About the Opportunity
We’re looking for an experienced Senior Software Engineer to join a highly technical engineering team responsible for a large-scale Machine Learning platform supporting critical production workloads.
This platform serves as a central entry point for ML capabilities across the organization, supporting use cases spanning search, recommendations, advertising, trust and safety, and generative AI. The environment is highly distributed, latency-sensitive, and built to support production applications at significant scale.
While the platform supports Machine Learning workloads, this is primarily a backend software engineering position. The ideal candidate is an accomplished Scala engineer with a strong foundation in distributed systems who can work independently alongside Senior and Staff-level engineers.
What You’ll Do
- Design, build, enhance, and support high-scale backend services using Scala.
- Improve the reliability, maintainability, and overall developer experience of a production ML platform.
- Increase automated test coverage and identify edge cases across a complex, distributed codebase.
- Leverage AI development tools such as Claude and Codex to improve engineering productivity, including PR reviews, approvals, testing, and code-quality workflows.
- Develop and enhance APIs, request-routing services, and orchestration capabilities supporting production workloads.
- Work with distributed, low-latency systems where performance and availability are critical.
- Operate and troubleshoot production services running on Kubernetes.
- Improve observability through metrics, tracing, alerting, profiling, and performance monitoring.
- Collaborate with Software Engineers, Applied Scientists, Data teams, model-serving teams, and other technical stakeholders.
- Help make complex engineering and ML infrastructure increasingly reliable, automated, and self-service.
Required Qualifications
- 7+ years of backend/software engineering experience.
- Strong hands-on Scala development experience in production environments.
- Strong software engineering fundamentals and experience working within complex, distributed systems.
- Experience building and operating scalable, production-grade backend services.
- Understanding of functional programming concepts such as immutability, composition, concurrency, and effects.
- Experience designing or integrating APIs, request-routing systems, messaging systems, queues, or technologies such as gRPC.
- Experience working with Kubernetes, including deployment, scaling, resource management, and production troubleshooting.
- Strong production engineering skills involving monitoring, observability, alerting, performance profiling, and system reliability.
- Ability to independently execute within an engineering organization consisting of experienced Senior and Staff Engineers.
- Strong communication skills and a product-oriented engineering mindset.
ML Platform Experience
A traditional Machine Learning background is not required.
Candidates should, however, have an understanding of or interest in production ML infrastructure concepts such as:
- Model inference and serving
- Feature retrieval
- Model evaluation
- Latency and throughput
- Batch versus real-time processing
- Production deployment of predictive or transformer-based models
Nice to Have
- Experience with cloud infrastructure, particularly Google Cloud Platform (Google Cloud Platform).
- Experience with Scala technologies such as Cats Effect, ZIO, Akka/Pekko, or http4s.
- Backend development experience with Python or PHP.
- Experience with ML/model-serving technologies such as vLLM, NVIDIA Triton, or KServe.
- Experience with feature stores, experimentation platforms, or online model-evaluation systems.
- Exposure to LLM inference infrastructure.
- Experience with GPU infrastructure, capacity management, or cloud cost optimization.
- Experience incorporating Claude, Codex, GitHub Copilot, or similar AI-assisted development tools into engineering workflows.
What We’re Really Looking For
First and foremost, we’re looking for an excellent Scala/backend engineer.
You do not need to be a Machine Learning Scientist or spend your career developing models. If you have deep Scala expertise, strong distributed-systems experience, and the ability to understand how ML systems operate in production, you could be an excellent fit.
This is an opportunity to work on sophisticated backend infrastructure at the intersection of Scala, distributed systems, ML platforms, developer productivity, and generative AI.