Top Machine Learning Jobs in Indianapolis, IN

Lead assessment, redesign, and hands-on implementation of data pipelines, ML workflows, and platform infrastructure. Produce prioritized system designs, harden production pipelines, improve observability, reliability, and data quality, and mentor the data team.

Develops AI/ML prototypes, APIs, microservices, integrations, and lightweight user interfaces for federal and DoD-aligned mission workflows. The role integrates and optimizes machine learning models for dashboards and applications, supports data ingestion and preprocessing, validates performance, and helps transition models into production. Collaboration with data scientists, architects, developers, PMO leadership, and cybersecurity teams is required. The position requires five years of relevant experience, a bachelor’s degree, ITIL v4 Foundation certification, and Public Trust or active Secret clearance.

Designs AI/ML pilots, predictive analytics and anomaly detection models, behavioral analytics, content classification, personalization algorithms, and end-to-end machine learning pipelines. Develops prototypes and production-ready solutions, evaluates AI technologies, supports experimentation, integrates ML insights into dashboards, and ensures model governance, explainability, bias mitigation, accuracy, and security compliance in federal and DoD-aligned environments.

Lead go-to-market and partner development for Personal Loans and Point-of-Sale products. Own end-to-end enterprise sales, build C-level relationships, close complex deals, and coordinate cross-functional teams to launch AI-driven consumer lending integrations and scale market presence.

Develop, deploy, and optimize scalable machine learning solutions, including predictive models, deep learning systems, data pipelines, and model-serving infrastructure. Monitor model performance, manage retraining and lifecycle processes, ensure quality and compliance, and support responsible AI practices. Collaborate with data scientists, engineers, product teams, and business stakeholders while researching emerging AI technologies and improving AI architectures.

Leads the architecture, development, deployment, and optimization of enterprise machine learning solutions. Responsibilities include designing scalable ML systems, developing predictive models, supporting MLOps and model lifecycle management, improving data quality and feature engineering, implementing governance and validation frameworks, and mentoring technical teams. The role also contributes to AI strategy, innovation initiatives, responsible AI practices, and organizational modernization.

Define and execute the product roadmap for AI-driven on-chain monitoring tools to detect blockchain threats. Collaborate with engineering, research, and stakeholders, analyze market and customer feedback, prioritize features, ensure timely delivery, and maintain transparent communication across teams.

Lead a global manufacturing engineering organization to standardize processes, drive lean continuous improvement, deploy automation and Industry 4.0 solutions (cobots, ML), and ensure safety, traceability, and zero-defect customer quality across international sites.

Partner with a retained search firm to place software engineers and quant developers at top hedge funds. Provide candidate flow from your network; firm supplies clients, process, and fees. Remote, commission-only role with placement and pipeline incentives. Apply with placement examples, outreach metrics, and a candidate submission.

Lead technical strategy and hands-on engineering for Airbnb's AI-powered ads growth engine. Architect scalable systems for creative generation, bidding, budget optimization, and audience management across paid channels. Write and review code, set standards for availability and quality, collaborate with Marketing/Product/ML teams, roll out staged automation, and mentor engineers to advance platform autonomy while ensuring brand safety and human-in-the-loop controls.

Build, ship, and own end-to-end ML models for vendor matching, order routing, and supply-chain optimization. Partner with data engineering to productionize models, quantify impact, document work, and implement decision logic components toward agentic AI in a fast-moving healthcare startup environment.

Lead and scale a strategic sales channel for an LTL logistics company, building high-performance sales teams, aligning commercial strategy with operations, driving growth through CRM and Power BI analytics, and executing aggressive performance and travel-heavy sales plans.

The role entails positioning and launching products, developing content for B2B markets, collaborating with sales, and driving vertical strategies in AI and computer vision.

The Staff Product Manager will define product strategy, lead the product lifecycle, collaborate across teams, and drive innovation in claims processing software.

Own RevOps data models and BI infrastructure to drive GTM strategy. Clean and analyze datasets, build predictive models, define and track leading/lagging KPIs, deploy dashboards, document data definitions, and deliver executive-ready insights while partnering cross-functionally with Finance and GTM teams.

The Director of System Safety oversees safety functions for autonomous vehicles, managing safety cases, deployment plans, and regulatory relationships while leading a multi-disciplinary safety team.

Lead development of time-series and econometric models (ARIMA, VAR, state-space, WLS, panel data) and Bayesian probabilistic frameworks. Use causal inference and stochastic processes to produce actionable insights, work with large datasets on Databricks, communicate results to stakeholders, and mentor junior team members.

Own a product area of the Content Authoring Platform end-to-end: set vision and roadmap, run continuous discovery, define and measure outcomes, partner with engineering and design to deliver features, evaluate and apply AI capabilities prudently, mentor peers, and communicate strategy and trade-offs to stakeholders.

Lead client-facing full-stack Java development, mentor teams, design solutions, promote engineering best practices (Agile, DevOps, CI/CD, TDD), and deliver enterprise software using modern cloud, container, and JS frameworks.

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.

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.

Administer enterprise AI governance framework, coordinate intake/assessment of AI use cases, maintain AI inventory, support model risk oversight, develop reporting/dashboards, interpret regulatory expectations, and coordinate exams and audits to ensure compliant, risk-based AI deployment.

Lead end-to-end development of AI and machine learning solutions for a medical claims synthesis product. Design and deploy LLM-driven features, hybrid statistical ML and RAG systems, document extraction, and human-in-the-loop summarization workflows. Make architecture decisions, optimize production systems, evaluate model performance, collaborate with SMEs and product teams, and mentor engineers. Translate emerging AI research into reliable, scalable production applications.

Design and implement scalable backend and distributed systems for a marketing intelligence platform. Build near-real-time ingestion and processing, scale an incrementality/experimentation product, integrate AI-powered workflows, improve data ingestion/exports and connection-state systems, and contribute frontend components to deliver end-to-end product experiences.

Lead a Big Data Engineering team to deliver production-grade ETL and data pipelines at scale. Own team execution, code quality, morale, and roadmap execution with product managers. Remove roadblocks, coach and develop engineers, promote engineering best practices, and enable data-driven and AI-powered product features (personalization, recommendations, search, ranking, matching).