Artificial Intelligence Jobs in Cambridge, MA

Owns the strategic vision, roadmap, delivery, adoption, and business value of Takeda’s AI-native discovery automation and robotics product family. Leads cross-functional teams across scientific, digital, data, computational, automation, and technology functions; defines AI-enabled laboratory workflows; prioritizes investments; guides platform and integration choices; manages stakeholders and external partners; and drives measurable improvements in laboratory throughput, data quality, scientific impact, and operating efficiency.

Own the technical roadmap, backlog, architecture direction, and delivery execution for Takeda’s AI-native large molecule discovery software products. Partner with business, scientific, engineering, data, and ML leaders to translate discovery workflows into scalable technical requirements and releases. Manage Agile delivery, technical risks, integrations, KPIs, external development partners, and architectural alignment across scientific platforms. Apply biology and laboratory workflow expertise to guide AI-enabled product development and technical trade-offs.

Leads the technical product strategy, backlog, architecture, and delivery roadmap for Takeda’s AI-native laboratory endpoint generation products. Translates scientific and business needs into technical requirements, coordinates engineering, data science, ML, and infrastructure teams, and ensures integration with laboratory platforms such as ELN/LIMS and scientific data lakes. Owns technical trade-offs, delivery quality, engineering KPIs, external partners, and alignment across the Labs of Tomorrow portfolio while enabling AI/ML-driven scientific workflow redesign.

Leads AI/ML application to large-molecule drug discovery, including antibody, bispecific, ADC, and other biologics design. Develops generative, predictive, and optimization models; manages large biological datasets; integrates computational methods with automated Design-Predict-Make-Confirm experiments; and partners with cross-functional drug development teams. Provides strategic direction, mentors scientists, identifies portfolio opportunities, and communicates technical insights to senior stakeholders.

Owns end-to-end RFP and proposal delivery, coordinating stakeholders, managing timelines, authoring responses, and ensuring quality and brand alignment. The role also modernizes proposal workflows through Claude, AI, automation, content libraries, and process optimization. It analyzes RFP trends, improves response strategy, reduces manual effort, and supports scalable revenue operations across global business verticals.

Designs, develops, deploys, and optimizes enterprise AI systems, including foundation models, LLM inference, agentic workflows, similarity search, model routing, and observability. Establishes AI architecture standards, improves system cost and performance, champions responsible AI, and leads technical direction, mentorship, and cross-functional collaboration across Capital One.

Develop and deploy scalable AI systems, including foundation models, LLM inference, agent workflows, vector search, guardrails, evaluation, and governance. Optimize model performance for cost, latency, throughput, and accuracy; build training and fine-tuning pipelines; collaborate with research and data engineering teams; and mentor junior engineers while advancing responsible AI practices.

Design and deliver enterprise-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Set architecture and optimization strategies for scalable, cost-efficient, responsible AI platforms. Lead model routing, caching, orchestration, and cloud deployments while mentoring engineers, influencing stakeholders, and advancing AI research into production.

Build and deploy scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model training and inference for cost, latency, throughput, and hardware utilization. Own complex AI architecture, reliability SLOs, and technical roadmaps while collaborating across engineering, research, infrastructure, and product teams. Lead deployment reviews for security and compliance and mentor senior technical staff.

Design, develop, optimize, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead optimization for cost, latency, throughput, and hardware utilization; establish AI engineering standards; guide technical roadmaps and design reviews; and mentor senior AI engineers.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, cost, latency, throughput, and hardware utilization. Define AI architecture, reliability objectives, and technical roadmaps while ensuring security, compliance, and ethical alignment. Lead technical reviews and mentor engineering teams across complex AI product initiatives.

Develops applied machine learning, causal inference models, generative AI systems, and agentic tools for pharmaceutical R&D. Analyzes multimodal clinical, genomic, transcriptomic, pathology, molecular, and real-world data; collaborates with pharma partners and internal research and engineering teams; communicates scientific findings; and coaches computational biology and real-world evidence teams on AI adoption.

Leads enterprise AI and software engineering modernization initiatives, designing, building, testing, deploying, and maintaining scalable cloud solutions. Establishes engineering standards, applies generative AI development practices, reviews complex architectures, resolves technical issues, and collaborates with infrastructure, security, operations, leadership, and external partners. Provides technical influence, mentorship, and strategic guidance while supporting cloud migration and automation across the organization.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead AI architecture, cost-performance governance, optimization, technical design reviews, and responsible AI standards. Partner with engineering, research, product, and program teams while mentoring senior technical staff and shaping the roadmap for foundational AI platforms.

Designs, develops, deploys, and optimizes large-scale generative AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. The role leads architecture and cost-performance optimization, establishes AI engineering standards, contributes to platform roadmaps, and mentors senior technical staff. It requires deep expertise in AI systems, software engineering, cloud platforms, model optimization, and responsible AI deployment.

Lead the architecture and development of enterprise AI platforms, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, scalability, safety, and transparency. Set long-term technical strategy, drive organization-wide standards, lead platform initiatives, partner cross-functionally, and mentor senior engineering and research leaders.

Build and deploy scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, GPU/TPU utilization, latency, throughput, and cost. Own complex AI architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to foundational AI roadmaps, and mentor engineers on scalable design and research-to-production practices.

Conduct fundamental research on on-device LLM efficiency and optimization. Develop proposals, survey academic literature, formulate hypotheses, and run experiments involving quantization, fine-tuning, compression, decoding, and model architectures. Collaborate with internal teams and academic labs, transition prototypes into production, publish at leading AI conferences, and independently drive research initiatives from ideation through implementation.

Lead AI-driven transformation across Operations by mapping current workflows, designing AI-native target states, validating feasibility and value, building business cases, and facilitating cross-functional workshops. Advise senior executives, manage communications and risks, secure stakeholder alignment, and lead a team of program managers delivering firmwide initiatives.

Lead and manage cross-firm AI in Operations program initiatives: develop strategic plans, apply structured problem-solving and design thinking, support multiple workstreams, engage senior stakeholders, standardize AI use cases, and drive scaling and reimagination of AI capabilities across operations.

Provide U.S. and foreign patent prosecution services for software and electronics clients. Advise clients across industries, prepare and manage patent matters, and collaborate with attorneys across the firm. Candidates must have a relevant bachelor's degree, patent prosecution knowledge, prior firm experience, and USPTO registration or eligibility. Strong academic, organizational, communication, client-service, and strategic skills are required. Patent litigation, industry, and health or medicine experience are beneficial.

Leads strategic customer relationships and go-to-market strategy for Microsoft's Life Sciences and MedTech accounts. Builds executive partnerships, aligns internal teams and partners, and drives cloud, data, AI, security, productivity, and Copilot adoption. The role develops scalable transformation opportunities, promotes responsible technology implementation, drives customer value and growth, and coaches sales and technical-sales teams.

Designs, develops, deploys, and supports large-scale AI systems, including foundation models, LLM inference, agentic workflows, similarity search, guardrails, and model evaluation. Defines enterprise AI architecture, optimizes model performance, cost, latency, and throughput, establishes AI safety and governance standards, leads multi-year platform initiatives, and mentors senior technical leaders across engineering and research.

Design and implement Dynamics 365 Finance solutions supporting financial operations, integrations, reporting, automation, and AI initiatives. Optimize General Ledger, accounts payable, accounts receivable, cash management, fixed assets, and project accounting processes. Lead finance system projects, stakeholder engagement, audit and reporting improvements, training, mentorship, and M&A-related activities. Advise finance leadership on ERP capabilities, Microsoft Copilot, Power Platform, data governance, and emerging AI technologies.

Leads Moderna’s Finance transformation agenda across the Record-to-Analyze value chain, including FP&A, Accounting, and Tax. Owns the transformation roadmap and portfolio, drives cross-functional initiatives from strategy through adoption and value realization, and advances finance technology, data, analytics, automation, and AI capabilities. Establishes governance, manages dependencies and risks, coordinates change and training, and influences Finance, Digital, HR, and business stakeholders to improve financial processes and decision-making.