ML Systems & Performance Engineer
Engram · San Francisco
About this role
## ML Systems & Performance Engineer ### About Engram Today’s AI can solve hard problems, but it often doesn’t truly understand *you* or your work. Engram trains models to study your context and anticipate your questions in advance—creating **compact memories (“engrams”)** that capture knowledge and history. This enables a new axis of scaling: the more we study your context during training, the better the model performs during inference. Engram is working with leaders in AI (e.g., Microsoft, Notion, Harvey) and recently raised **$98M**. ### About This Role You’ll be an early **ML Systems Engineer** on a team of machine learning researchers and performance engineers building a **personalization and continual learning API**. This API powers models and agents that learn from user context. Your focus will be to **design, optimize, and scale training and inference workloads**, bridging cutting-edge research and production systems. **You will: ** - Design and execute new frameworks, techniques, and systems to improve **performance, reliability, latency, and efficiency**. - Partner with researchers to turn prototypes into systems that run at scale, and feed system constraints back into research decisions. - Optimize serving paths for **personalization and memory retrieval**, balancing per-user state with **low latency**. - Work on **distributed training** (data/model parallelism, communication scheduling, and scaling efficiency across multiple GPUs and nodes). - Serve as a bridge between researchers and platform engineering, collaborating closely with AI-native customer teams. This is a **founding hire** role—helping set the engineering bar (code review, testing, on-call, and security posture) and shaping engineering culture as the team grows. ### Your Background - Bachelor’s degree (or equivalent experience) in CS, engineering, or related field. - **5+ years** experience building **training and/or inference systems** with measurable performance improvements. - Strong engineering foundation; proven ability to ship high-quality code in fast-paced, complex environments. - Deep understanding of **ML frameworks** (e.g., PyTorch, JAX), **GPUs**, **distributed systems**, and infrastructure. - Comfortable operating in ambiguity with real ownership. - Ability to turn research ideas into concrete, executable plans. ### Bonus Points - Prior early-stage experience. - Experience with open-source ML or ML systems infrastructure. ### Location & Compensation - **San Francisco (in-person)** - Competitive cash compensation and startup equity. Engram is an **equal opportunity employer** and welcomes applicants regardless of race, color, religion, national origin, gender, gender identity, sexual orientation, age, disability, or veteran status.
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