Top Tech Jobs & Startup Jobs in Boston, MA

Build and lead full-stack software engineering initiatives using distributed microservices, cloud platforms, APIs, databases, and modern front-end frameworks. Lead technology projects and developers, establish engineering standards, define architectures, improve system performance, reduce technical debt, mentor engineers, and influence long-term technology strategy. The role emphasizes AWS and other cloud technologies, Agile delivery, open-source frameworks, CI/CD, infrastructure as code, automated testing, observability, and AI-assisted development.

Conduct applied research on large language models, training optimization, and fine-tuning. Design, train, evaluate, validate, and deploy AI foundation models using PyTorch, AWS, Hugging Face, and Lightning. Lead research initiatives across data science, engineering, and product teams, translating advances into scalable production systems. Candidates should have advanced academic training and experience with large-scale deep learning, model optimization, self-supervised learning, robustness, explainability, or RLHF, plus a record of impactful research and publications.

Lead data science for US Card customer management by partnering with data scientists, engineers, product managers, and business teams. Build, validate, and implement machine learning models using large-scale numeric and textual data. Apply statistical modeling, clustering, classification, sentiment analysis, time series, and deep learning to generate customer insights and forecasts. Translate technical findings into business outcomes while contributing to innovation, stakeholder collaboration, and talent development.

Leads full-stack software engineering projects and developer teams building cloud-based, distributed microservices and customer-facing systems. Responsibilities include designing service boundaries, developing APIs, improving system performance, reducing technical debt, establishing engineering standards, mentoring engineers, and influencing technology strategy. The role uses modern programming languages, databases, container orchestration, cloud platforms, CI/CD, infrastructure as code, automated testing, observability, and AI-based development tools.

Leads full-stack software engineering projects and developer teams building cloud-based microservices and distributed systems. Responsibilities include designing APIs and reusable frameworks, establishing engineering standards, mentoring engineers, improving system performance, reducing technical debt, and evaluating emerging technologies. The role uses multiple programming languages, databases, cloud platforms, containers, CI/CD, infrastructure as code, automated testing, and observability tools.

Leads engineering projects and developers building cloud-based, AI-powered risk management solutions. Designs distributed microservices and full-stack systems, establishes technical standards, partners with architects and product managers, improves system performance, reduces technical debt, and mentors engineers. The role uses AWS technologies, modern programming languages, databases, APIs, CI/CD, infrastructure as code, automated testing, and observability within Agile teams.

Lead the architecture, development, deployment, optimization, governance, and observability of scalable AI platforms and systems. Build foundation model training, LLM inference, agentic workflows, similarity search, guardrails, and evaluation capabilities. Establish enterprise AI safety and transparency standards, drive long-term infrastructure strategy, mentor technical leaders, and guide organization-wide adoption of responsible AI solutions.

Build, deploy, scale, and maintain machine learning models and platforms in production. Develop optimized data pipelines, distributed ML systems, cloud-based architectures, and automated CI/CD workflows. Collaborate with product and data science teams, monitor and retrain models, apply responsible AI practices, and ensure secure, governed software delivery. The role requires extensive experience with ML frameworks, distributed systems, cloud services, Kubernetes, and production machine learning operations.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Define reliability objectives, lead architecture and technical reviews, ensure security and compliance, contribute to long-term AI platform strategy, and mentor senior technical staff.

Design, develop, deploy, optimize, and support large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, and orchestration pipelines. Lead cost-performance governance, technical design reviews, AI engineering standards, and long-term platform architecture. Apply state-of-the-art optimization techniques for latency, throughput, scalability, hardware utilization, and inference costs while mentoring senior technical staff and ensuring responsible AI practices.