Top Machine Learning Jobs in Indianapolis, IN

Designs secure, scalable cloud-enabled architectures for AI-assisted modernization of legacy Marine Corps systems. Advises customers and technical teams, recommends AI/ML, data, cloud, and DevSecOps patterns, manages technical risks, and builds hybrid deterministic-agentic pipelines using LangChain and LlamaIndex. The role also fine-tunes large language models, integrates code-as-data techniques such as ASTs and control/data flow graphs, and develops production AI systems using Java, Python, APIs, and Databricks.

Develop and deploy reliable machine learning, LLM-powered agent, and agentic workflow systems for government and enterprise customers. Integrate models with tools, APIs, MCP servers, and data sources; build evaluation frameworks for performance, reliability, and safety; improve Chariot, the company’s AI operations platform; and support mission-critical deployments. Collaborate with data scientists, software engineers, DevOps teams, and customers in secure government environments.

Develop and deploy reliable machine learning, LLM, agentic, and data-centric systems for government and enterprise customers. Build and evaluate AI agents, workflows, tool integrations, and retrieval-augmented systems; measure performance, reliability, and safety; and improve the Chariot AI operations platform. Collaborate with customers, data scientists, software engineers, and DevOps teams on mission-critical deployments. The role is fully remote or hybrid in Austin, with travel up to 20%.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Own the strategic vision and roadmap for Coursera’s AI-driven search experience. Lead conversational search, personalization, semantic and multilingual search, ranking, and discovery improvements. Partner with engineering, design, research, data science, ML, marketing, and partnerships teams to deliver scalable platform capabilities. Use experimentation, market analysis, KPIs, and customer insights to improve conversion, merchandising, learner experience, and revenue.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Leads Lilly’s enterprise strategy, architecture, and deployment of agentic AI across pharmaceutical CMC development. Builds an autonomous development platform using LLMs, multi-agent orchestration, retrieval systems, knowledge graphs, digital twins, and workflow automation. Recruits and leads a multidisciplinary technical organization, partners with scientific, manufacturing, quality, regulatory, and corporate technology teams, owns ROI and adoption metrics, and drives responsible AI governance in regulated environments. The role also represents Lilly externally through industry, regulatory, academic, and technology partnerships.

Develop and deploy AI, machine learning, statistical, and adaptive process-control models for bioprocess optimization. Analyze sensor, batch, and experimental data; improve fermentation and downstream processing productivity, yield, cost, and scalability. Collaborate with engineers and scientists to integrate data science solutions into manufacturing and R&D operations, enhance data pipelines and modeling frameworks, and communicate findings to technical and non-technical stakeholders.

Develops machine learning and deep learning models for biologicals discovery in crop health. The role integrates genomic, metabolomic, and phenotypic data to prioritize microbial and natural product candidates, applies modern AI methods, and partners with scientists across biology, chemistry, genomics, and data engineering. Responsibilities include framing research problems, defining evaluation strategies, interpreting model outputs, generating biological hypotheses, and advancing predictive solutions through integrated discovery pipelines.

Drive enterprise revenue growth for security-for-AI products across the Southeast. Develop strategic sales plans, build executive relationships, lead complex negotiations, create customized proposals and demonstrations, manage pipeline and forecasts in CRM systems, and represent the company at industry events. The role requires deep cybersecurity and AI understanding, strong communication and negotiation skills, and at least seven years of enterprise sales experience.

Drive enterprise revenue growth for security-for-AI products across the Pacific Northwest. Develop strategic sales plans, build executive relationships, lead complex negotiations, create customized proposals and demonstrations, manage pipeline and forecasts in CRM tools, and represent the company at industry events. Requires cybersecurity or AI enterprise sales experience, strong knowledge of AI security concepts, and excellent communication, presentation, and negotiation skills.

Design, optimize, and deploy machine learning models for resource-constrained edge devices, including mobile and embedded platforms. Responsibilities include model compression, quantization, pruning, hardware-aware optimization, performance profiling, production deployment, and systems engineering. The role also addresses on-device privacy, security, personalization, and collaboration across engineering teams.

Design and build maintainable data pipelines, ETL processes, and data architecture standards. Collaborate with product managers, analysts, and machine learning engineers to translate requirements into automated, scalable solutions. Apply software engineering best practices, asynchronous systems, APIs, streaming, testing, monitoring, and Python and SQL expertise. Help prioritize data initiatives based on business impact and communicate complex technical concepts to technical and non-technical stakeholders.

Lead the strategy, roadmap, and execution for Salesforce’s Implementation Health capabilities. Define health frameworks, metrics, data architecture, ML-powered insights, dashboards, and customer success workflows. Partner with Product, Engineering, Data Science, Sales, Marketing, and Customer Success leaders to deliver scalable solutions, influence executive stakeholders, improve customer outcomes, and support retention and expansion across Salesforce’s cloud portfolio.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Serve as a technical leader supporting sales across enterprise, service provider, and public sector customers. Evaluate F5 solutions, explain technical and business trade-offs, conduct demonstrations and presentations, and collaborate with Sales, Marketing, Product Management, and Development. Provide expertise in application delivery, security, cloud, Kubernetes, containers, Linux, NGINX, APIs, AI infrastructure, and automation. Act as a technical evangelist, publish educational content, mentor solution engineering teams, and support strategic opportunities and product launches.

Designs, engineers, tunes, and maintains security detections, correlation rules, behavioral analytics, and CI/CD pipelines for detection content. Supports network security, access management, incident response, risk assessments, security architecture, and complex troubleshooting. Leads incident recovery, root cause analysis, testing, technical escalations, vendor coordination, and collaboration with security operations, fraud, threat intelligence, infrastructure, and engineering teams.

Lead the design and delivery of knowledge graph and machine learning systems that infer personal work context from connected tools. Build graph inference pipelines, schemas, permission-aware APIs, Rovo Chat integrations, and MCP-compatible CLI experiences. Establish evaluation frameworks, improve retrieval and grounding, provide technical direction across teams, mentor engineers, and ensure responsible, privacy-safe AI systems operate reliably at production scale.

Leads software engineering teams and owns technical strategy, architecture, roadmaps, budgets, staffing, and operational health across application or platform domains. Guides development of scalable web and cloud solutions, engineering standards, AI-enabled capabilities, and modernization initiatives. Partners with product, business, medical, regulatory, and executive stakeholders while developing technical leaders, managing vendors, improving delivery processes, and ensuring security, privacy, reliability, and regulatory compliance.

Own the design, adaptation, integration, release, and production rollout of tailored AI solutions for a large client. Collaborate with distributed engineers, contribute client-specific changes to a shared codebase, ensure reliability in a regulated environment, serve as the client’s technical contact, and resolve complex production issues independently.

Leads and develops a data science team applying statistics, machine learning, causal inference, and AI to business problems. Establishes standards for experimentation, model governance, validation, responsible AI, and reproducibility. Partners with business, engineering, BI, and IT stakeholders to prioritize analytics initiatives, translate business questions into solutions, deploy models into production, and communicate actionable insights to executives and nontechnical audiences. Oversees career development, stakeholder adoption, and strategic alignment.

Lead enterprise security operations, resilience, incident response, threat intelligence, cloud security, IAM, and risk management. Serve as deputy to the CISO and primary incident commander while advising executives on cyber risk. Build and scale multidisciplinary security teams, modernize cloud-native defenses, embed security-by-design across products and AI/ML systems, and establish metrics-driven governance, automation, regulatory alignment, and business-focused security strategies.

Architects and delivers enterprise data, analytics, machine learning, generative AI, and agentic AI products for pharmaceutical manufacturing and quality operations. Leads technical product pods, defines scalable data architectures and pipelines, guides engineering and data science teams, and advances solutions from prototypes to governed production systems. Applies quality assurance, GxP, validation, cybersecurity, data integrity, and responsible AI principles to regulated workflows including CAPA, deviations, audits, inspection readiness, and quality risk management. Partners with business and technology leaders to drive reusable, secure, and measurable digital capabilities.

Teaches undergraduate and graduate artificial intelligence courses in an online and blended format. Develops syllabi, lesson plans, course materials, assessments, and student feedback aligned with university objectives. Uses online technologies and course software to facilitate learning, integrates responsible AI practices, and evaluates appropriate student use of AI tools. Requires a doctorate, applied AI experience, AI-enabled software development expertise, strong communication skills, and commitment to ongoing faculty development.

Senior Product Designer responsible for creating high-craft, 0-to-1 AI recruiting and talent product experiences. The role involves visual and interaction design, rapid iteration, prototyping, design-system development, production-ready delivery, and experimentation with generative AI. Candidates should have strong Figma expertise, exceptional visual design skills, experience shipping SaaS, enterprise, or AI products, and a portfolio demonstrating creative range and execution.