AI/ML Infra / Systems Engineer (Inference)
- Compensation
- Not disclosed
- Location
- Seattle, WA or West Coast Remote Preferred
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Ml / AI
- Posted
Skills
Must have
- Five or more years in infrastructure, systems, or ML infrastructure engineering.
- Hands-on experience serving ML or LLM models in production at scale.
- Deep understanding of latency, throughput, and performance optimization.
- Experience with GPUs or accelerators and their efficient use.
- Strong systems programming and distributed systems fundamentals.
- A track record of reliable, cost-aware production systems.
- Fluency with observability and operational excellence.
Nice to have
- Experience with inference-serving frameworks and model runtimes.
- Experience optimizing LLM inference, including batching, quantization, and caching.
- Familiarity with retrieval systems and their performance characteristics.
- Experience owning cost and capacity for AI workloads.
- Exposure to neuro-symbolic or agentic systems in production.
Full description
About Sapience AI
Sapience AI is the collective intelligence platform for professional communities. We sit above the CRMs, AMS platforms, and knowledge bases that organizations already run, and we turn the expertise scattered across them into something every member can search, act on, and share.
The intelligence a community needs is already inside it. Most organizations just cannot reach it. Knowledge lives in silos, in legacy systems, in the heads of a few experts, and in fragmented records no one can connect. We change that.
Our work is grounded in four commitments: technology elevates people and never replaces them, the best expertise is already inside the community, everything is built on trust, and every deployment is purpose-driven for the organization it serves.
Let’s achieve more, together.
Where this role sits
This role owns how Sapience AI serves intelligence at scale. You build and run the inference infrastructure that turns the models and reasoning behind MINERVA and COGENT into fast, reliable, affordable answers for members.
You work where models meet production: serving, scaling, latency, and cost. Every time a member asks the platform something, your systems are what make the answer arrive quickly and hold up under load.
You sit close to applied AI, research, and platform engineering, and you make the difference between a model that works in a notebook and one that serves a community reliably.
Why this role exists
Collective intelligence is only useful if it is fast and dependable. Members will not wait, and communities cannot rely on a platform that buckles under load or costs too much to run.
Serving modern AI at scale is a hard systems problem: large models, tight latency budgets, expensive hardware, and demand that spikes. It takes real infrastructure engineering to get right.
The AI/ML Infrastructure and Systems Engineer owns that problem. You make inference fast, reliable, and affordable, so the intelligence the platform promises actually reaches members.
What you will own (Areas of Responsibility)
You hold seven areas of responsibility across inference infrastructure. Each one is yours to set direction on, build, and measure.
1. Inference serving and systems
- Build and operate the inference systems that serve models and reasoning behind MINERVA and COGENT.
- Own the serving path end to end, from request to response, under real load.
- Make serving robust to failure, traffic spikes, and change.
2. Latency and performance
- Drive down latency so members get fast answers, and keep it low as the platform grows.