Top Machine Learning Jobs in Atlanta, GA

Lead customer-facing data science engagements: manage data acquisition and cleaning, build and evaluate scalable models, drive model best practices and ethics, write extensible code, research industry trends, define solution strategy, and collaborate across engineering and product teams to deliver AI/cloud solutions.

Lead design, build, and productionize ML models and agent workflows. Own model selection strategy, AI governance (bias, compliance, audit trails), and evaluation systems using the GenAI Eval Framework. Coach teams on AI adoption, build monitoring/infrastructure, benchmark models for cost/quality, curate labeled datasets, detect drift, and translate model results to business value.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

Lead HR Operations and Total Rewards with an AI-native mindset. Own compensation, equity, benefits, HRIS (Workday) operations, ATS (Ashby) administration, workforce analytics, compliance, and immigration. Partner with Enterprise Technology to embed LLMs and automation, build self-service HR experiences, manage vendor renewals, and lead a team delivering people insights to executives.

Senior pre-sales engineer who partners with sales and channel teams to present Snowflake, run demos and proofs-of-concept, design solutions, and close large enterprise deals. Requires deep expertise in databases, cloud data architectures, ML/generative AI, SQL and Python, and the ability to engage both technical and executive stakeholders.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

Lead the machine learning strategy, design scalable ML systems, drive MLOps, mentor engineers, and comply with AI governance.

As a Principal Software Engineer, you'll architect and code backend services and ML models, integrate them with Salesforce, and mentor junior engineers.

Lead the Client Services Program Manager to deploy business solutions in financial services, managing multiple client programs and ensuring high-quality service delivery.

Design, develop, and deploy AI/ML solutions to enhance enterprise Identity, Credential, and Access Management (ICAM). Build predictive analytics, anomaly detection, behavioral risk scoring, LLM/RAG solutions, and NLP capabilities. Integrate AI with ICAM platforms, create prototypes, ensure DoW security and responsible AI governance, support data preparation and model lifecycle, and collaborate with stakeholders and cybersecurity teams.

Lead development, validation, and operationalization of P&C actuarial models and workflows. Convert actuarial requirements into production-ready code, leverage cloud/AI tools, validate data pipelines, produce client-ready reporting, and liaise between actuarial SMEs and engineering teams while contributing to practice tooling and business development.

Lead a team to analyze, optimize, and grow merchant revenue using analytics and AI. Define strategy, set KPIs, deploy ML solutions, drive go-to-market alignment, execute tests, and deliver actionable insights to executives and cross-functional partners.

Lead and build a team that drafts and submits state-specific rating rules, underwriting guidelines, and forms; manage filing execution via SERFF, handle objection responses, coordinate cross-functional stakeholders, establish operating rhythms, improve documentation and controls, and coach staff to scale filing expertise and regulatory execution.

Lead partner analytics by building scalable dashboards, forecasting and ROI models, and experimental designs to evaluate and optimize partner performance. Collaborate with Product, BD, Marketing, and Data Engineering to improve pipelines, translate insights into recommendations, and mentor analysts to scale partner growth.

Contribute to building and scaling ML development infrastructure for Copilot: design and optimize CI/CD for large-scale training, build developer tools, maintain multi-cloud infrastructure, and deploy/host models for inference and data generation while collaborating with cross-functional teams to improve reliability, performance, and model development workflows.

Lead and build Adelphi's end-to-end talent acquisition function, focusing initially on sourcing and closing Forward Deployed AI Engineers for defense and intelligence customers. Own full-cycle recruiting, structured scorecard-based interviewing, candidate experience, offer negotiation, recruiting infrastructure, and vendor/tool management while partnering with executive leadership.

Early-to-mid career Product Manager to translate customer needs into product requirements for AI-driven scientific SaaS. Work cross-functionally with engineering, AI/ML, solutions, and commercial teams to prioritize backlog, run discovery, support launches, gather customer feedback, and define product metrics for customers in chemistry, manufacturing, and energy.

Embed with customers to discover needs, design and implement production-grade integrations (APIs, data pipelines, event-driven workflows), deploy and troubleshoot solutions, train IT teams, and drive adoption and measurable business outcomes.

Own strategy and execution to expand Pagaya's auto asset class: drive end-to-end enterprise sales to OEMs, banks, and dealers; close complex deals; build C-level relationships; coordinate across Risk, Tech, Product, Legal, and Data; monitor market trends; and evangelize Pagaya externally.

Lead strategy and execution to prevent, detect, and disrupt digital fraud, scams, and money mule activity. Build and mentor data science and analytics teams, develop ML predictive models, partner with product, cyber, vendors, and risk teams, and drive prevention-first controls, compliance, and operational efficiencies across payments, onboarding, and authentication channels.

Design, implement, and operationalize fraud detection and prevention across authentication, trading, crypto, and admin workflows. Own telemetry, investigate incidents using logs and data warehouses, build detection models and scoring systems, tune alerting/triage workflows, and translate findings into playbooks and engineering requirements to reduce fraud and strengthen controls.

The role involves applying established ML techniques to cybersecurity data problems, integrating ML outputs into data pipelines, and ensuring model reliability. Collaboration with engineers and product teams is key, along with strong communication skills and understanding of data flow.

Build and deploy machine learning models on large healthcare datasets, extract actionable insights, develop scalable data processing systems, collaborate with cross-functional teams, and communicate findings to stakeholders to improve patient outcomes.

Lead Pearly's Data & Analytics product: own customer-facing dashboards, internal insights, data architecture, predictive/AI features, and new data products. Drive discovery with finance and RCM leaders, partner cross-functionally, and translate data into monetizable, decision-grade products and roadmaps.