Software Engineer, Full Stack
- Vancouver, BC, Canada
Full-time
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Job details
- Job type
- Full-time
- Apply by
- Experience
- Experience building and scaling end-to-end production systems, with 5+ years of relevant engineering work
- Posting language
- English
- Posted
Original job description
Software Engineer, Full Stack - Vancouver
About Ema
Ema is building the world’s leading Agentic AI platform to transform enterprise productivity. We enable organizations to delegate repetitive tasks to Ema, the Universal AI Employee, delivering 10x gains in workforce efficiency, across functions. 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, 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
You'll be an engineer who builds AI agents in production, sitting close to the customers who depend on them. This is a full-stack engineering job with an unusually short distance between your code and someone's actual workday. You'll write Go and Python, design schemas, build evals, and present your solution to a senior executive at an enterprise - often in the same week.
What You Will Do
About Ema
Ema is building the world’s leading Agentic AI platform to transform enterprise productivity. We enable organizations to delegate repetitive tasks to Ema, the Universal AI Employee, delivering 10x gains in workforce efficiency, across functions. 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, 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
You'll be an engineer who builds AI agents in production, sitting close to the customers who depend on them. This is a full-stack engineering job with an unusually short distance between your code and someone's actual workday. You'll write Go and Python, design schemas, build evals, and present your solution to a senior executive at an enterprise - often in the same week.
What You Will Do
- Design and ship production agents. You'll build agents that are mission-critical from day one embedded in Teams, Slack, intranets, voice lines, and email, taking real actions against SAP, ServiceNow, Workday, and a long tail of systems nobody has heard of. These run at enterprise volume under enterprise scrutiny.
- Own the full lifecycle. Discovery, build, eval, launch, and the unglamorous months afterward where an agent goes from good to genuinely reliable.
- Work directly with the people whose problem it is. You'll sit with leaders at global enterprises, extract the process from their heads, and decide what should be an agent, what should be a workflow, and what should stay human.
- Push your work back into the platform. The best patterns you find in the field become part of Ema's core product - new connectors, new agent primitives, new tooling. You'll surface unmet needs and prototype the fix, working with product and platform engineering to make it general.
- Built the orchestration layer for a 200,000+ employee IT services firm spanning 65 countries, unifying HR, IT, and payroll into a single interface across SAP, ServiceNow, and Teams.
- Rebuilt a recruiting firm's core stack - sourcing, screening, ranked shortlists - cutting time-to-hire by 67% and retiring their legacy ATS along the way.
- Cut first-response time from two hours to under 90 seconds for an e-commerce logistics platform, including live carrier-site lookups, and held SLAs through peak holiday volume with no temporary hiring.
- Experience building and scaling end-to-end production systems, with 5+ years of relevant engineering work
- Strong backend fundamentals - algorithms, data modeling, and comfort in more than one language, Python and Go especially
- Proven problem-solving in fast-changing, ambiguous environments where the requirements arrive in a meeting rather than a ticket
- A builder's mindset and high agency - you find a way to ship rather than waiting for the blocker to clear
- Comfort working directly with customers
- Clear, direct communication with technical and non-technical audiences alike
- You've built or deployed LLM systems in production, and have opinions about evals
- Familiarity with agent tooling, RAG pipelines, MCP, and prompt engineering as an engineering discipline rather than a trick
- You've been a founder or founding engineer, and know how to balance craft against speed
- Experience integrating enterprise applications - HRIS, ITSM, ERP, ticketing - and the authentication that guards them
- You've worked with distributed, global teams and multi-tenant SaaS environments
More about this job
Responsibilities
Build, deploy, and maintain production AI agents and enterprise integrations across the full lifecycle, from customer discovery and solution design through evaluation, launch, and reliability improvements. Work directly with enterprise customers and product and platform teams to identify needs, deliver solutions, and turn successful field patterns into reusable platform capabilities.
Requirements
Requires at least five years of relevant engineering experience building and scaling end-to-end production systems, strong backend fundamentals, and proficiency in multiple languages, especially Go and Python. Candidates should be comfortable solving ambiguous problems, shipping independently, working directly with customers, and communicating with technical and non-technical audiences; production LLM experience and enterprise integration experience are advantageous.
Skills
- Full-Stack Engineering
- Go
- Python
- Backend Engineering
- Algorithms
- Data Modeling
- Production Systems
- AI Agents
- LLM Systems
- Evaluation Design
- RAG Pipelines
- Prompt Engineering
- Enterprise Application Integration
- Customer Communication
- Multi-Tenant SaaS
- Distributed Teams
Visa sponsorship
Not detected in the job text
Categories
- Software
- Technology
- Engineering
- Consulting
Keywords
- Agentic AI
- AI Agents
- Full-Stack Engineering
- Go
- Python
- Production Systems
- Algorithms
- Data Modeling
- LLM Systems
- Evaluation
- RAG Pipelines
- MCP
- Prompt Engineering
- Enterprise Application Integration
- SAP
- ServiceNow
- Workday
- Microsoft Teams
- Slack
- HRIS
- ITSM
- ERP
- Authentication
- Connectors
- Agent Orchestration
- Workflow Automation
- Multi-Tenant SaaS
- Distributed Teams
- Customer Discovery
- Enterprise Productivity