Senior Infrastructure Engineer (Backend/Data Performance)
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Infrastructure Engineer (Backend/Data Performance) based in the United States.
This role sits at the intersection of high-performance infrastructure engineering and mission-driven AI research, supporting systems that power large-scale animal communication modeling. You will design and optimize the core backend and data infrastructure that enables researchers to process, store, and analyze complex multi-species datasets efficiently and reliably. The work involves building scalable pipelines and distributed systems that directly accelerate AI experimentation and model training. You will focus heavily on performance engineering, improving latency, throughput, GPU utilization, and system reliability across production-grade ML workflows. Partnering closely with researchers, ML engineers, and external collaborators, you will translate experimental needs into robust, scalable infrastructure. The environment is highly collaborative and research-intensive, blending cutting-edge AI development with real-world scientific impact. This is a high-ownership role where your contributions directly shape the speed and effectiveness of breakthrough research.
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Infrastructure Engineer (Backend/Data Performance) based in the United States.
This role sits at the intersection of high-performance infrastructure engineering and mission-driven AI research, supporting systems that power large-scale animal communication modeling. You will design and optimize the core backend and data infrastructure that enables researchers to process, store, and analyze complex multi-species datasets efficiently and reliably. The work involves building scalable pipelines and distributed systems that directly accelerate AI experimentation and model training. You will focus heavily on performance engineering, improving latency, throughput, GPU utilization, and system reliability across production-grade ML workflows. Partnering closely with researchers, ML engineers, and external collaborators, you will translate experimental needs into robust, scalable infrastructure. The environment is highly collaborative and research-intensive, blending cutting-edge AI development with real-world scientific impact. This is a high-ownership role where your contributions directly shape the speed and effectiveness of breakthrough research.
Accountabilities:
- 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.
- 5+ years of experience in backend engineering, infrastructure engineering, or data systems roles.
- Strong Python programming skills, with additional experience in lower-level languages considered a plus.
- 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.
- Bonus: Knowledge of CUDA, TPU optimization, HPC systems, or tools like Slurm; experience with monitoring stacks such as Prometheus or Grafana.
- Competitive annual salary range of $225,500 – $235,500
- Full medical, dental, and vision insurance coverage (100% employer-paid)
- 401(k) retirement plan with employer match (U.S. based employees)
- $2,000 home office stipend
- Unlimited paid time off with a recommended minimum of three weeks annually
- Flexible working hours and remote-friendly structure
- Regular global team retreats and collaboration opportunities
- Inclusive, mission-driven culture focused on scientific and AI innovation.