About the Role
As a Software Engineer on the Marketplace team, you’ll work at the intersection of full-stack product engineering and machine learning operations—helping power the systems that match renters to homes across ApartmentList.com, Kaleno.com and Sunny.com. You’ll contribute to a range of work: building and maintaining the backend which powers search, supporting ML pipelines and model serving infrastructure, and collaborating closely with data scientists and engineers to bring ML capabilities into production.
This is a blended role for an engineer who is comfortable context-switching between product feature work and ML infrastructure, and who is excited to grow their depth in both areas. You’ll work closely with your Engineering Manager, product partners, and a globally distributed team.
What You’ll Do
- Build and maintain full-stack features across our marketplace products, including search, ranking, and renter-facing UI
- Support and extend ML Ops infrastructure—including model deployment pipelines, feature stores, real-time data pipelines, and monitoring—primarily on Chalk and Vertex AI
- Collaborate with data scientists to bring models into production reliably and at scale
- Write clean, well-tested code and participate actively in code reviews
- Contribute to technical planning and help break down complex problems into well-scoped, executable work
- Identify and address performance, reliability, and scalability issues in existing systems
- Communicate progress clearly and surface blockers early
What You’ll Bring
Required
- 5+ years of professional software engineering or ML Ops experience, with meaningful work in backend or full-stack development
- Proficiency in at least one backend language (We use Ruby/Javascript, Go, Python)
- Exposure to ML concepts and an interest in working at the boundary of software engineering and machine learning— you don’t need to be an ML Engineer, but you should be comfortable in that world.
- Hands-on experience with ML Ops tooling—Vertex AI, MLflow, Kubeflow, or similar
- Hands-on experience with feature stores — Chalk or similar
- Demonstrated proficiency managing data using SQL and programming languages, with exposure to feature pipelines and A/B testing frameworks
- Experience with a balanced approach using AI tools – improving workflows, reducing feedback loops
- Experience working in cloud environments (GCP preferred; AWS or Azure also considered)
- Excellent communication skills as well as debugging instincts with an ownership mindset
- Ability to collaborate effectively across time zones and with cross-functional partners
Nice to Have
- Mentorship experience for other engineers
- Experience with search or recommendation systems
- Experience working in an online marketplace.
- Familiarity with real-time systems, particularly in the context of optimizing latency and performance
Here's the Pay Range:
At Apartment List, we carefully consider a variety of factors to determine compensation for each position, including the role, level, and work. The US Total Target Compensation (TTC) for this position is:
- Zone 1: $210,000 - $255,000 TTC (including $189,000 - $229,000 base salary) + equity
- Zone 2: $194,000 - $236,000 TTC (including $175,000 - $212,000 base salary) + equity
- Zone 3: $179,000 - $217,000 TTC (including $161,000 - $195,000 base salary) + equity
This reflects the compensation target for new hire salaries for the position across all US locations. Please note, the compensation details provided do not include benefits and perks that we offer.
We also rely on market indicators along with considering your work location, job related skills, experience and relevant education and training, to determine compensation that is fair and competitive for you. Apartment List will consider paying compensation near the higher of the range in exceptional circumstances, where candidates have the experience, credentials or expertise that would warrant such consideration. It is always our goal to hire exceptional talent and we would be happy to share more about compensation during the hiring process.