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Own product execution for Deploy Data, from customer discovery and requirements through delivery and iteration. Partner with engineering, design, sales, and leadership to prioritize work, decompose complex projects, manage timelines, resolve blockers, communicate risks, and improve delivery practices. The role focuses on enterprise data-labeling tools supporting AI and machine learning products, with emphasis on technical judgment, quantitative decision-making, security, and continuous product improvement.

Design, build, and improve scalable backend systems, including REST APIs, microservices, data processing pipelines, and distributed job systems. Write secure, maintainable, and tested code; troubleshoot production issues; improve engineering practices and tooling; and participate in on-call support. The role requires experience with web applications, Node.js, highly available distributed systems, scalable backend APIs, and relational databases, preferably PostgreSQL.

Lead Deploy’s public relations and corporate communications function, including strategic communications planning, media relations, press releases, thought leadership, executive messaging, product and funding announcements, issues management, and communications reporting. Build relationships with business, technology, and AI journalists; translate complex technical concepts into compelling narratives; advise executives and internal teams; and expand the function through agency or team management over time.

Design, build, and improve scalable backend systems, REST APIs, microservices, data processing pipelines, and distributed task systems. Write secure, maintainable code, contribute to product and engineering improvements, practice test-driven development, debug production issues, participate in on-call rotations, and apply security best practices including OWASP Top 10 techniques.

Leads and mentors machine learning engineers developing and deploying large-scale models. Partners with product, engineering, DevOps, labeling, sales, and recruiting teams to define roadmaps, deliver product milestones, and improve model quality. Oversees the full ML lifecycle, including data collection, labeling, experimentation, training, testing, deployment, security, and risk management. Contributes to technical architecture, modeling strategies, hiring, onboarding, and engineer development.

Manage and audit data projects supporting proprietary AI model development. Responsibilities include configuring projects on the Deploy Data platform, coordinating timelines and stakeholders, identifying and resolving issues, collaborating with Product, ML, and Engineering teams, optimizing data pipelines, auditing project quality, and following information security policies.

Senior Counsel, Commercial will draft, negotiate, and review technology transactions, including SaaS, data licensing, partnership, and master service agreements. The role will advise on privacy, AI regulations, data security, and commercial legal risks; improve contract processes, forms, and policies; collaborate with cross-functional teams and executives; and support the General Counsel on corporate matters.

Prospects and engages strategic enterprise accounts through targeted outreach, market research, and stakeholder management. Partners with Account Executives to support complex sales cycles from discovery through onboarding, develops expertise in AI and machine learning products, tracks sales metrics, and communicates customer feedback to Product. The role requires strong analytical, communication, organizational, and relationship-building skills.

Coordinate enterprise deployments of machine learning products across engineering, product, and business development teams. Responsibilities include configuring models, analyzing technical performance and customer data, generating accurate APIs, resolving technical issues, and managing client engagements. The role requires technical competence, client-facing communication, cross-functional collaboration, and programming knowledge in Python or Node.js.

Leads end-to-end machine learning development, including neural network design, data collection, model training, tuning, deployment, monitoring, and optimization at production scale. Builds scalable code and data pipelines, develops novel ML algorithms, improves backend systems and engineering processes, conducts metric-driven research, mentors engineers, and contributes to product strategy and roadmap planning.