Top Tech Jobs & Startup Jobs in Boston, MA

Build and productionize data and AI applications on Palantir Foundry for biomedical domain teams. Responsibilities include designing data pipelines, transformations, ontology models, workflow applications, integrations, access controls, data quality checks, and production monitoring. The role leads discovery and iterative delivery, partners with product, data, governance, and platform teams, supports user adoption, and creates reusable delivery patterns. Candidates need strong programming, data and application engineering, stakeholder management, and biomedical domain experience.

Provides strategic intellectual property counsel to R&D teams, develops and manages life sciences patent portfolios, prosecutes and defends patent filings, evaluates patentability, validity, infringement, and freedom to operate, and supports transactions including M&A, collaborations, and licensing. The role requires extensive expertise in U.S., PCT, European, and global patent law, advanced therapeutic technologies, and partnership with scientific, legal, and business stakeholders.

Leads strategic AI and digital transformation initiatives for Novartis executives, translating priorities into scalable cross-functional solutions and technology products. Generates insights and recommendations, serves as an AI subject matter expert, builds networks of technical leaders, and manages multidisciplinary teams. The role requires executive communication, strategic project and program management, consulting experience, and travel as business needs require.

Serve as a trusted HR advisor to leaders, managers, and employees across the full employee lifecycle. Implement people initiatives, use workforce analytics, support employee relations and organizational transformations, ensure EEO and regulatory compliance, and coach managers on HR policies and practices. Partner with global and local P&O stakeholders to improve employee experiences, build leadership capability, and advance technology-enabled HR processes.

Develop and evaluate AI and machine learning methods for low-molecular-weight drug discovery. Build generative, multimodal, foundation, and agentic models; establish rigorous benchmarking; connect model performance to experimental decisions; and integrate AI into design-make-test-analyze workflows. Collaborate with medicinal chemistry, DMPK, biology, engineering, and data science teams while communicating results to multidisciplinary stakeholders.

Lead development and deployment of hybrid ML and mechanistic modelling pipelines (PK/PD, QSP) to generate actionable insights across drug discovery and development. Drive reproducible workflows, integrate diverse datasets, advance AI-augmented modelling, and communicate results to influence translational decision-making.

Leads the strategy, architecture, development, deployment, and optimization of an agentic AI platform. The role transforms products and workflows into scalable AI experiences, oversees AI engineering operations, establishes security and governance practices, evaluates models and orchestration frameworks, and drives observability and continuous improvement. The Associate Director partners with product, data, architecture, security, and business teams while building and mentoring high-performing engineering teams.

Leads the design and implementation of AI-powered data pipelines, enterprise feature stores, analytics-ready repositories, and self-service data access layers. Builds automated feature engineering, data quality, observability, and governance capabilities for large-scale data science workloads. Partners with Enterprise IT, establishes data availability and quality SLAs, integrates structured and unstructured data sources, and leads teams delivering scalable analytics infrastructure.

Leads enterprise reporting for data quality, governance, compliance, and operational performance. Designs executive dashboards, scorecards, and reporting packages; integrates metadata and quality metrics; aligns definitions and thresholds with stakeholders; oversees incident and remediation tracking; manages vendors, schedules, documentation, and change controls; validates data integrity; automates recurring reports; and escalates risks and compliance issues to leadership.

Lead enterprise metadata strategy, standards, governance, capture, curation, validation, lineage, and lifecycle management across data products and business assets. Partner with data governance, engineering, quality, analytics, modeling, and stewardship teams to improve data trust, discoverability, reuse, and AI readiness. Drive metadata automation, catalog synchronization, certification support, issue remediation, and stakeholder communications across a complex enterprise data environment.