ኢዮብ
ከአጋር ምግብ
Engineering Leader
ዋጋ በጥያቄ
ዝርዝሮች
- የቅጥር አይነት
- የሙሉ ጊዜ
- የርቀት
- አይ
- ኩባንያ
- Ema
መግለጫ
About Ema
Ema builds AI Employees for HR, IT and Finance. Our AI Employees take on the busy work across the employee experience, from recruiting, onboarding and benefits to IT support, invoice processing and payroll, so people can spend their time on work that needs them. Founded by former executives from Google, Coinbase, Flipkart, and Okta, our team includes engineers from premier tech companies and graduates of Stanford, MIT, UC Berkeley, CMU, and IITs.
We are backed by industry leading investors including Accel, Naspers/Prosus, Creaegis, Section32, and angels like Sheryl Sandberg and Dustin Moskovitz. Headquartered in Silicon Valley and with offices in London, Bangalore and Vancouver, Ema is at the frontier of what Agentic AI can do in production. We ship real systems that run real business processes at scale.
The Role
We are looking for an Engineering Leader to manage and scale multiple product lines in the Voice, BPO, and Workforce Management space. This is a high-impact leadership role that sits at the intersection of real-time voice systems, operations research, and data-intensive platform engineering.
You will report directly to the Head of Engineering and own the engineering organization that builds the infrastructure powering Ema’s Voice AI Employees, Agent QA, auto-learning pipelines, rich analytics, and workforce optimization capabilities — all operating as scalable, multi-tenant systems deployed across global geographies.
You will collaborate with Product, ML/AI, and Go-to-Market teams to translate customer needs into production systems that handle high volumes of voice data, deliver real-time insights, and continuously improve through automated learning loops. As the owner of multiple product lines, you will balance roadmap priorities across Voice, BPO operations, and WFM (work force management) — ensuring each product evolves cohesively while meeting distinct customer needs.
What You Will Do
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
Required
• 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.…
Source: Arbeitnow (https://www.arbeitnow.co.uk/jobs/companies/ema/engineering-leader-london-302686)
Ema builds AI Employees for HR, IT and Finance. Our AI Employees take on the busy work across the employee experience, from recruiting, onboarding and benefits to IT support, invoice processing and payroll, so people can spend their time on work that needs them. Founded by former executives from Google, Coinbase, Flipkart, and Okta, our team includes engineers from premier tech companies and graduates of Stanford, MIT, UC Berkeley, CMU, and IITs.
We are backed by industry leading investors including Accel, Naspers/Prosus, Creaegis, Section32, and angels like Sheryl Sandberg and Dustin Moskovitz. Headquartered in Silicon Valley and with offices in London, Bangalore and Vancouver, Ema is at the frontier of what Agentic AI can do in production. We ship real systems that run real business processes at scale.
The Role
We are looking for an Engineering Leader to manage and scale multiple product lines in the Voice, BPO, and Workforce Management space. This is a high-impact leadership role that sits at the intersection of real-time voice systems, operations research, and data-intensive platform engineering.
You will report directly to the Head of Engineering and own the engineering organization that builds the infrastructure powering Ema’s Voice AI Employees, Agent QA, auto-learning pipelines, rich analytics, and workforce optimization capabilities — all operating as scalable, multi-tenant systems deployed across global geographies.
You will collaborate with Product, ML/AI, and Go-to-Market teams to translate customer needs into production systems that handle high volumes of voice data, deliver real-time insights, and continuously improve through automated learning loops. As the owner of multiple product lines, you will balance roadmap priorities across Voice, BPO operations, and WFM (work force management) — ensuring each product evolves cohesively while meeting distinct customer needs.
What You Will Do
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
Required
• 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.…
Source: Arbeitnow (https://www.arbeitnow.co.uk/jobs/companies/ema/engineering-leader-london-302686)
ይህ ዝርዝር ከአጋር ምግብ የመጣ ነው። ከምንጩ ድህረ ገጽ ላይ ያመልክቱ።
ምንጭ: Ema
ዝርዝር የቀረበው በEma ነው።
