Top Machine Learning Jobs in Atlanta, GA

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.

Design, develop, optimize, and deploy scalable computer vision and vision-language systems using classical computer vision and deep learning. Build evaluation frameworks, monitor production models, debug issues, and improve architectures with new data. Collaborate with product managers and cross-functional teams to deliver practical AI solutions across cloud and edge environments.

Leads an enterprise Account Executive team across a U.S. region, owning pipeline, revenue, forecasting, account planning, sales processes, onboarding, coaching, and growth targets. Supports strategic customer deals, partners with marketing on account-based campaigns, builds utility-industry relationships, and represents Neara at customer sites and industry events. The role also develops user groups and customer feedback loops to inform product direction.

Leads enterprise AI and intelligent automation strategy, including Generative AI, machine learning, and RPA initiatives. Manages a multidisciplinary IT team, budgets, vendors, governance, project portfolios, and end-to-end solution delivery. Partners with business and technology leaders to identify automation opportunities, develop business cases, ensure compliance and security, and deliver scalable solutions with measurable business value. The role is hybrid in Camden, New Jersey, with 15% domestic and international travel.

Leads the end-to-end design, deployment, and performance of real-time ML prediction and bid-optimization systems for programmatic advertising. Provides hands-on technical leadership to applied scientists and ML engineers, formulates optimization problems, manages production ML challenges such as latency, delayed feedback, drift, and retraining, and aligns technical roadmaps with business priorities.

Leads enterprise AI, machine learning, data architecture, business intelligence, and advanced analytics initiatives. Designs scalable data platforms, models, governance frameworks, and distributed processing architectures while integrating diverse data sources. Provides technical strategy, evaluates emerging technologies, ensures security and data quality, and mentors architecture, engineering, and analytics teams. Partners with business and technical stakeholders to deliver secure, scalable solutions aligned with organizational objectives.

Develop statistical and machine learning models using soccer tracking and event data to support player recruitment decisions. Build spatio-temporal models, metrics, analyses, and interpretation tools; validate models; communicate findings to professional club clients; and partner with engineering on cloud deployment and production reliability. The role also includes code review, model maintenance, and advancing analytical methods.

Provides technical leadership for designing, building, and scaling distributed software systems supporting a virtual healthcare platform. Responsibilities include writing high-quality code, guiding architecture, developing AI-powered clinician tools and agents, mentoring engineers, and partnering with product and clinical leaders on technical strategy. Requires extensive software engineering experience, database and systems expertise, modern frontend framework experience, and proficiency in one or more programming languages.

Lead research and development for Harris School Solutions’ K12 products, managing 40+ onsite, offshore, and remote employees. Set product and technical roadmaps, scale AI-assisted development, deliver AI-enabled capabilities, oversee cloud and Microsoft-stack engineering, and establish responsible AI governance. Manage budgets, P&L considerations, portfolio delivery, hiring, coaching, performance, and executive communication while improving development productivity, quality, and operational efficiency.

Leads enterprise product evaluations, implementations, integrations, and adoption initiatives. Manages cross-functional projects involving AI, automation, system modernization, and third-party platforms. Translates business requirements into implementation plans, develops business cases and ROI analyses, coordinates vendors and stakeholders, prepares executive communications, assesses risks, and supports organizational change and user adoption.

Owns the intelligence platform roadmap for enterprise healthcare engagement, including decisioning, orchestration, integrations, interoperability, identity and consent management, and scalable deployment. The role defines strategy and requirements, prioritizes platform investments, leads partner integrations, establishes success metrics, and coordinates Engineering, AI/ML, Data Science, Security, Design, and client teams. It also embeds healthcare compliance, security, and responsible AI throughout the product lifecycle.

Lead phData’s life sciences commercial strategy and solutions business across market access, pricing, patient services, commercial operations, omnichannel, payer analytics, and gross-to-net. Develop go-to-market offerings, accelerators, proposals, strategic pursuits, thought leadership, and account expansion opportunities. Advise executive clients while partnering with data, engineering, machine learning, and platform teams to create scalable, technically feasible solutions.

Provides strategic finance leadership to venture-backed AI and SaaS startups. Responsibilities include monthly close oversight, budgeting and forecasting, capital planning, treasury management, board reporting, investor updates, fundraising, M&A support, cap table analysis, revenue cycle management, cash flow planning, controls, audit readiness, and advising founders on financial strategy.

Build edge-focused full-stack product experiences integrating machine learning and autonomy for frontline defense users. Collaborate with designers, infrastructure teams, product teams, and ML engineers to create intuitive applications, efficient edge-deployed application stacks, and internal modeling APIs. Develop maintainable software using strong engineering practices, including design documents, code reviews, and style guides.

Own end-to-end product design for a two-sided affordable housing marketplace, from research and strategy through prototyping, launch, and iteration. Simplify complex workflows, conduct user research, collaborate cross-functionally, design polished Figma interfaces, run experiments, and create AI-powered experiences. Connect design decisions to customer needs, business metrics, and marketplace outcomes.

Leads enterprise product and technology integrations, cross-functional projects, AI and automation initiatives, business analysis, vendor coordination, executive communications, and change adoption. Develops requirements, implementation plans, business cases, risk strategies, and executive reporting while ensuring initiatives meet scope, schedule, compliance, and organizational objectives.

Leads the credit risk data science function across underwriting, fraud, loss forecasting, profitability, and portfolio analytics. Owns the model development roadmap and lifecycle, including development, deployment, monitoring, governance, validation, and regulatory readiness. Manages and mentors data science talent, establishes technical standards, partners with cross-functional teams, and translates business objectives into scalable analytical solutions. Guides responsible AI adoption and communicates model performance, risks, and recommendations to senior leadership.

Lead the data science function, owning machine learning strategy, predictive models, healthcare analytics, DBT data marts, and executive dashboards. Analyze claims, eligibility, pharmacy, EMR, HIE, clinical, and operational data to identify high-risk patients, evaluate clinical interventions, and improve patient and provider engagement using AI and LLMs. Collaborate with clinical, operations, and technology teams while leading data science staff in a fast-growing healthcare organization.

Lead design, development, and deployment of AI/ML models (forecasting, optimization, RL, generative AI) for pricing and promotion. Extract and integrate complex data, monitor model performance, establish AI engineering patterns, document code, and communicate results to technical and non-technical stakeholders to operationalize models across the supply chain.

Manage client relationships throughout implementation and ongoing support, serving as the primary client contact. Oversee KPIs, operational metrics, strategic planning, project execution, and client goal attainment. Translate client needs into platform opportunities, represent customer feedback to Product and Engineering, and develop case studies and success materials. The role requires healthcare client success or account management experience, strong communication, project leadership, and knowledge of medication management and healthcare quality measures.

Own end-to-end client relationships for a book of B2B SaaS customers: drive adoption, engagement, retention, and expansion of AI-powered contract drafting tools. Lead onboarding, training, large-scale rollouts, and translate customer feedback into product improvements while designing scalable CS processes and tracking health metrics to demonstrate value.

Design and operate large-scale, AI-native cloud infrastructure (especially Kubernetes/AKS). Improve service quality, automate incident prevention/remediation, accelerate engineering productivity, and integrate AI tools into development workflows.

Leads the strategy, roadmap, and execution of search, ranking, matching, and recommendation products for a two-sided marketplace. Oversees algorithmic performance, resource allocation, experimentation, metrics, executive communication, and cross-functional alignment across consumer and enterprise experiences. Coaches product managers and establishes scalable operating rituals while connecting technical improvements to engagement, retention, customer value, and marketplace growth.

Own customer-facing AI and data engagements from discovery through production launch. Define business value, MVPs, success criteria, and product requirements; prioritize engineering backlogs; lead technical discovery, architecture reviews, agile delivery, testing, production readiness, launch, adoption, and strategic handoff. Build C-suite relationships, translate business needs into technical requirements, resolve delivery risks, and identify future high-value opportunities.

Lead Cure from company formation through product-market validation and seed financing. Define the market wedge, secure enterprise design partners, shape an AI-native recovery product, establish data and evaluation loops, recruit the founding team, build the company narrative, and lead fundraising. The role requires enterprise AI judgment, commercial credibility, consumer-finance and regulatory understanding, and founder-level ability to create a new category.