Top Tech Jobs & Startup Jobs in Boston, MA

Build and secure scalable backend services and APIs while debugging, optimizing performance, implementing features, refactoring codebases, and conducting security reviews and penetration testing. The role also involves automated testing, code review, vulnerability assessment, and contributing domain expertise to training next-generation AI systems.

Design, build, and maintain scalable big data pipelines, architectures, distributed databases, and storage systems. Use Python and frameworks such as Hadoop, Spark, or Flink for data integration, transformation, and processing. Ensure system performance, availability, security, governance, and data quality while collaborating with cross-functional teams and documenting technical solutions.

Develop and maintain scalable Java backend systems using Spring Boot and microservices architecture. Collaborate with customer teams, review specifications, write clean code, participate in code reviews, document solutions, and troubleshoot and optimize backend performance, reliability, and security. The role also contributes technical expertise to training next-generation AI systems, though prior AI experience is not required.

Complete, review, and document realistic Airtable workflows for AI training. Create and manage bases, tables, views, fields, records, relationships, automations, forms, interfaces, and integrations. Demonstrate Airtable use across project, product, and business operations workflows; evaluate the realism and accuracy of instructions; and identify efficient information-management approaches.

Build reinforcement learning environments to evaluate AI systems on software engineering tasks involving MCP tools. Responsibilities include creating reproducible environments, deterministic verification, golden reference solutions, bug fixes, feature development, codebase refactoring, and performance optimization. The role is remote, flexible, contract-based, and output-focused, with no prior AI experience required.

Evaluate and improve AI-generated outputs for accuracy, clarity, quality, and business alignment. Develop and refine prompts, annotate data, conduct rubric-based assessments, fact-check content, and provide actionable feedback on model performance. Create and edit technical documentation, strategy reports, market analyses, business cases, executive summaries, and professional communications. The role requires strong analytical reasoning, writing, research, quality assurance, and attention to detail.

Conduct aerodynamic and fluid-dynamics analyses for an AI training project. Build geometries, computational domains, meshes, boundary conditions, and solver-ready CFD cases; run command-line simulations; automate workflows with Python; perform parameter sweeps and postprocessing; and validate numerical and physical results. Diagnose convergence issues and communicate modeling assumptions and engineering trade-offs. Requires an aerospace or mechanical engineering background with aerodynamics expertise and practical CFD and Python experience.

Applies statistics, experimentation, data analysis, and machine learning to complex datasets and AI model evaluation. Designs analyses, develops and validates models, performs Python and Pandas data transformations, creates expert prompts and datasets, identifies quantitative errors, and provides structured feedback. Produces reproducible code, analytical reports, and clear documentation while collaborating with stakeholders and reviewing peer contributions.

Create reinforcement learning environments to evaluate AI systems solving complex software engineering tasks with MCP tools. Responsibilities include designing reproducible environments, deterministic verification, golden reference solutions, bug fixes, feature implementation, codebase refactoring, and performance optimization across software codebases.

Part-time remote contractor who evaluates and improves AI-generated software engineering content. Responsibilities include prompt authoring, rubric-based model evaluation, technical documentation, fact-checking, data annotation, research, and detailed feedback on AI outputs. The role requires strong software engineering knowledge, technical writing, analytical reasoning, and attention to detail. Three or more years of professional software engineering experience is preferred, along with experience reviewing technical artifacts such as architecture documents, RFCs, design specifications, or code reviews.