ML Systems & Performance Engineer
Engram · San Francisco
CHECKED 4H AGO
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At a glance
Highlights
- Build personalization API powering models that learn from user context.
- Optimize low-latency serving for memory retrieval with per-user state.
- Scale distributed training across GPUs and nodes for efficiency.
What they offer
- Competitive cash compensation
- Startup equity
Ideal candidate
Systems-minded builder who thrives in ambiguity and turns research prototypes into fast, reliable production systems. You care about performance, ownership, and setting engineering bar as a founding hire.
Requirements
- Bachelor's degree or equivalent experience in computer science, engineering, or similar.
- 5+ years of experience with training or inference systems with measurable results.
- Strong engineering foundation shipping high-quality code in fast-paced environments.
- Deep understanding of ML frameworks, GPUs, distributed systems, and infrastructure.
- Operate well in ambiguous environments with real ownership.
- Bias toward action turning research concepts into executable plans.
- remote policy
- in-person
Lab profile
About Engram
Training models that study your work and form compact memories to anticipate questions.
What they build
- Continually learning models for workspace understanding
- Engrams — compact memories capturing user knowledge
Known for
- Continual learning
- Persistent memory formation
- Workspace personalization