Top Tech Jobs & Startup Jobs in Boston, MA

Leads software engineering projects and developer teams building cloud-based full-stack and microservices solutions for financial technology. Responsibilities include technical leadership, mentoring, architecture collaboration, API and database development, cloud engineering, performance optimization, technical debt reduction, Agile delivery, and establishing engineering standards. The role uses Python and AWS alongside multiple programming languages, container orchestration, CI/CD, infrastructure as code, automated testing, observability, and AI-assisted development tools.

Lead and deliver large, complex Risk Tech programs and platforms; grow and coach a TPM team; identify and mitigate technical risks; drive cross-functional decisions and execution across product, engineering, design, and data to achieve business impact.

Conduct applied AI research focused on multimodal guardrails and vision-language models. Build and evaluate foundation models across training, validation, optimization, and production implementation. Collaborate with data scientists, engineers, and product managers to deliver scalable AI products. Develop research libraries and platform code, investigate emerging methods, document findings, and contribute to technical reports or conference publications.

Leads multiple software engineering teams and sets technical strategy for scalable, resilient cloud platforms. Responsibilities include mentoring engineers and managers, guiding architecture and design standards, overseeing full-stack development, deploying applications to AWS, modernizing monoliths into microservices, improving engineering practices and delivery velocity, and partnering with product and executive leaders. The role also owns talent strategy, succession planning, workforce development, and technical health across engineering organizations.

Configure and support Workday across HCM, Recruiting, Compensation, Payroll, Benefits, and related modules. Translate business priorities into technical solutions, build and document system configurations, troubleshoot production issues, test updates, improve HR processes, and collaborate with product owners and stakeholders. The role also requires experience with business processes, calculated fields, custom reports, Workday security, reporting, and EIB data loads.

Leads an enterprise data science team developing agentic AI products and machine learning solutions. Responsibilities include defining modeling strategy, designing experiments, analyzing telemetry, influencing senior leaders, validating and stress-testing models, and overseeing the full model lifecycle from design through implementation. The role partners with engineering and research teams and requires extensive experience in large-scale analytics, machine learning, relational databases, cloud computing, and open-source programming.

Leads an AI engineering team developing scalable, responsible AI systems, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Oversees architecture, deployment, optimization, build-versus-buy decisions, and long-term technical strategy. Translates enterprise goals into measurable roadmaps, operationalizes responsible AI standards, collaborates cross-functionally, and mentors engineering talent while advancing production AI capabilities.

Staff Engineer leading technical vision, architecture, modernization, and engineering excellence for Capital One’s cloud-based enterprise data protection platform. Responsibilities include designing scalable distributed systems, migrating monoliths to microservices and event-driven architectures, establishing API and coding standards, mentoring engineers, driving cross-domain initiatives, and providing hands-on technical leadership across security, data, and cloud technologies.

Leads enterprise technical programs and API strategy across Product, Engineering, Machine Learning, and Supplier Management. Establishes TPM operating models, roadmaps, maturity frameworks, and measurable platform outcomes. Drives architectural alignment, dependency sequencing, investment planning, risk escalation, and delivery accountability across engineering teams. Influences executives and technical leaders, manages tradeoffs, advances platform standardization, and develops the organization’s TPM discipline.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI infrastructure for cost, latency, throughput, reliability, and hardware utilization. Define system architectures and SLOs, lead technical reviews, ensure security and compliance, contribute to long-term AI platform strategy, and mentor engineering colleagues.