Leads a team optimizing ML model inference and performance, focusing on frameworks like PyTorch, TensorRT, and CUDA. Requires 5+ years software engineering experience with 2+ years technical leadership, plus expertise in production AI/ML scaling.
260k – 380k/yr
Hybrid5+ YOEEngineering Management
About the role
Responsibilities
Lead, mentor, and manage a team of engineers focused on developing and optimizing ML model inference and performance.
Oversee technical strategy and architecture decisions, driving improvements across our engineering organization.
Collaborate with cross-functional teams to ensure seamless integration and scalability of ML models in production environments.
Dive into the codebase of frameworks like TensorRT, PyTorch, CUDA, and others to identify and solve complex performance bottlenecks.
Drive the development and deployment of large-scale optimization techniques for various ML models, especially large language models (LLMs).
Own the full lifecycle of projects from inception through delivery, including planning, execution, and resource management.
Foster a collaborative, inclusive team environment that encourages continuous learning and growth.
Requirements
Bachelor’s, Master’s, or Ph.D. in Computer Science, Engineering, or a related field.
5+ years of professional experience in software engineering, with at least 2 years in a technical leadership role.
Proven experience managing and mentoring teams of engineers.
Expertise in one or more programming languages, such as Python, C++, or Go.
In-depth understanding of ML model performance optimization, especially using libraries such as PyTorch, TensorRT, and CUDA.
Strong knowledge of containerization (Docker) and orchestration systems (Kubernetes).
Experience with production-level AI/ML solutions, including scaling and deploying large models.
Ability to balance hands-on technical work with team leadership and project management.
Bonus Points
Experience enhancing the performance of large language models (LLMs) or similar AI systems.
Familiarity with LLM optimization techniques such as quantization, speculative decoding, or continuous batching.
Deep knowledge of GPU architecture and performance tuning.
Previous experience in a high-growth startup environment.
Benefits
Competitive compensation, including meaningful equity.
100% coverage of medical, dental, and vision insurance for employee and dependents.
Generous PTO policy including company wide Winter Break.
Paid parental leave.
Company-facilitated 401(k).
Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.
Leads and mentors Forward Deployed Engineers building and optimizing LLM inference for customers, while hands-on contributing to product features and customer engagements. Requires 4+ years software engineering with Python/ML expertise and 1+ year leadership.
260k – 380k/yr
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