Top Tech Jobs & Startup Jobs in Boston, MA

Own the architecture and engineering of LiveKit’s go-to-market systems, including Salesforce, HubSpot, integrations, data flows, observability, change management, and AI agentic workflows. Lead build-versus-buy decisions, vendor evaluations, roadmap planning, and cross-functional initiatives spanning Sales, Marketing, Finance, RevOps, and Engineering. Mentor engineers, establish technical standards, maintain reliable CRM synchronization, and support CPQ-to-billing automation.

Design developer tools for testing, evaluating, debugging, and observing AI voice agents. Translate dense real-time data into clear interfaces, create AI-native workflows, prototype in code, collaborate closely with engineers, and contribute polished patterns to a design system for technical users.

Own 8–10 technical searches end to end, partnering with engineering leaders to define roles, source senior infrastructure, backend, SDK, and AI talent, close candidates, and build recurring talent pipelines. The recruiter will improve recruiting systems, sourcing playbooks, interview processes, and candidate experience while serving as a strategic talent partner to engineering teams.

Run US and international payroll end to end, including multi-state processing, tax filings, reconciliations, and compliance. Manage PEO and EOR relationships, maintain payroll controls and documentation, support month-end close, and partner with People, Talent, and Accounting. Serve as the internal payroll subject-matter expert, handle auditor requests, and support Carta equity administration and board reporting.

Owns revenue accounting and the full order-to-cash cycle for complex, consumption-based infrastructure software revenue. Responsibilities include developing ASC 606 policies, managing monthly revenue close, deferred revenue and contract balances, external audits, billing-to-revenue reconciliation automation, billing accuracy, collections, commission capitalization, and reviewing non-standard contracts. The role requires technical accounting authority, process creation, and hands-on startup experience.

Own the product organization’s operating system, including planning cadence, roadmap management, goal tracking, reporting, product development standards, launch readiness, customer insight systems, PM onboarding, tooling, and team development. Partner with product leadership, data, marketing, sales, success, and engineering to improve product execution and organizational effectiveness.

Own the development of LiveKit’s marketing website and brand system. Build high-performance launch and product pages, reusable components, motion patterns, and tooling that translate brand designs into polished web experiences. Collaborate with designers, marketers, and design engineers while maintaining strict performance standards and Core Web Vitals. The role emphasizes front-end engineering, interaction design, animation, fast delivery, and solving complex technical problems.

Lead LiveKit’s brand design across campaigns, events, digital, print, out-of-home, presentations, environmental design, and merchandise. Own projects from concept through production, extend the brand across new formats, and partner with marketing and sales to ship polished work quickly. The role requires strong craft, systems thinking, creative direction, broad medium experience, and experimentation with AI tools. Motion sensibility and experience at developer-facing or design-forward companies are advantageous.

Own GTM strategic finance for a high-growth AI infrastructure company, including consumption revenue forecasting, deal desk governance, pricing and margin management, sales compensation, quota planning, financial modeling, KPI analytics, and strategic partnership with GTM leadership and Finance. The role requires advising on deals, building scalable frameworks, and translating data into actionable recommendations.

Build post-training infrastructure for voice and text agents, including training environments, verifiers, synthetic data pipelines, evaluations, and model release gates. Run end-to-end training experiments, select open-weight models, improve tool-using and multi-turn behavior, deploy models to production, and iterate using real-world usage. The role emphasizes Python engineering, reinforcement learning, GPU-based training, data quality, reward design, and production machine learning.