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Responsibilities
- Design and implement scalable systems-level software components that support large-scale ML training and inference workloads across distributed compute infrastructure
- Identify and resolve performance bottlenecks in ML pipelines through profiling, instrumentation, and targeted optimization of compute, memory, and I/O utilization
- Develop and maintain high-quality, well-tested software for ML runtime systems, including scheduling, resource management, and execution frameworks
- Collaborate with AI researchers and hardware teams to co-design software solutions that efficiently map deep learning model architectures onto accelerated hardware platforms
- Own the technical design of systems components, evaluating trade-offs across correctness, performance, reliability, and maintainability
- Establish and monitor service-level objectives and performance metrics for ML infrastructure systems, building dashboards and alerting to reduce mean time to mitigation during incidents
- Drive adoption of engineering best practices including automated testing, staged rollout strategies using feature flagging and A/B testing frameworks, and comprehensive documentation
- Mentor other engineers on the team by identifying growth areas, sharing technical expertise, and contributing to code and design reviews
- Partner with cross-functional stakeholders including product, data science, and operations teams to translate ML research requirements into production-ready systems
- Proactively identify opportunities to improve developer tooling, automation, and engineering efficiency across the ML systems organization
Minimum Qualifications
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
- 6+ years of experience in software engineering with a focus on systems software, ML infrastructure, or distributed computing
- Experience designing and optimizing software systems for large-scale machine learning training or inference workloads
- Experience with performance analysis and optimization techniques including profiling, bottleneck identification, and low-level systems tuning
- Experience building reliable, production-grade distributed systems with well-defined observability, monitoring, and incident response practices
- Experience communicating technical decisions through design documents, architectural proposals, or cross-functional stakeholder alignment
Preferred Qualifications
- Experience with ML frameworks such as PyTorch, TensorFlow, or JAX, including framework internals, operator development, or runtime optimization
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
- Experience with compiler technologies or graph-level optimizations for deep learning, such as MLIR, LLVM, TVM, or XLA
- Experience with GPU or AI accelerator programming models such as CUDA, ROCm, or vendor-specific kernel libraries
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
- Experience applying responsible AI practices in systems design, including bias mitigation, model quality evaluation, or AI risk assessment
$154,003/year to $217,000/year + bonus + equity + benefits
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