Senior Infrastructure Engineer (Backend/Data Performance)
New
Based in the United StatesFull-TimeSenior
Salary$225,500 – $235,500
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Job Details
- Experience
- 5+ years
- Required Skills
- AWSDockerPythonGCPKubernetesPyTorchAzureTerraformDistributed Systems
Requirements
- 5+ years of experience in backend engineering, infrastructure engineering, or data systems roles.
- Strong Python programming skills.
- Experience with distributed systems and cloud platforms (AWS, GCP, or Azure).
- Hands-on experience with Docker, Kubernetes, and infrastructure-as-code tools such as Terraform.
- Proven experience building or supporting production-grade ML or AI infrastructure.
- Strong understanding of high-performance data systems such as Spark, DuckDB, or Delta Lake.
- Experience optimizing large-scale machine learning or distributed training workloads.
- Familiarity with ML frameworks such as PyTorch or JAX and platforms like SageMaker or Vertex AI.
- Experience mentoring engineers, interns, or researchers and breaking down complex technical work.
- Ability to communicate technical concepts clearly and collaborate across research and engineering teams.
- Experience contributing to technical hiring and evaluating engineering talent.
Responsibilities
- Design, build, and optimize high-performance data pipelines supporting distributed AI training, experimentation, and large-scale data processing.
- Develop and maintain backend infrastructure for scalable storage, retrieval, and processing of complex biological and audio datasets.
- Improve system performance across latency, throughput, GPU utilization, and overall computational efficiency.
- Build monitoring, observability, and visualization tools to track pipeline health, data quality, and experiment performance.
- Support distributed machine learning workloads using tools such as Arrow, DuckDB, BigQuery, and vector databases.
- Optimize and maintain cloud-based infrastructure across AWS, GCP, or Azure environments.
- Collaborate with researchers, engineers, and external partners to translate experimental requirements into scalable engineering solutions.
- Scope, structure, and guide technical projects to enable collaboration with interns, PhDs, and research contributors.
- Contribute to infrastructure strategy, system design decisions, and scaling plans for future research growth.
- Participate in hiring processes and help shape the growth of the engineering team.
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