Top AI & Machine Learning Jobs

Own machine learning at Metriport end to end, including problem framing, feature engineering, model training, evaluation, deployment, monitoring, and infrastructure. Build predictive models and document-processing systems from large, messy clinical datasets. Develop training and inference pipelines, experiment tracking, model versioning, and production monitoring. Collaborate with founders, engineers, and customers to identify high-value ML opportunities and deliver reliable healthcare intelligence.

Entry-level associate supporting AI and automation initiatives through workflow configuration, data preparation, testing, performance monitoring, documentation, and process improvement. The role provides exposure to AI solution development, validation, deployment, governance, risk management, data security, and compliance. Associates will identify issues, maintain process documentation, support audits and internal controls, and follow enterprise policies while developing foundational skills in artificial intelligence, automation, and process optimization.

Develop and validate machine learning and computer vision models using Python and PyTorch. Work with CNNs, Vision Transformers, image classification and detection, metric learning, self-supervised learning, model calibration, uncertainty detection, and MLOps. Conduct experimentation, ensure data quality, and work with large, messy, domain-specific datasets in an onsite three-year contract role.

Train with engineering, product, and client teams before embedding in client engagements. Discover workflows through SME interviews, document review, and system mapping; identify automation opportunities; write specifications; build and test AI-agent prototypes; lead rollouts; and measure productivity and ROI. This early-career role offers development paths toward AI engineering, product and implementation, or engagement leadership.

Build and deploy computer vision and machine learning solutions from research prototypes into scalable production services. Develop MLOps pipelines for data preparation, training, validation, versioning, deployment, and monitoring. Train and optimize deep learning models, integrate them into secure cloud systems, and support national security missions. Collaborate with mission partners, engineers, and domain experts while applying containerization, DevSecOps, model governance, and inference optimization practices.

Develop and govern MBSE and SysML digital models for defense and space mission systems while applying Python, SQL, statistics, visualization, and machine learning to engineering and operational data. Establish digital-thread traceability, data architectures, requirements relationships, technical baselines, and explainable analytics. Support trade studies, capability assessments, AI/ML evaluation, architecture decisions, Agile and DevSecOps delivery, and collaboration with systems engineers, architects, operators, cybersecurity teams, and government stakeholders.

Designs, builds, and evaluates language models for AI security products. Responsibilities include dataset curation, preprocessing, LLM experimentation, safety and robustness evaluation, dashboards, knowledge graphs, model fine-tuning, production integration, monitoring, CI/CD, and ML tooling. Collaborates with researchers and data scientists to automate quality assurance and guardrails while delivering reliable, scalable, and interpretable models.

Conduct postdoctoral research on AI agent-enabled autonomous experimentation for bio-inspired materials discovery. Develop trustworthy planning, reasoning, navigation, reinforcement learning, imitation learning, control, and probabilistic algorithms for single- and multi-agent systems. Build robotic laboratory workflows integrating liquid handlers, analytical instruments, cloud infrastructure, and AI agents. Publish research, collaborate with academic and industry partners, and mentor students and research staff.

Build and operationalize Generative AI data pipelines and scalable APIs using Palantir Foundry. Collaborate with architects, engineers, analytics, application development, and data science teams to deliver reliable data solutions. Responsibilities include designing data platforms, optimizing processing workflows, implementing containerized parallel execution, maintaining code quality, participating in reviews, and supporting CI/CD, monitoring, testing, and troubleshooting.

Lead product direction for cloud-native GPU infrastructure and Kubernetes virtualization. Translate business priorities into technical specs, run alpha/beta validations, engage platform-engineer customers, define market positioning, and align engineering, sales, and marketing to ship and support scalable multi-tenant GPU platforms.