Senior Backend Software Engineer (Infrastructure MLOps)
New
P
PhiloStreaming media
San Francisco, CA, Brooklyn, NY, Cambridge, MA, or remoteFull-TimeSenior
SalarySan Francisco, New York City: $195K - $250K
Boston, DC Metro, Los Angeles, Seattle: $185K - $240K
Denver, Atlanta, Austin, Las Vegas, Sacramento, Chicago: $175K - $230K
Texas, Florida: $165K - $220K
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Job Details
- Experience
- 8+ years of software development and infrastructure management experience; 4+ years of experience developing and deploying ML systems at scale across training and inference
- Required Skills
- AWSDockerKubernetesGoCI/CD
Requirements
- 8+ years of software development and infrastructure management experience.
- 4+ years of experience developing and deploying ML systems at scale across training and inference.
- Experience with distributed systems and microservices architecture principles.
- Experience with Linux and containerized environments such as Docker.
- Experience managing cloud computing environments such as AWS or GCP and configuring cloud services.
- Experience with Kubernetes and familiarity with running GPU or accelerated workloads on Kubernetes.
- Strong understanding of networking and internet application protocols, including TCP/IP, DNS, and HTTP.
- Strong understanding of network and application security principles and best practices.
- Familiarity or hands-on experience with configuration management and Infrastructure as Code, such as Terraform or CloudFormation.
- Familiarity or hands-on experience with monitoring and observability systems and CI/CD automation systems.
- Experience with relational and non-relational databases and familiarity with data warehousing and querying.
- Proficiency writing, testing, and profiling software in Golang, JavaScript/TypeScript, C++, Ruby, Python, or similar languages.
- Experience collaborating and communicating with internal and external stakeholders in business and technical roles.
Responsibilities
- Manage core infrastructure supporting ML and LLM workloads.
- Design, build, and maintain cloud infrastructure components using AWS, Kubernetes, in-house technology, and open-source software.
- Build and scale model serving and inference infrastructure for recommendation models and LLM workloads.
- Manage GPU and accelerator infrastructure on Kubernetes, including scheduling, capacity, quotas, and cost optimization.
- Build and maintain monitoring and observability systems for system health and model quality.
- Build and operate model evaluation infrastructure, including offline and online evaluation pipelines, regression datasets, LLM-as-judge harnesses, A/B testing, and feedback loops.
- Design, build, and maintain CI/CD systems for testing and deploying code.
- Establish tools, methods, and best practices for engineers using the ML infrastructure, and promote Infrastructure as Code.
- Work with engineers to deploy and instrument software systems.
- Evaluate, select, and integrate third-party vendor systems and work with vendors to configure and manage them.
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