Top Tech Jobs & Startup Jobs in Boston, MA

Contract equity research contributor evaluating AI tools for financial research. The role involves expert interviews, testing AI-generated analysis, reviewing financial content for accuracy and industry relevance, assessing sell-side research workflows, and documenting actionable feedback. Candidates should have recent sell-side equity research experience covering public companies, including published research, ratings, price targets, earnings notes, or coverage initiations.

Design production-style DevOps tasks covering CI/CD, containers, orchestration, infrastructure as code, monitoring, and incident response. Develop reproducible reference solutions with setup and validation instructions, evaluate AI-generated outputs, identify failures, provide precise corrections, and maintain high standards for correctness. Share structured feedback to improve future task design. The contract role requires at least 20 hours weekly and professional English proficiency.

Analyze product and design challenges; explain design and frontend trade-offs; create design documents, usability reports, research summaries, RFCs, and ADRs; evaluate Figma-based design systems, accessibility, and frontend architecture; synthesize complex information; and provide structured expert feedback on project artifacts. The role supports AI training through high-quality analytical and explanatory work.

Reconstructs and enhances PowerPoint presentations, improving slide structure, visual storytelling, data visualization, content clarity, and narrative flow. The contractor analyzes complex materials, edits and refines content, provides constructive feedback, and delivers polished presentation assets remotely. Prior client-facing management consulting experience is required, with contributions supporting AI model training and evaluation.

Create and validate complex datasets for AI benchmark tasks, including files with malformed records, missing data, truncations, and inconsistent formatting. Define correct final states, write detailed grading rubrics, and assess AI outputs for accuracy and completeness. The role requires exceptional attention to detail, error detection, format reconciliation, accurate documentation, and clear asynchronous communication in regulated or audit-sensitive contexts.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Develop and evaluate analytic cases for AI systems across scaled-analysis and pipeline-integration scenarios. Map use cases to taxonomies, assess model responses, identify over-refusal and failure modes, document edge cases, and provide actionable feedback. The role requires expertise in data science, analytics, LLM integration, technical documentation, and evaluation methodology, with government, defense, or intelligence experience valued.

Reconstructs and redesigns data-driven presentations in Microsoft PowerPoint, creating visualizations and analytics reports that communicate complex insights clearly. Reviews and edits content for accuracy, flow, and narrative impact; applies data analysis and statistical reasoning; and refines slides based on feedback. The role supports AI training projects through high-quality presentation and analytical deliverables.

Build and scale distributed data pipelines and architectures supporting AI products and research. Responsibilities include processing large datasets with Spark, designing AWS data systems, managing SQL and NoSQL storage, writing Python and SQL, ensuring data quality and reliability, and implementing automation, orchestration, monitoring, and scalable data operations.

Analyze, debug, and secure diverse codebases; implement features; refactor legacy software; conduct audits and penetration tests; optimize backend performance; and document findings to improve AI training datasets.