Engineering Manager, ML Acceleration
Engineering Manager leading teams to optimize ML inference and training systems, remove bottlenecks, and scale compute resources efficiently. Requires 1+ years management experience in performance/distributed systems and ML/AI background.
About the job
Responsibilities
- Provide front-line leadership of engineering efforts to improve model performance and scale our inference and training systems
- Become familiar with the team’s technical stack enough to make targeted contributions as an individual contributor
- Manage day-to-day execution of the team's work
- Prioritize the team’s work and manage projects in a highly dynamic, fast paced environment
- Coach and support your reports in understanding, and pursuing, their professional growth
- Maintain a deep understanding of the team's technical work and its implications for AI safety
You may be a good fit if you
- Have 1+ years of management experience in a technical environment, particularly performance or distributed systems
- Have a background in machine learning, AI, or a similar related technical field
- Are deeply interested in the potential transformative effects of advanced AI systems and are committed to ensuring their safe development
- Excel at building strong relationships with stakeholders at all levels
- Are a quick learner, capable of understanding and contributing to discussions on complex technical topics
- Have experience managing teams through periods of rapid growth and change
- Are a quick study: this team sits at the intersection of a large number of different complex technical systems that you’ll need to understand (at a high level of abstraction) to be effective
Strong candidates may also have experience with
- High performance, large-scale ML systems
- GPU/Accelerator programming
- ML framework internals
- OS internals
- Language modeling with transformers
Annual Salary: $500,000 — $850,000 USD
Skills
Machine Learning, Distributed Systems, Performance Optimization, Gpu Programming, Ml Frameworks, Transformers, Os Internals, Large-Scale Ml Systems, Inference Systems, Training Systems
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