Top Machine Learning Jobs in Atlanta, GA

Drive end-to-end product design for Avid Content Core, representing the customer to define and prioritize product stories, collaborate with engineering on implementation, integrate AI into SaaS offerings, engage customers and partners, and ensure workflows, deployment, security, and quality throughout the agile development lifecycle.

Lead strategy and execution for fraud risk management across the platform. Develop detection and prevention frameworks, optimize transaction screening, partner with Compliance, Data Science, Engineering and Product, oversee KPIs and vendor costs, lead a risk strategy team, and pilot AI/LLM-driven tools to enhance detection while enabling business growth and regulatory alignment.

The Enrollment Advisor helps prospective patients through the onboarding process, providing information and support throughout their enrollment in Revero's program.

Provide technical advisory and solution feasibility analysis for enterprise AI initiatives. Evaluate AI engineering approaches, integration complexity, reusable components, and architectural tradeoffs. Support prototyping guidance, vendor capability evaluation, and surface governance, risk, and technical constraints to inform enterprise decisions.

The Director of Product Management will oversee the product lifecycle for AI products, focusing on customer collaboration, rapid prototyping, and product strategy. The role involves leading product teams, driving execution, and shaping the product culture.

Remote technical recruiter responsible for sourcing and hiring software engineers and niche technical talent for startups. Tasks include sourcing via LinkedIn and job boards, shortlisting, verifying technical fit, researching companies and market trends, developing and executing recruitment strategies, and communicating with candidates and stakeholders. Immediate joiners preferred.

As a GTM Engineering Intern, you will automate workflows, design demos, and develop AI-powered tools for market outreach while collaborating with sales and engineering teams.

Lead architecture, design, development, deployment, and optimization of an agentic AI platform. Partner with product, data, security, and business teams to productionize AI solutions, establish engineering best practices, mentor teams, and drive reusable AI services, automation, and operational improvements to deliver business impact.

Own Cash Application and Deduction Resolution Management products end-to-end. Lead discovery, define requirements, prioritize roadmap, partner with engineering/design/sales, ensure financial accuracy, build ERP integrations, drive launches, and maintain product quality and customer-focused outcomes.

Lead machine learning initiatives, oversee product development, improve execution pace, enhance team reliability, and foster team growth at Hightouch.

The VP of Marketing will lead the marketing ecosystem to drive growth, define competitive advantages, influence product design, and mentor a diverse team towards a mission-driven culture.

The SAP Innovation Architect will design SAP automations, mentor engineers, and drive innovation in AI-driven SAP operations, while ensuring scalability and security in integrations.

The Customer Success Manager drives the adoption of AI solutions in life sciences by managing implementations, developing customer satisfaction strategies, and serving as a trusted advisor.

Define and execute the companys AI strategy; build and lead the data science, ML engineering, and AI teams; govern intake, prioritization, and AI guardrails; advance production ML, MLOps, LLMs and agentic workflows; partner cross-functionally to deliver measurable ROI; and report AI impact and roadmap to the CEO and board.

Lead design and delivery of scalable AI/ML systems for public health: architect generative AI, RAG, and agent solutions; build data pipelines with PySpark/Foundry; define evaluation, governance, privacy, and MLOps practices; integrate enterprise AI tools and ensure secure, explainable production deployments.

Lead ML engineer responsible for designing, building, and deploying scalable ML and Generative AI solutions. Own ML workflows, pipelines (PySpark/Palantir), evaluation, agent architectures, retrieval and memory management, data governance, and cross-functional technical collaboration for public health solutions.

Lead strategic positioning and execution for wildfire risk management solutions. Responsibilities include market strategy, sales enablement, research, demand generation, product evangelism, and cross-functional collaboration to ensure effective product marketing.

The Director of Fraud Risk will lead the fraud management strategy, overseeing governance, loss minimization, and advanced detection technologies, ensuring organizational integrity and security.

Serve as a customer-facing technologist partnering with sales to position, demo, and package Forrester AI solutions; translate technical AI capabilities into business value, support deal structuring and negotiations, develop enablement and adoption assets, gather product feedback from the field, and drive client retention through training and value realization.

This role involves developing and validating actuarial models, collaborating with engineering, and translating actuarial specifications into production-ready tools for P&C insurance clients.

Manage the vision, strategy, and roadmap for Contract Intelligence AI products. Collaborate with teams to define requirements and market strategies, ensuring delivery of generative AI solutions.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

Lead statistical activities for clinical trials from protocol design through reporting. Provide statistical oversight, collaborate with cross-functional teams and CROs, apply SAS/R (and ML) for analyses, automate processes, and mentor junior statisticians.

The Lead Bioinformatics AI Scientist will lead AI and GenAI R&D initiatives to develop advanced clinical testing and genomic data analysis platforms, leveraging expertise in bioinformatics and machine learning.

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.