Ema

Leads and scales Ema’s global customer delivery function, personally sponsoring strategic enterprise deployments and establishing repeatable practices for go-live, adoption, stabilization, and value realization. Requires 12+ years in enterprise delivery and strong technical fluency with production AI systems.

Build and lead the revenue operations function for a US go-to-market organization spanning direct enterprise and partner-led sales. The role owns forecasting, attribution, territory and compensation design, CRM and data infrastructure, deal desk processes, and executive reporting.

Leads strategic go-to-market partnerships with consultancies, resellers, cloud platforms, system integrators, and AI ecosystem players. The role requires at least five years of partner management experience and a track record of enabling partners and driving enterprise revenue.

Product manager responsible for shaping product strategy, researching customers, prioritizing features, and driving AI product adoption. Requires 6–10 years of SaaS or enterprise product management experience, strong cross-functional leadership, and a computer science or related degree.

Leads Ema’s North American partnerships and alliances strategy across GSIs, ISVs, cloud ecosystems, and Private Equity. The role owns partner-sourced revenue, builds the partner program, and manages a team while requiring 10+ years of partnerships experience and enterprise GTM expertise.

Six-month remote internship for final-year Computer Science students working across AI and data engineering. Residents develop and evaluate machine learning models, build backend and data pipelines, and analyze large datasets while receiving mentorship and a monthly stipend.

Measures and improves healthcare AI agents in production by instrumenting performance, diagnosing quality issues, and designing experiments with clinical experts. The role requires Python, analytical judgment, comfort with ambiguity, and interest in healthcare.

Owns high-severity enterprise customer issues for deployed agentic AI systems, diagnosing failures across AI behavior, workflows, integrations, and configuration. The role requires 6–9+ years of senior technical support, reliability, or production engineering experience, strong incident management, and clear customer communication.

Build and deploy agentic AI workflows directly with enterprise customers and partners, from process discovery and solution design through demos, technical validation, production implementation, and adoption. The role requires coding ability, persuasive communication, product judgment, and comfort operating independently across international deployments.

AI Resident who owns a hard ML/agent problem end-to-end: from proposal and building to evaluation, shipping in production, and rigorous write-up. Requires strong ML fundamentals, Python/PyTorch engineering, and depth in at least one area like post-training, reward modeling, agents, or eval.

The DevOps Engineer will design and operate highly available, multi-tenant cloud infrastructure, automation, CI/CD, observability, security, and data or ML systems. The role requires 7+ years of infrastructure experience, cloud expertise, and the ability to work in a fast-paced global startup environment.

Design intuitive user experiences for Ema’s enterprise AI platform, simplifying complex workflows through research, prototyping, and design systems. The role requires 3+ years of UX/UI experience, proficiency with tools such as Figma or Sketch, and a bachelor’s degree in design or HCI.

Leads end-to-end delivery and stabilization of production agentic AI implementations for enterprise customers. The role requires 8+ years in technical delivery or program leadership, strong technical judgment, and experience with AI or automation systems, cloud platforms, APIs, and complex integrations.

Leads strategic enterprise AI accounts, advising C-suite stakeholders, shaping transformation roadmaps and business cases, and influencing complex deals. Requires 7+ years of consulting or equivalent enterprise strategy experience, strong AI/ML fluency, and executive relationship management skills.

Social Media & Community Manager responsible for scaling LinkedIn presence, creating and repurposing content, building an enterprise AI community, and supporting brand initiatives through events and thought leadership. Requires 5+ years in B2B tech marketing with strong writing, trend awareness, and AI familiarity.

Generates and qualifies enterprise sales opportunities through multi-channel outreach, webinars, events, and targeted account engagement. The role requires at least three years of sales or business development experience, strong communication skills, and familiarity with SaaS platforms and HubSpot.

Build and maintain scalable enterprise backend systems, APIs, integrations, and ML data pipelines using Python, Go, cloud technologies, and modern AI architectures. The role requires 7+ years of experience, strong software engineering fundamentals, and experience with databases, containers, SaaS, and enterprise integrations.

Build and operate multi-tenant, distributed infrastructure for an enterprise AI platform across Kubernetes and multiple clouds. The role requires 5+ years of platform, infrastructure, or backend engineering experience, strong Golang and Python skills, and deep expertise in reliability, databases, and distributed systems.

Builds and operates multi-tenant, cloud-native platform infrastructure for an agentic AI product. The role requires 5+ years of platform, infrastructure, or backend engineering experience, strong Golang and Python skills, Kubernetes expertise, and deep distributed-systems knowledge.

Build and operate production AI agents for enterprise customers, owning the full lifecycle from discovery through reliability improvements. The role requires 5+ years of engineering experience, strong backend fundamentals, Python and Go proficiency, customer-facing communication, and comfort in ambiguous environments.

Owns the reliability, availability, and operational health of an agentic AI platform across customer environments. The role focuses on cloud infrastructure, deployment automation, observability, incident response, and production stability, requiring 3–5 years of DevOps, infrastructure, or deployment engineering experience.

Designs and builds scalable infrastructure for AI products, focusing on cloud platforms, Kubernetes orchestration, CI/CD pipelines, and observability. Requires 3+ years in infrastructure engineering and bachelor's/master's in CS.

Leads multiple engineering teams and product lines spanning voice AI, Agent QA, data-intensive platforms, and workforce management. The role requires 12+ years of software engineering experience, substantial people leadership, and expertise in scalable real-time systems, multi-tenant SaaS, and data infrastructure.

Serves as the CEO's strategic right hand, driving executive operations, sales/GTM acceleration, corporate strategy, investor relations, and special projects in a high-growth AI company. Requires 10+ years experience including top-tier consulting and tech strategy roles, MBA, and full co-location in Mountain View, CA.

Owns portfolio of strategic enterprise accounts end-to-end, orchestrating AI delivery teams, ensuring post-go-live outcomes like adoption and ROI, managing C-level relationships, and driving expansion through proven value. Requires 15+ years in enterprise tech delivery with 5+ years account ownership.

Develops exceptional user interfaces for web applications using modern frontend technologies like React and Next.js, handles API integrations, and collaborates on UX design. Requires 5+ years UI engineering experience and bachelor's degree or equivalent.

Defines product strategy, roadmaps, and requirements for AI enterprise products. Collaborates with engineering, design, and cross-functional teams to deliver, launch, and iterate based on market research and performance data. Requires 3-5 years PM experience in tech.

Leads the architecture, roadmap, and production execution of GenAI and agentic machine-learning systems at enterprise scale. The role requires 10–12+ years of applied ML experience, expertise in retrieval and large-scale systems, and the ability to mentor senior technical teams.

Leads AI strategy for high-value enterprise accounts, advises C-level executives on AI transformation roadmaps, and collaborates cross-functionally to drive deals. Requires 7-12+ years consulting experience at top firms like McKinsey/BCG and enterprise sales familiarity.

Build full-stack web applications with a front-end focus using React and gRPC, develop UIs, APIs, and integrations. Requires 4+ years experience, strong React proficiency, and backend skills in Node.js/Python.

Leads backend engineering for enterprise AI software, building scalable systems with Go/Python, APIs (REST/GraphQL), databases (PostgreSQL/Redshift), and cloud/container tech. Requires 4+ years experience, CS bachelor's, and full-stack contributions in a hybrid role.

Develops and deploys ML models for NLP, retrieval, ranking, reasoning, dialog, and code-generation systems. Requires Master's/PhD, 2+ years experience with production ML, deep NLP expertise, Python, and frameworks like PyTorch/TensorFlow.

Build and maintain scalable backend systems, APIs, data schemas, and enterprise integrations using Go and Python. The role requires at least four years of relevant experience, strong database and cloud expertise, and a bachelor's degree in computer science or a related field.

Technical pre-sales expert collaborating with sales to architect and demonstrate AI platform solutions, execute PoCs, and drive customer deployments from demo to production. Requires 8+ years in solutions engineering and strong cloud/SaaS expertise.

Drives enterprise sales for AI platform by prospecting leads, building executive relationships, managing sales pipeline, and closing $500k+ deals. Requires 2+ years outbound sales experience, CRM proficiency, and technical acumen in fast-paced startup.

Leads development and deployment of ML models for NLP, retrieval, ranking, reasoning, dialog, and code-generation systems. Requires Master's/PhD, production ML experience, deep NLP expertise, Python proficiency, and MLOps knowledge in a fast-paced startup.