Ema - Engineering Leader

London, Greater London, United Kingdom - Hybrid+ Equity3w ago

In Office Mid EMEA Artificial Intelligence Software Distinguished Engineer Kafka WebRTC Go Linear Erlang

Requirements

• 12+ years of software engineering experience, with 4+ years leading engineering teams of 8+ engineers at high-growth startups or top-tier tech companies. • Deep expertise in building real-time, data-intensive systems. • Strong foundation in data stores and storage systems: relational databases, time-series stores, columnar analytics engines, and caching layers — with the judgment to choose the right tool for the workload. • Experience building quality software using AI-driven tools and workflows. • Track record of taking products from 0→1 and iterating rapidly based on customer feedback. • Hands-on proficiency with SOLID principles, clean architecture, and design patterns — you write and review production code and lead by example on system design. • Experience building scalable multi-tenant SaaS platforms with data isolation, geo-distributed deployments, and compliance requirements (SOC 2, GDPR, HIPAA). • Track record of building ingestion systems for high volumes of load — event-driven architectures, stream processing (Kafka, Flink, or equivalent), and batch/real-time hybrid pipelines. • Strong product sense: ability to deeply understand customer problems in contact center, voice, and workforce domains and translate them into clean technical solutions. • Proven ability to hire, develop, and retain high-caliber engineers across multiple disciplines in competitive markets. • Good to have • Good to have • Experience in voice/telephony systems: WebRTC, SIP, voice-over-IP infrastructure, or contact center platforms. • Background in operations research or optimization: scheduling algorithms, linear/integer programming, constraint satisfaction, or demand forecasting models. • Experience building analytics platforms: OLAP systems, real-time dashboards, metric computation engines, or BI infrastructure. • Prior work on auto-learning or continuous improvement systems: feedback loops from production data, automated retraining pipelines, or quality monitoring systems. • Experience with AI/ML-powered products in enterprise settings — agentic automation, LLM orchestration, or AI-driven UX. • Familiarity with WFM or contact center operations — staffing models, Erlang-based capacity planning, or real-time adherence systems.

Responsibilities

• Scalable Multi-Tenant Systems • Architect and build multi-tenant systems that serve enterprise customers across geographies with strict data residency, isolation, and compliance requirements. • Design high-throughput ingestion systems capable of processing large volumes of voice data, call metadata, and operational telemetry in near real-time. • Make foundational architectural decisions on data stores, stream processing, and storage tiers — balancing query performance, cost, and operational simplicity. • Champion SOLID principles, clean architecture, and engineering rigor across all codebases — ensuring systems are testable, extensible, and maintainable at scale. • Team Building & Engineering Culture • Recruit, hire, and develop senior engineers across a multi-disciplinary function spanning voice engineering, backend systems, data engineering, and applied operations research. • Establish engineering standards, code review culture, and a strong bias toward shipping — with equal commitment to system reliability and customer experience. • Coach and grow senior/staff engineers into technical leaders; manage engineering managers as the organization scales. • Drive cross-functional alignment with Product, ML/AI, and Go-to-Market teams to ensure the platform evolves in lockstep with customer needs and market feedback. • Voice Platform & Agent QA • Own the end-to-end engineering for Ema’s AgentQA capabilities — real-time voice pipelines, telephony integrations, and voice-to-action workflows. • Build and scale the Agent QA platform: automated call scoring, compliance monitoring, sentiment analysis, and coaching feedback loops. • Design auto-learning systems that continuously improve voice agents from production interactions — closed-loop feedback, model retraining triggers, and quality regression detection. • Deliver rich voice analytics: call volume trends, handle time distributions, first-call resolution metrics, agent performance dashboards, and anomaly detection. • Build scalable voice pipeline across geographies – keeping in mind PII, security and compliance requirements. • Workforce Management & Operations Research • Build WFM capabilities including demand forecasting, shift scheduling, real-time adherence monitoring, and capacity planning — applying operations research techniques to optimize workforce utilization. • Design and implement optimization algorithms for headcount modeling, skill-based routing, and workload balancing across multi-site, multi-timezone contact center operations. • Partner with ML/AI teams to integrate predictive models for call volume forecasting, attrition risk, and staffing efficiency. • What We Look For • What We Look For

Benefits

• Build at the intersection of Voice AI, operations research, and enterprise-scale systems — a rare combination of deeply technical challenges with direct customer impact. • Shape the engineering foundation of a category-defining AI platform — your architectural decisions will power how the world’s largest enterprises run their voice and workforce operations. • High-impact, high-visibility role with direct access to the Head of Engineering, co-founders, and enterprise customers. • A team of exceptional engineers from Google, Meta, Microsoft Research, and top CS programs worldwide. • Competitive compensation, meaningful equity, and the opportunity to build something that matters. • Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits.