Top Tech Jobs & Startup Jobs in Boston, MA

Conduct applied AI research and build foundation models across design, training, evaluation, validation, and production implementation. Collaborate with data scientists, engineers, machine learning engineers, and product managers to deliver AI-powered customer experiences. Research emerging methods, develop scalable model and platform code, translate technical findings into business outcomes, and contribute to technical reports or conference publications. The role requires ownership of a research agenda and expertise in deep learning, optimization, robustness, explainability, or model fine-tuning.

Leads full-stack engineering projects and development teams building cloud-based risk management and GRC solutions powered by AI. Responsibilities include designing distributed microservices, APIs, and reusable frameworks; using AWS, Docker, Kubernetes, databases, and multiple programming languages; establishing engineering standards; mentoring developers; improving system performance; reducing technical debt; and influencing technology strategy across Agile teams.

Build and optimize foundational AI systems, including LLM inference, agentic workflows, vector search, model evaluation, guardrails, and orchestration pipelines. Lead cost-performance governance, improve model scalability and hardware utilization, shape technical roadmaps, establish AI engineering standards, and mentor senior technical staff. The role requires applying current AI research to reliable, responsible production systems across cloud and large-scale infrastructure.

Leads an AI engineering team developing, deploying, and supporting production AI systems, including foundation models, LLM inference, similarity search, guardrails, evaluation, observability, and responsible AI governance. Owns technical strategy, optimization, build-versus-buy decisions, roadmaps, team development, and cross-functional collaboration. The role also applies emerging AI research to scalable, reliable, cost-effective production systems.

Conduct applied research and develop large-scale AI foundation models, particularly LLMs, optimization methods, and fine-tuning techniques. Responsibilities include designing, training, evaluating, validating, and deploying models; optimizing training and inference at scale; collaborating with data scientists, engineers, and product managers; translating research into business applications; and producing technical reports or conference publications.

Leads engineering projects and developers building cloud-based, full-stack risk management solutions. Designs distributed microservices, APIs, databases, and AI-powered systems; establishes engineering standards; mentors engineers; partners with product managers and architects; improves system performance and reduces technical debt. The role uses modern programming languages, cloud platforms, container orchestration, CI/CD, automated testing, and observability practices.

Leads technology projects and engineering teams developing full-stack, cloud-based risk management solutions. Builds distributed microservices and AI-powered systems, establishes engineering standards, partners with architects and product managers, improves system performance, reduces technical debt, and influences technology strategy. Provides technical leadership, mentors engineers, evaluates emerging technologies, and delivers scalable solutions using modern programming languages, databases, containers, cloud platforms, APIs, CI/CD, and automated testing.

Conduct applied AI research and build foundation models from design through production deployment. Partner with data scientists, software engineers, machine learning engineers, and product managers to develop scalable AI products. Lead research initiatives, optimize training and inference, evaluate emerging methods, translate research into business outcomes, and deliver model libraries or platform code. Work may focus on large language models, deep learning optimization, fine-tuning, robustness, explainability, or reinforcement learning.

Designs, develops, deploys, and supports large-scale AI systems, including foundation-model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. The role optimizes AI infrastructure for scalability, latency, throughput, cost, and hardware utilization; defines reliability objectives; leads architecture and technical reviews; and mentors engineering staff.

Design, develop, deploy, and optimize foundational AI systems, including large language model inference, foundation model training, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead architecture, cost-performance optimization, technical standards, and design reviews for scalable production AI systems. Mentor senior technical staff and contribute to the long-term roadmap for responsible AI infrastructure.