Software Engineer, Marketplace
Job description
About the role
The role connects human expertise with AI demand through core marketplace systems. Team members work in person in San Francisco five days each week. The position shapes how opportunities match experts and how the marketplace scales with volume.
Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.
Key facts
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Location: USA
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Engagement: Full-time
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Compensation: Offered
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Team: Engineering
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Workplace: OnSite
- Visa: Not mentioned
- Degree: Not mentioned
What you'll do
Search, eligibility, scoring, allocation, and fulfillment APIs define which candidates fit specific opportunities. Data models and system abstractions support a labor marketplace that evolves quickly across job and expert types. Real-time and asynchronous decisioning infrastructure at high volume keeps latency, reliability, and throughput aligned with business outcomes. API performance and reliability for marketplace decisions improve in real time for internal consumers and downstream systems.
Requirements
The posting states a minimum of 4 years of experience.
Track record of building and operating reliable backend systems in production at meaningful scale guides marketplace decisions. Strong judgment in system design, performance, reliability, and data modeling handles complex marketplace behaviors. Comfort with high-throughput APIs, distributed systems, and asynchronous workflows powers critical operations. Product and marketplace requirements translate into clean technical systems that balance speed, correctness, and maintainability. High engineering standards favor simple, durable abstractions that reduce long term complexity.
Nice to have
Experience with search, recommendation, matching, scheduling, or marketplace systems aligns technical solutions with marketplace needs. Supporting ML-powered products or integrating model inference into production systems strengthens end-to-end workflows. Familiarity with event-driven architecture, queues, caching, and observability tooling keeps systems robust and maintainable.
Practical notes
The role requires in person presence five days a week in the San Francisco office. Typical interview steps
Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.
Good to know
Roles centered on marketplace infrastructure rely on distributed systems and observability to serve expert matching at scale. Python, Go, Kafka, Postgres, Redis, Elasticsearch, Kubernetes, and Terraform commonly power these platforms. Designing for reliability, performance, and clean abstractions helps marketplaces adapt to changing demands. Domain expertise and system design shape how well platforms integrate human work with AI workflows.
Questions to ask
Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.
Career growth
Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.