client Success, and enterprise customers to develop data-driven products, manage backlogs in Jira, support migrations, monitor KPIs, and drive adoption, growth, and product-market fit.

Leads and builds the mid-market business development function, owning territory strategy and the full sales cycle for pharma and biotech accounts. Develops and coaches a high-performing BD team, drives pipeline and new-logo acquisition, closes multi-year SaaS deals, and partners with marketing, strategy, product, and commercial operations. Represents the company at industry events and contributes to commercial frameworks, forecasting, segmentation, and coverage models.

Lead technical product strategy and execution for AI-powered learning intelligence infrastructure. Own roadmaps for AI curation, data ingestion, recommendation algorithms, learner profiles, analytics, and platform interoperability. Translate architecture, research, data, and business requirements into technical specifications, backlogs, success criteria, and implementation plans. Coordinate cross-functional stakeholders, manage delivery risks, and ensure systems are explainable, privacy-aware, validated, and aligned with educator and learner needs.

Manage a portfolio of B2B SaaS customers in the HR technology space, driving renewals, expansions, and revenue growth. Build strategic client relationships, conduct business reviews, monitor customer health, manage renewal and sales cycles, introduce products, and advocate for customers internally. The role requires strong sales acumen, analytical abilities, HR technology expertise, and experience influencing HR and L&D decision-makers. It also includes identifying adoption barriers, creating customer feedback loops, and mentoring others.

Own Nabla’s product and EHR partnerships from sourcing through negotiation, integration, launch, and performance tracking. Build scalable partnership processes, negotiate commercial agreements, coordinate cross-functional workstreams, and strengthen relationships with healthcare platforms and health systems. Support corporate development by monitoring adjacent markets, sourcing acquisition targets, developing investment theses, and coordinating diligence. Translate technical and commercial requirements across Product, Engineering, Legal, Finance, Marketing, and Customer Success.