Senior Software Engineer, Applied AI & Customer Solutions

At Coursera, we aim to allow employees to develop their skills and grow their careers. One way we do this is with internal transfers. In order to support open and fair transfers, we practice transparency and have set guidelines for who is eligible to request a transfer.

Anyone who has been in their role for more than 6 months and in good standing may apply for a new position at Coursera. For consideration outside this timeframe, individuals should talk to their manager and People Business Partner. Please be sure to read the our Internal Mobility Program Policy here.

We’re a globally distributed team that comes together intentionally for collaboration, complex problem-solving, and key milestones — creating opportunities for teams to do their best work together. Our virtual hiring and onboarding experience makes it easy to join us and start making an impact from anywhere. If you’re ready to make a global impact, help scale unique products across Coursera + Udemy, and grow your career, apply below.

Job Overview

As a Senior Software Engineer, you will join a fast-paced innovation team that builds and deploys AI-powered solutions directly with Coursera's enterprise and campus customers. You will sit at the intersection of AI/Data Engineering, cloud and security architecture, and customer-facing solutioning — working hands-on with customers to map their workflows and data, prototype solutions quickly, and harden the ones that prove valuable into production deployments.

You'll operate across the full engagement lifecycle: scoping a customer's environment and pain points like a consultant, prototyping working demos in real time with the customer, and then hardening the strongest patterns into production-grade, secure and compliant deployments. This role is customer-facing and will require regular travel to customer sites (domestic and occasionally international) for discovery, prototyping, and go-live phases of engagements. You will work closely with Product Managers, AI Specialists, Data Analysts, and other Engineers on the team, and directly with customer executive sponsors and IT/data owners, to decide what gets standardized, deployed, or retired.

Key Responsibilities

• Scope customer environments directly with executive sponsors and IT/data owners — mapping systems, data models, and workflows to identify the real business problem, not just the stated one
• Rapidly prototype and demo working solutions in front of customers, iterating in real time to prove value fast
• Serve as the bridge between customer & core engineering team to harden validated prototypes into production deployments
• Design and implement multi-tenant, hybrid, or customer-controlled deployment architectures, as per customer's data residency, privacy, and IT-maturity requirements
• Build and own identity and access management, encryption, and secure cross-network connectivity (mTLS, VPC peering/PrivateLink, API gateways) for customer-embedded deployments
• Bring security, data-residency and compliance judgment into discovery conversations before a commercial commitment is made, not after
• Own CI/CD, observability, and production support for systems living inside customer environments
• Recognize repeatable patterns across customer engagements and feed field evidence back to Product to inform what should be standardized, deployed more broadly, or retired
• Collaborate closely with Product Managers, AI Specialists, and Program Managers to scope problem statements with a laser focus on customer and business impact
• Travel to customer sites as needed (expect regular travel) to support scoping, prototyping, and go-live phases of an engagement, including in-person workshops and executive readouts

Basic Qualifications

• 5+ years of experience in a software engineering role, with strong hands-on backend engineering and cloud infrastructure experience
• 1+ years of experience building production-grade agentic AI solutions
• Proficiency in backend languages such as Python, Java, Typescript and technologies such as Docker, Kubernetes and Kafka with comfort working across the stack
• Deep understanding of cloud platforms (AWS preferred), able to design and operate both multi-tenant and hybrid customer-cloud deployment models
• Strong experience with data engineering fundamentals — ingesting, cleaning, and normalizing messy, inconsistent customer data across disparate source systems
• Working knowledge of identity and access management, encryption/key management, and secure network patterns (VPC peering, PrivateLink, mTLS) for customer-embedded or regulated environments
• Demonstrated comfort operating directly with customers — scoping ambiguous problems, running discovery, and demoing work-in-progress solutions live, in person and remotely
• Willingness and ability to travel regularly to customer sites, domestically and occasionally internationally, as engagement needs require
• Prior experience leading projects and debugging complex issues with minimal supervision

Preferred Qualifications

• Experience with modern agentic AI tooling such as LangChain, LangGraph, FastMCP, RAG, or MCP
• Experience with Postgres, DuckDB, pgvector, or similar analytical/transactional data layers
• Prior experience in a solutions engineering, professional services, or technical consulting role where you owned a customer relationship end-to-end, including on-site engagement
• Familiarity with data privacy and residency regimes relevant to enterprise/education/government customers (e.g., GDPR, FERPA, DPDPA, HIPAA)
• Demonstrated ability to work in a fast-paced, ambiguous environment and make sound technical trade-offs with limited guidance
• Excellent communication skills, with the ability to translate technical constraints into terms an executive sponsor or non-technical stakeholder can act on

Why Join Us?

• Work on high-visibility engineering problems with direct, measurable impact on enterprise and campus customers
• Work directly with strategic customers across geographies, owning engagements end-to-end rather than a narrow slice of a roadmap
• Directly influence what graduates from customer-facing custom solutions into Coursera's core product
• Be part of a lean, cross-functional team (Engineering, AI Specialists, Product, Program Management) with high autonomy and high trust and become a go-to technical leader
• Be part of a mission-driven company transforming global access to education and upskilling in the AI era

Compensation

Coursera offers competitive pay and fair compensation practices across all regions. Job titles may span multiple career levels, and the targeted hiring base salary range for this role in Canada is $137,000 to $172,000. Actual compensation will depend on factors such as experience, education, transferable skills, business needs, and location. This range may be adjusted over time and may include eligibility for variable pay, equity, and comprehensive benefits.

Coursera is an Equal Opportunity Employer committed to building a welcoming and inclusive workplace. We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request at [email protected].