Top Machine Learning Jobs in Indianapolis, IN

The Strategic Account Executive will manage key customer relationships, drive technical sales cycles, identify new use cases, and influence product direction for Vision AI solutions.

Senior presales role driving planning solution strategy for large enterprises. Lead executive discovery, design tailored demos, articulate value of Workday Adaptive Planning and AI-enabled forecasting, support sales motions (RFPs, workshops, POCs), mentor peers, and travel up to 50% to engage customers and represent Workday at events.

As an Enterprise Account Executive at Salesforce, you will drive revenue growth by leveraging AI-driven solutions, understanding client needs, and demonstrating how Agentforce can solve business challenges while fostering client relationships.

Own and execute multi-year strategic partnerships with a hyperscaler and an AI lab, aligning technical integrations, commercial motions, and joint go-to-market plans. Coordinate cross-functional teams, run operational cadences, track measurable outcomes (pipeline, ARR, integrations), and represent partnerships to executives while driving hands-on execution and follow-through.

The Staff Machine Learning Engineer will lead the development of AI solutions, ensure project delivery, and manage technical teams while interfacing with customers. Responsibilities include designing data pipelines, overseeing machine learning model development, and facilitating customer engagement.

Develop reusable ML tooling and libraries, collaborate with data scientists to productionize models, build services and APIs (Docker/Linux), follow engineering best practices, and support project lifecycle from intake to maintenance.

Work with sales and partners to solve complex customer problems, deliver value-based demos and proofs of concept, map customer architectures to Snowflake Data Architecture, support design and implementation, and help close large enterprise deals while maintaining expertise in data warehousing, ETL, analytics, and ML/generative AI.

Lead strategy and execution for Dynamic Targeting and machine learning-driven sales force capabilities. Develop DT strategy, measurement plans, and unified customer insights; translate analytics and user feedback into model, business rule, and platform optimizations to improve sales force performance and ROI. Partner across brand and portfolio teams to drive adoption and roadmap enhancements.

Lead end-to-end analyses of single-cell, spatial, and multi-omics datasets and functional genomics workstreams to prioritize therapeutic targets. Develop and apply Bayesian, causal, and ML/AI methods, build scalable pipelines and predictive models, integrate genetic evidence, and influence analytical standards and platform design while collaborating across discovery, translational, and AI teams.

Perform biomolecular NMR experiments and integrate experimental data with quantum mechanics, molecular dynamics, and free energy calculations. Develop AI/neural-network methods and automate computational workflows to model protein motion and protein-ligand interactions, communicating results and collaborating with discovery teams to advance lead molecules toward drug candidates.

Lead strategy, roadmap, and delivery for Finance data products. Partner with Finance and engineering to define requirements, drive AI-enabled analytics, ensure data quality and governance, and increase product adoption through stakeholder engagement and continuous improvement.

Design and execute molecular pharmacology assay cascades (binding, kinetic, functional, imaging) for GPCR drug discovery; interpret mechanistic data to drive SAR and program decisions; develop/validate novel assays, integrate AI/ML and automation, mentor junior scientists, and represent molecular pharmacology on cross-functional teams while ensuring safety and data integrity.

Partner with healthcare clients to design, prototype, and deploy production-grade AI/LLM solutions. Lead discovery, architecture, cloud integration, and delivery while troubleshooting distributed systems and driving measurable client outcomes.

The role focuses on developing and implementing digital strategies using AI and automation in pharmacology and drug development, managing partnerships, and ensuring compliance with industry regulations.

The Associate Director PV Systems Strategy and Implementation leads technology and analytics strategies in pharmacovigilance, enhancing data quality and compliance. They collaborate on system development, project oversight, and regulatory compliance, while promoting efficiency and operational excellence in PV processes.

Lead identification and delivery of high-impact data science solutions from concept to production using advanced analytics, machine learning, and deep learning. Collaborate with cross-functional stakeholders, prototype proofs-of-concept, and communicate insights to drive business decisions across Roche Diagnostics.

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.

Design, build, and deploy production ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM features, data pipelines, observability, MLOps, and containerized deployments across cloud and on-prem environments.

Support risk and analytics efforts by performing SQL/Python-based analysis, monitoring portfolio health, building scalable reports, collaborating cross-functionally, and integrating AI tools to surface insights that inform product, risk, and growth decisions.

Design and govern enterprise AI architectures and roadmaps, lead solution design using LLMs, RAG, vector DBs and AI agents, establish LLMOps and evaluation frameworks, ensure security/compliance and scalability, and provide technical leadership and mentorship across AI initiatives.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

Lead research and engineering of production-grade LLM/GenAI systems for cybersecurity: design models and architectures, drive data labeling and evaluation, collaborate with product and engineering to deploy scalable solutions, mentor data scientists, set technical strategy, and advance AI safety, red-teaming, and applied ML innovations for detection and operational use.

Lead development and deployment of large-scale LLM and GenAI solutions for cybersecurity. Drive research strategy, model training, evaluation, safety/red-teaming, and cross-functional productization. Mentor data scientists, set technical standards, and represent the team through publications and thought leadership.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

As an ML Ops Infrastructure Engineer, you will design CI/CD pipelines for ML, maintain deployment systems, and implement monitoring while collaborating with research teams to ensure model quality and performance.