AI Engineer

• De sign, de velop, and fine-tune a variety of AI models
• Design autonomous agents and multi-step pipelines using LangChain, ReAct, tool-calling, or custom orchestration
• Build Retrieval-Augmented Generation pipelines that combine external knowledge bases with LLMs to improve factual accuracy for warfighting applications
• Implement end-to-end data pipelines, ETL processes, and back-end services (Python, C/C++, Java) that feed data to models
• Create CI/CD pipelines for model training, validation, containerized deployment (Docker/Kubernetes), and security scanning
• Maintain model registries, monitoring, and version control of context protocols
• Produce rapid prototypes, run benchmarks, and conduct robustness/adversarial testing in realistic environments
• Work closely with senior ML engineers, software developers, and government customers; mentor junior staff and contribute to design reviews and documentation
• Stay current with emerging LLM architectures, agentic paradigms, PEFT/LoRA methods, and AI-safety techniques; translate new research into operational capabilities

AI Engineer – Mission Innovation Lab

• Ship and debug code on a live, real-time voice pipeline
• Design control systems around LLMs: guardrails, budgets, watchdogs, safe fallbacks
• Build and operate LLM evaluation and batch-analysis pipelines
• Own traditional ML workflows from data to scheduled production inference
• Trace production issues from a metric anomaly to root cause

Senior Software Engineer – Applied AI

• Own stakeholder relationships across Medbridge’s executive team, working with them to find the problems and AI enabled solutions that can expedite, or remove, the current blockers in the business.
• Design, build, and ship AI-powered features across our internal teams, from the user interface to the backend services and AI workflows behind it.
• Build full-stack applications using AI-native engineering workflows and tools such as Claude, Cursor, Codex, and OpenAI and Anthropic APIs.
• Develop APIs, backend services, and automation pipelines that support intelligent product capabilities and production-ready deployments.
• Apply software engineering, DevOps, and MLOps best practices to the testing, deployment, monitoring, evaluation, and optimization of AI solutions.
• Partner closely with our Product and Engineering teams to turn ambiguous goals into scalable internal products.
• Use AI tooling throughout your workflow (coding, code review, testing, and planning) and help push the team’s adoption forward.
• Research emerging AI technologies, LLM implementation patterns, and modern product engineering frameworks, and bring what works back to the team.
• Instrument what you ship and use usage data and evidence to decide what to build, fix, or kill next.

Product Engineer – AI

AI Research Scientist

• Contribute to the research, design, and development of large-scale foundation models for machine-generated data
• Develop and enhance distributed training and inference workflows
• Collaborate with engineering, product, and data science teams to understand requirements
• Share emerging ideas, technical insights, and best practices with teammates
• Take ownership of assigned projects and deliver high-quality results

Applied AI Scientist

• Provide governance, oversight, and controlled expansion of internal AI tool
• Execute quarterly, risk-based testing program for independent credible challenge
• Collaborate with various stakeholders to review AI initiatives and escalate risks
• Evaluate controls across the AI lifecycle
• Translate regulatory requirements into AI-focused testing procedures
• Develop credible challenge documentation and reporting for governance committees
• Enhance AI testing frameworks and tools

Lead AI Compliance Testing

Machine Learning Engineer

• Own technical direction of the ML Platform — feature store, model hosting and serving, experimentation, training infrastructure — driving architectural decisions around scalability, reliability, latency, and cost
• Lead design and delivery of large-scope platform initiatives from conception through production, coordinating across ML, data, and infrastructure teams
• Identify and resolve systemic technical challenges: online/offline feature parity, model deployment friction, experimentation velocity, GPU utilization, cross-team dependencies
• Set and maintain a high engineering quality bar through hands-on code contributions, design reviews, and mentorship of platform and ML-adjacent engineers
• Partner with ML engineering, data science, product, and platform leadership to translate ML strategy into technical roadmaps
• Define the paved paths ML teams use to ship models safely — from feature registration through canary rollout, monitoring, and rollback
• Leverage AI-augmented development tools to increase development velocity and code quality

Staff Machine Learning Engineer

• Design, develop, and deploy machine learning models and AI-powered applications
• Collaborate with cross-functional teams to identify and implement AI/ML solutions for client needs
• Build and maintain data pipelines, model training workflows, and inference systems
• Evaluate and integrate large language models (LLMs), generative AI tools, and emerging AI frameworks
• Monitor model performance, conduct testing, and iterate on improvements
• Document technical architectures, methodologies, and best practices
• Stay current with AI/ML research and translate advancements into practical applications

• Characterize and integrate biomedical data modalities — digital pathology (whole slide images), neuroimaging (PET, structural and functional MRI), omics (genomics, transcriptomics, proteomics, metabolomics), and longitudinal clinical data to develop specialized, domain-specific models for neurodegeneration.
• Build and refine data engineering pipelines that harmonize heterogeneous modalities — reconciling differences in spatial resolution, temporal scale, and dimensionality — into unified analytical frameworks.
• Identify where cross-modal integration produces genuine insight versus where it introduces noise or artifact, establishing ground truth for downstream AI evaluation.
• Critically assess AI-driven literature synthesis and automated “third reviewer” capabilities for detecting methodological weaknesses, logical gaps, and unsupported claims across data modalities.
• Establish standards for how agentic systems incorporate overlooked or contradictory evidence, such as negative findings, failed clinical trials, etc. and evaluate whether these integrations generate genuinely novel hypotheses.
• Design evaluation frameworks for agentic AI systems operating across neuroscience data modalities — assessing whether models can reason credibly across imaging, omics, and clinical evidence.
• Develop benchmarks using synthetic and real-world multi-modal datasets that probe AI co-scientist capabilities under realistic research conditions, testing for robustness, reproducibility, and alignment with expert-level biomedical reasoning.
• Serve as a neurodegeneration domain expert within the AI/ML team, ensuring that model outputs remain anchored to clinically relevant disease questions.
• Translate evaluation findings into actionable guidance for AI system development, bridging computational and experimental perspectives.
• Publish evaluation methodologies and findings in leading journals and conferences (e.g., AD/PD, AAIC, NeurIPS).
• Articulate emerging AI/ML approaches — causal reasoning, intent classification, agentic planning — to diverse audiences with clear framing of practical applications in drug discovery.
• Co-author manuscripts, concept papers, and translational strategy documents.

Translational Postdoctoral Researcher – Agentic AI for Neurodegeneration

• Characterize and integrate biomedical data modalities — digital pathology (whole slide images), neuroimaging (PET, structural and functional MRI), omics (genomics, transcriptomics, proteomics, metabolomics), and longitudinal clinical data to develop specialized, domain-specific models for neurodegeneration
• Build and refine data engineering pipelines that harmonize heterogeneous modalities — reconciling differences in spatial resolution, temporal scale, and dimensionality — into unified analytical frameworks
• Identify where cross-modal integration produces genuine insight versus where it introduces noise or artifact, establishing ground truth for downstream AI evaluation
• Critically assess AI-driven literature synthesis and automated “third reviewer” capabilities for detecting methodological weaknesses, logical gaps, and unsupported claims across data modalities
• Establish standards for how agentic systems incorporate overlooked or contradictory evidence such as negative findings, failed clinical trials, etc. and evaluate whether these integrations generate genuinely novel hypotheses
• Design evaluation frameworks for agentic AI systems operating across neuroscience data modalities — assessing whether models can reason credibly across imaging, omics, and clinical evidence
• Develop benchmarks using synthetic and real-world multi-modal datasets that probe AI co-scientist capabilities under realistic research conditions, testing for robustness, reproducibility, and alignment with expert-level biomedical reasoning
• Serve as a neurodegeneration domain expert within the AI/ML team, ensuring that model outputs remain anchored to clinically relevant disease questions
• Translate evaluation findings into actionable guidance for AI system development, bridging computational and experimental perspectives
• Publish evaluation methodologies and findings in leading journals and conferences (e.g., AD/PD, AAIC, NeurIPS)
• Articulate emerging AI/ML approaches — causal reasoning, intent classification, agentic planning — to diverse audiences with clear framing of practical applications in drug discovery
• Co-author manuscripts, concept papers, and translational strategy documents

• Design, train, and deploy robust policies for locomotion, manipulation, and dynamic interactions with the environment.
• Develop core reinforcement learning infrastructure, including scalable training pipelines and evaluation frameworks.
• Design and implement new simulation environments and tasks to support training and deployment of control policies.
• Develop, design, and test imitation learning methods
• Collaborate with Robotics Software and AI engineering teams to develop policies which can be transferred to production

Senior AI Research Engineer

• Build and maintain shared infrastructure: secure web app hosting infrastructure, MCP/API standards, secret/key management, model selection, guardrails, and a centralized pattern library that all builders across the organization can pull from.
• Own the review, tracking, and advisory process for all corporate AI and automation builds — MCP servers and tool configurations, Claude Skills, API integrations, and agentic workflows — before deployment.
• Partner directly with AI specialists across departments to identify, scope, and build AI and automation solutions that meet high standards for cost, security, and scalability security before they ship.
• Define and maintain the secure intake path for AI and automation requests across the organization, replacing ad-hoc builds and shadow IT with a governed, cost-effective, and scalable process.
• Contribute to the organization’s AI cost efficiency by monitoring, alerting, and setting thresholds on AI spend across usage-based tools and platforms.

AI & Automations Lead

• Lead the development and implementation of quality management frameworks across Supply Chain Technology systems, ensuring compliance, reliability, and performance
• Oversee digital quality programs, including system validation, testing, and continuous improvement initiatives aligned with business and regulatory requirements
• Drive the integration of AI and advanced analytics into quality systems to enhance monitoring, predictive insights, and process optimization
• Establish and monitor quality metrics, dashboards, and scorecards to evaluate system performance and identify opportunities for improvement
• Partner with cross-functional teams (IT, Supply Chain, Quality, Regulatory) to ensure alignment on quality standards and execution priorities
• Lead risk management activities, including assessment of system controls, mitigation strategies, and issue escalation processes
• Guide process improvement initiatives, leveraging methodologies such as Lean, Six Sigma, or continuous improvement practices
• Ensure adherence to regulatory and compliance standards, including auditing systems, interpreting regulations, and implementing required changes
• Manage and develop a team, providing coaching, performance feedback, and career development support
• Foster a culture of quality, innovation, and accountability aligned with organizational values and business goals.

Technology Manager, Quality Systems – AI Lead

• Lead the development and implementation of quality management frameworks across Supply Chain Technology systems, ensuring compliance, reliability, and performance
• Oversee digital quality programs, including system validation, testing, and continuous improvement initiatives aligned with business and regulatory requirements
• Drive the integration of AI and advanced analytics into quality systems to enhance monitoring, predictive insights, and process optimization
• Establish and monitor quality metrics, dashboards, and scorecards to evaluate system performance and identify opportunities for improvement
• Partner with cross-functional teams (IT, Supply Chain, Quality, Regulatory) to ensure alignment on quality standards and execution priorities
• Lead risk management activities, including assessment of system controls, mitigation strategies, and issue escalation processes
• Guide process improvement initiatives, leveraging methodologies such as Lean, Six Sigma, or continuous improvement practices
• Ensure adherence to regulatory and compliance standards, including auditing systems, interpreting regulations, and implementing required changes
• Manage and develop a team, providing coaching, performance feedback, and career development support
• Foster a culture of quality, innovation, and accountability aligned with organizational values and business goals

Technology Manager, Quality Systems, AI Lead

• Developing, implementing, and maintaining AI-driven solutions to enhance the efficiency, accuracy, and scalability of pharmaceutical packaging operations
• Collaborating with cross-functional teams, including operations, quality assurance, and regulatory compliance, to integrate AI technologies
• Designing and deploying AI/ML algorithms to mine and analyze MES and data lake data
• Building intuitive dashboards and reporting tools for actionable insights
• Developing data pipelines that integrate and normalize data across packaging lines
• Applying statistical and machine learning techniques to identify root causes of rejects and downtime
• Developing predictive maintenance algorithms to forecast failures
• Supporting equipment and OT vendors in configuring and tuning AI/ML modules
• Partnering with Quality and Regulatory teams to ensure compliance with standards
• Monitoring, retraining, and documenting deployed models for accuracy

• Architect, implement, and maintain scalable data pipelines and feature stores for batch and real-time workloads.
• Build reproducible ML training, evaluation, and inference workflows using modern orchestration and MLOps tooling.
• Integrate event streams from Twilio products (e.g., Messaging, Voice, Segment) into unified, analytics-ready datasets.
• Monitor, test, and improve data quality, model performance, latency, and cost.
• Partner with product, data science, and security teams to ship resilient, compliant services.
• Automate deployment with CI/CD, infrastructure-as-code, and container orchestration best practices.
• Produce clear documentation, dashboards, and runbooks; share knowledge through code reviews and brown-bag sessions.
• Embrace Twilio’s “We are Builders” values by taking ownership of problems and driving them to completion.

LLM Engineer

• Design, build, and maintain scalable ML infrastructure and pipelines supporting model training, deployment, monitoring, governance, and lifecycle management.
• Develop and optimize CI/CD pipelines for machine learning and AI workloads across development, staging, and production environments.
• Build reusable ML platform capabilities including feature stores, model registries, experimentation frameworks, artifact management, and deployment automation.
• Implement scalable orchestration and workflow solutions for batch and real-time ML inference workloads.
• Create robust monitoring systems to measure model performance, detect model drift, monitor data quality, and ensure production reliability.
• Develop automation tools and self-service capabilities to improve the efficiency, scalability, and reliability of MLOps processes.
• Collaborate with Data Scientists and Software Engineers to streamline the ML lifecycle from experimentation through enterprise production deployment.
• Apply software engineering best practices to AI/ML systems including testing, observability, resiliency, security, versioning, and infrastructure-as-code.
• Identify gaps and improvement opportunities within the organization’s ML platform ecosystem and architect scalable solutions to address them.
• Support enterprise AI governance, compliance, auditability, and model risk management requirements.
• Ensure platform scalability, reliability, security, and operational excellence across AI/ML systems.
• Lead the architecture, design, and deployment of enterprise Generative AI solutions leveraging LLMs, foundation models, and agentic AI systems.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases, embeddings, semantic search, reranking, and retrieval optimization strategies.
• Build scalable LLM orchestration frameworks using technologies such as LangChain, LlamaIndex, Semantic Kernel, or equivalent frameworks.
• Develop advanced prompt engineering strategies, prompt chaining, context management, and agent workflows to improve LLM accuracy and reliability.
• Evaluate and implement fine-tuning, parameter-efficient tuning, and prompt-based optimization approaches for domain-specific use cases.
• Build AI evaluation and benchmarking frameworks to measure hallucination rates, response quality, grounding accuracy, toxicity, bias, latency, and business performance metrics.
• Implement AI safety guardrails, governance controls, content filtering, and responsible AI practices for enterprise healthcare environments.
• Design scalable GenAI APIs and microservices supporting high-throughput enterprise AI applications.
• Optimize GenAI systems for cost, latency, throughput, and inference performance across cloud and hybrid environments.
• Integrate enterprise data sources, healthcare systems, and knowledge repositories into secure GenAI workflows.
• Research and evaluate emerging GenAI technologies, open-source frameworks, and foundation models to drive innovation and continuous improvement.
• Develop architecture diagrams, technical roadmaps, implementation strategies, and executive-level documentation for enterprise AI initiatives.
• Collaborate with cybersecurity, compliance, and infrastructure teams to ensure secure and compliant deployment of GenAI solutions involving PHI and sensitive healthcare data.
• Contribute to the development of AI platform standards, reusable GenAI accelerators, templates, and engineering best practices.

Senior MLOps, Generative AI Engineer

• Develop, train, and fine-tune machine learning models for multimodal sensor data (e.g., vision, LiDAR).
• Implement scalable data preprocessing and augmentation pipelines.
• Help develop embedding-based search tools and 'active learning' workflows.
• Help build and maintain dashboards to monitor model health, data drift, and system performance.
• Follow software engineering standards for ML code and participate in code reviews.
• Work closely with senior engineers and machine learning engineers to translate model prototypes into maintainable engineering solutions.

Machine Learning Engineer, Data Mining

• Design and build AI-powered applications and integrations across a variety of use cases
• Lead implementation of small- to medium-scale AI solutions, with contribution to larger enterprise initiatives
• Translate business requirements into scalable, supportable technical architectures
• Design and implement data pipelines for AI/ML use cases (ingestion, transformation, and delivery)
• Prepare and manage datasets for AI consumption, including cleansing, normalization, and feature engineering
• Ensure data quality, lineage, and readiness for model training and inference
• Apply best practices for handling sensitive data, including PII and regulated data
• Evaluate and apply appropriate AI/ML (regression models, NLP, LLMs, Multi-modal models, embedding models, etc.) approaches based on use case.
• Design and implement retrieval systems, including Retrieval-Augmented Generation (RAG)
• Apply agentic AI patterns and architectures to enable multi-step reasoning and orchestration
• Support training, fine-tuning, and optimization of models where applicable
• Implement techniques such as model distillation, quantization, and performance tuning
• Balance cost, performance, and accuracy in model selection and deployment
• Build and integrate AI capabilities into enterprise systems, APIs, and workflows
• Design scalable, secure architectures for AI services and applications
• Ensure solutions are production-ready, maintainable, and aligned to enterprise standards
• Engage with business stakeholders to gather and refine requirements
• Clearly articulate technical concepts, trade-offs, and solution approaches
• Validate requirements and translate them into actionable technical plans
• Act as a bridge between business and engineering teams

• At PNC, our people are our greatest differentiator and competitive advantage
• We work together each day to foster an inclusive workplace culture
• As a Software Engineer Senior, you will be based in Dallas, TX or Pittsburgh, PA
• PNC is seeking an AI Engineer with strong expertise in building and deploying LLM-powered, production-grade systems
• Requires end-to-end ownership across model development, optimization, and real-world integration
• Proficiency with modern inference frameworks (vLLM, TGI, Triton)
• Proven ability to build and manage pipelines across training, deployment, monitoring, and evaluation
• Design AI agents and multi-agent workflows, balancing model reasoning with deterministic business logic

Senior Software Engineer – AI Engineer

• Leading, mentoring, and growing a global team of software engineers and Forward Deployed Engineers, fostering a high-performance, artificial intelligence–first culture.
• Driving adoption of artificial intelligence across the software development lifecycle to improve engineering productivity, quality, and velocity.
• Managing team execution, delivery, hiring, performance management, and career development while ensuring operational excellence.
• Partnering with Product Management and senior engineering leaders to define and deliver scalable artificial intelligence agent platforms and enterprise capabilities.
• Designing and contributing to cloud-native solutions leveraging Microsoft Azure, Python, and C# technologies.
• Building and leading a Forward Deployed Engineering function that collaborates directly with customers to validate use cases and accelerate artificial intelligence adoption.
• Translating customer feedback and real-world implementations into reusable platform features and product enhancements.

Machine Learning Engineer jobs in Pennsylvania

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AI Engineer – Mission Innovation Lab

Senior Software Engineer – Applied AI

Product Engineer – AI

Applied AI Scientist

Lead AI Compliance Testing

Staff Machine Learning Engineer

AI Engineer

Translational Postdoctoral Researcher – Agentic AI for Neurodegeneration

Translational Postdoctoral Researcher – Agentic AI for Neurodegeneration

Senior AI Research Engineer

AI & Automations Lead

Technology Manager, Quality Systems – AI Lead

Technology Manager, Quality Systems, AI Lead

AI Engineer

Machine Learning Engineer

Senior MLOps, Generative AI Engineer

Machine Learning Engineer, Data Mining

AI Engineer

Senior Software Engineer – AI Engineer

Product Development Manager – AI Platform