Basis Research

ML Systems Engineer, Infrastructure & Cloud

New York Office · Full-time

Sponsorship not specifiedDetected 241 days ago

Cloud PlatformsMachine LearningPyTorchFirewallResearchCommunication

About the role

ML Systems Engineers at Basis ensure training and evaluation infrastructure is fast, reliable, and scalable.
We are looking for engineers who combine deep understanding of ML systems with operational excellence.
You will be the guardian of training stability, the optimizer of compute costs, and the enabler of reproducible research.

Own distributed training infrastructure including job launchers, checkpointing systems, recovery mechanisms, and monitoring that ensures experiments run reliably at scale.
Profile and optimize training performance by identifying bottlenecks in data loading, gradient computation, communication overhead, and implementing solutions that improve step time.
Manage cloud infrastructure and costs including capacity planning, spot instance strategies, storage optimization, and building tools that give researchers visibility into resource usage.
Implement security and compliance measures including access controls, data encryption, audit logging, and ensuring infrastructure meets requirements for handling sensitive data.
Build evaluation and benchmarking infrastructure that enables consistent, reproducible measurement of model performance across different conditions and datasets.
Maintain development environments including containerization, dependency management, and tools that ensure researchers can reproduce results across different systems.
Document and share knowledge through runbooks, post-mortems, and training materials that help the team understand and operate ML infrastructure effectively.
Collaborate with researchers to understand requirements, suggest infrastructure solutions, and ensure systems support rather than constrain research goals.

The second is to advance society's ability to solve intractable problems.
This means expanding the scale, complexity, and breadth of problems that we can solve today, and even more importantly, accelerating our ability to solve problems in the future.
Experience with on-premise GPU cluster management.
Knowledge of optimization theory and numerical methods.
Possess deep knowledge of distributed training frameworks including PyTorch/JAX distributed strategies (DDP, FSDP, ZeRO), gradient accumulation, mixed precision training, and checkpoint/recovery systems.

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Competitive salary.

Develop monitoring and alerting systems that detect anomalies in training metrics, resource utilization, or system health, enabling rapid response to issues.

We are in the office four days a week.
Be prepared to attend multi-day Basis-wide in-person events.
In-person Policy: We are in the office four days a week. Be prepared to attend multi-day Basis-wide in-person events.

This listing is sourced directly from Basis Research's careers page and normalized into a canonical job model.