Top Machine Learning Jobs in Atlanta, GA

Early-career role analyzing PB-scale on-chain, transactional and customer data. Perform data engineering, ETL, querying, build visualizations and ML models, translate insights for teams, manage reporting environment, secure confidential data, and troubleshoot reports/databases.

Design, build, and manage large-scale search and content recommendation systems using machine learning and personalization techniques. Collaborate with content, product, and business teams to define success metrics, drive AI product improvements, and maximize content value and user impact.

Lead design and management of search and content recommendation services and models. Build personalized recommender systems using ML/AI, define success metrics with product and business teams, drive core module development, and translate PB-scale data insights into measurable product impact.

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.

Early-career Performance Engineer in the Binance Accelerator Program. Perform hands-on performance testing and tuning, design AI-powered test strategies using LLMs and ML, build AI-driven anomaly detection and bottleneck analysis tools, research new AI technologies, and improve reliability of large-scale distributed systems.

Design, back-test and implement pricing, risk management, and agency trading strategies; collaborate with developers to integrate models into the trading platform; participate in product ideation, modeling, parameter tuning, and risk evaluation.

The VP of Data and AI will define and execute data and AI strategies, lead technical teams, oversee data platform infrastructure, and drive measurable business outcomes.

Lead a team of analysts to advance fraud and safety operations through analytics, ensuring robust data products and compliance while empowering stakeholders with actionable insights.

Own end-to-end analysis of payments, chargebacks, and user lifecycle to identify drivers of net loss. Deep-dive into fraud cases, design and evaluate A/B experiments and risk-rule changes, build monitoring dashboards, partner with Risk, Payments, and Product to convert insights into mitigations, and run ad-hoc analyses of emerging loss trends.

The Product Marketing Manager will develop go-to-market strategies, create compelling messaging, perform market research, and collaborate with product and sales teams to position Oscilar's AI risk platform effectively.

Provide legal support for commercialization and product development of an AI-powered ad tech platform: draft and negotiate commercial agreements, manage IP portfolio and complex IP litigation, advise on AI, data, and privacy matters, counsel product teams on compliance and IP protection, and develop automated legal workflows.

Lead Lean transformation initiatives and AI coordination to enhance process excellence across multiple business functions, coaching teams and ensuring integration with operational standards.

Lead enterprise architecture and define future-state cloud, data, and integration platforms. Guide DevOps, data, integration, and infrastructure teams; design scalable secure cloud and on-prem solutions across AWS/Azure/Nutanix; drive IaC, CI/CD, automation, monitoring, cost optimization, and security; advise senior leadership and mentor engineering teams.

Lead and mentor engineering teams to deliver cloud-native, scalable, secure supply chain and retail platforms. Drive delivery, engineering excellence, CI/CD/DevOps practices, observability, vendor partnerships, and AI/ML-enabled development while partnering with Product and Architecture.

Two research fellows will receive a stipend and compute credits to pursue projects in legal AI and automated negotiation. Fellows get expert access, private contract data, and are expected to produce a publishable paper, benchmark, or open-source artifact. Research topics include reward modeling under expert disagreement, negotiation simulation, minimal-diff editing benchmarks, uncertainty/hand-off strategies, or related legal AI problems.

Lead and execute independent research programs on NLP and LLM safety, design and run experiments, secure funding, publish and present results, lead cross-functional teams, and develop evaluation tools and challenges to advance digital safety.

The Principal Data Scientist leads AI/ML strategy, ensuring high standards in modeling and experimentation, and fosters cross-functional collaboration for enterprise decision-making.

Lead and operate a 10,000 sq ft micro-fulfillment lab to validate and scale warehouse automation, robotics, and AI-driven operations. Own strategy, budgeting, vendor selection, experimentation, and deployment frameworks to drive measurable productivity, capacity, accuracy, and cost improvements across Stord's fulfillment network. Build and mentor a multidisciplinary team and partner with internal and external stakeholders to commercialize solutions.

Independently validate and monitor machine learning models for credit underwriting, credit risk, and fraud detection. Identify model weaknesses, recommend improvements, and collaborate with model owners to remediate findings. Support and maintain the Model Risk Management framework and coordinate with Internal Audit, Internal Controls, and Compliance on audit and regulatory requests.

As a Machine Learning Engineer II, you will enhance AI systems for automating customer operations, develop evidence extraction pipelines, and collaborate with teams on model development and deployment.

Design, develop, and implement secure, scalable cloud systems (Azure/AWS); perform system engineering, risk assessments, and NIST-compliant ATO/ATD processes; manage secure data ingestion and orchestration; integrate AI/ML concepts; secure communications and network infrastructure; present technical findings to senior USG leadership and provide hands-on support.

Evaluate and strengthen AI model behavior on cybersecurity topics by writing expert prompts, annotating and grading model responses for accuracy and safety, and applying structured guidelines to classify sensitive content. Work part-time on AI safety tasks with emphasis on secure, responsible handling of dual-use information.

Research, deploy, and maintain AI models and automation solutions; integrate systems and APIs; manage AWS infrastructure and PostgreSQL databases; build BI dashboards; monitor and optimize AI systems and provide technical support.

Design, prototype, and deploy production-ready AI products using ML and generative AI. Lead cross-functional teams, collaborate with stakeholders to solve workflow problems, build scalable prototypes and proofs-of-concept, and apply latest ML/GenAI research techniques.

Own growth and performance for Kits AI across funnel optimization, activation, retention, pricing, and monetization. Productize ML capabilities into creator-facing workflows, partner with research and engineering, run experiments and analytics (e.g., Amplitude), conduct customer research, and leverage AI tools to accelerate shipping and internal insight systems.