Top Tech Jobs & Startup Jobs

Own product and feature launches, positioning, messaging, go-to-market plans, sales enablement, and technical content. Develop collateral, case studies, thought leadership, vertical-specific messaging, and customer proof points for technical and executive audiences. Collaborate with product, engineering, sales, and design teams while tracking launch adoption, share of voice, and pipeline impact.

Support banking and payments transformation programs for financial institution clients. Facilitate discussions, analyze performance, develop recommendations, forecast improvements, collaborate with business and technology teams, and produce insights and deliverables for decision-making. The role requires financial services or management consulting experience, banking and payments knowledge, strong analytical and communication skills, and proficiency in Excel and PowerPoint.

Own the design, implementation, configuration, testing, administration, and optimization of Legora’s global HRIS. Gather requirements, translate People processes into scalable workflows, manage implementation workstreams, maintain data integrity, coordinate releases, troubleshoot issues, and support stakeholders across People, Finance, IT, and the broader business.

Research Engineer responsible for identifying weaknesses in voice AI models, defining improvement targets, designing evaluations, curating and generating data, fine-tuning deep learning models, and rapidly shipping production systems. The role emphasizes end-to-end ownership of customer problems, measurable production improvements, practical judgment, and fast experimentation. Candidates should have strong Python and PyTorch skills, production ML experience, and ideally experience with speech technologies, distributed training, publications, open source, or public benchmarks.

Research and improve voice AI models by identifying production weaknesses, defining evaluation metrics, curating data, fine-tuning deep learning systems, and rapidly shipping solutions. The role focuses on speech-to-text, text-to-speech, voice agents, model evaluation, customer-driven research, and production machine learning systems.

Own end-to-end product execution for AI-native experiences, from concept through launch. Shape the roadmap using customer, market, competitive, and business insights; collaborate with Design, Research, Engineering, Marketing, and Analytics; iterate rapidly based on data; and define agentic product experiences that help small businesses automate administrative work.

Develops semiconductor reliability verification methods, stress patterns, modeling, and simulations for DRAM products. Analyzes circuit degradation to the device level, supports silicon validation and qualification, improves verification tools, collaborates across design and product teams, and mentors junior engineers.

Supports advanced DRAM process integration and manufacturing through semiconductor process optimization, electrical and yield data analysis, statistical investigation, experiments, dashboard development, and cross-functional engineering collaboration. The intern identifies process anomalies and yield detractors, evaluates relationships between process conditions and device performance, documents technical findings, and communicates recommendations while using automation, visualization, and AI-assisted tools.

Develops next-generation wafer bonding equipment from concept through manufacturing implementation. Responsibilities include supplier-led hardware and subsystem development, equipment qualification, precision alignment and overlay improvements, controls and automation, reliability and productivity optimization, data-driven troubleshooting, modeling, roadmap development, and technical leadership. The role collaborates across process development, manufacturing, suppliers, and engineering teams, while mentoring engineers and supporting AI-enabled equipment monitoring and analysis.

Supports Azure cloud infrastructure, platform operations, CI/CD pipelines, Infrastructure as Code, and deployment automation. Monitors and improves cloud reliability, performance, and security; assists with incident response, root cause analysis, troubleshooting, documentation, and platform standardization. Partners with senior engineers and cross-functional teams to implement DevOps practices and scalable enterprise solutions.

Lead digital product strategy and feature development across channels. Own product lifecycle from discovery through release, prioritize roadmaps, partner with engineering, design, marketing and business stakeholders, drive business cases and outcomes, support risk/compliance, and coach other product staff.

Leads computational physics modeling and simulation projects supporting product, process, ingredient, and packaging innovation. Develops, validates, deploys, and governs simulation models; collaborates with scientists, engineers, IT, and external partners; translates complex technical findings for business stakeholders; establishes modeling roadmaps; documents best practices; and coaches junior colleagues. The role emphasizes Python-based scientific computing, simulation tools, experimental validation, and reducing reliance on large-scale factory trials.

Own global full-cycle recruiting with a primary focus on enterprise sales and GTM hiring. Build proactive talent pipelines, partner with senior leaders, define candidate profiles, manage hiring through Greenhouse, improve interview processes, deliver strong candidate experiences, and use recruiting metrics to optimize outcomes. The role is hybrid in Boston, requiring three office days per week.

Lead global demand generation strategy and execution for enterprise accounts, owning pipeline growth, ABX, digital, field and regional marketing, events, web experience, budget, attribution, and full-funnel performance. Manage a global cross-functional team and align Marketing with Sales and Revenue Operations across multiple regions. Drive predictable revenue through data-driven optimization, AI-enabled scaling, and enterprise B2B SaaS go-to-market programs.

Develop dashboards, data pipelines, forecasts, and scenario models for engineering leadership. Analyze resource utilization, headcount, budgets, and operational performance while applying AI/ML, LLMs, and automation to improve reporting and workflows. Translate complex, inconsistent data into actionable recommendations and identify process improvements across engineering operations.