Customer Success Engineer (CXE)

Higher People; New York (NY); On-site; USD 100,000 - 140,000; Full time

Benefits offered by this job

Health Benefits

Equity

Competitive salary

Job summary

Higher People is building AI-powered tools for personalized K-12 learning paths. As a CS Engineer you will own core systems turning models into classroom-ready experiences used by thousands of students daily.

Your code will influence whether a reader gets the right next problem and give teachers actionable insights. You will work directly with the CTO and a compact team on a stack including Python, FastAPI, PyTorch, Postgres, Redis, and AWS.

Qualifications

• Shipped production ML or high-scale backend systems with real user traffic.
• Deep Python + systems skills (concurrency, caching, data modeling).
• Experience building or integrating recommendation, NLP, or generative systems with evaluation loops.
• Track record of pragmatic trade-offs under resource constraints typical of seed-stage companies.
• Genuine curiosity about how kids learn and willingness to validate assumptions in classrooms.

Responsibilities

• Deliver production-grade AI inference and personalization services handling 10k+ concurrent sessions with sub-200ms p95 latency within 90 days.
• Design data pipelines ingesting student interactions, embeddings, and feed the recommendation engine.
• Reduce model-serving cost per student through quantization, caching, and efficient batching without quality loss.
• Own end-to-end delivery of two high-impact features from prototype to 95%+ uptime.
• Establish observability, evaluation harnesses, and A/B testing infrastructure to measure learning outcomes.
• Mentor one junior engineer and document architecture decisions to scale the team.

Skills

ML systems

Python

Concurrency

Caching

Data modeling

NLP

Recommendation systems

Evaluation loops

Seed-stage startup

Git & CI

Tools

Postgres

Redis

AWS

PyTorch

FastAPI

Docker

Job description

CS Engineer

Flint is building AI that gives every K-12 student a truly personalized learning path while giving teachers superpowers. As CS Engineer you will own the core systems that turn our models into classroom-ready experiences used by thousands of students every day. Your code will directly decide whether a struggling reader gets the right next problem or a teacher gets the insight that changes a lesson.

Performance Objectives

• Ship production-grade AI inference and personalization services that serve 10k+ concurrent student sessions with <200ms p95 latency within your first 90 days.


• Design and implement the data pipelines that ingest student interactions, generate embeddings, and feed our recommendation engine so teachers see real-time mastery maps.


• Reduce model-serving cost per student by 40% through quantization, caching, and efficient batching while maintaining pedagogical quality.


• Own end-to-end delivery of at least two high-impact features (adaptive practice, automated feedback, or teacher copilot) from prototype to 95%+ uptime in production.


• Establish observability, evaluation harnesses, and A/B infrastructure so we can measure learning-outcome lift, not just click-through.


• Mentor one junior engineer and document architecture decisions so the team can scale without you becoming a bottleneck.

Environment & Resources

You report directly to the CTO and work with a tight 8-person product-engineering team (ML, full-stack, design). Stack: Python, FastAPI, PyTorch, Postgres, Redis, AWS. You will have cloud budget, GPU access, and autonomy to choose tools that actually move student outcomes. Early-stage means you will sit in every product conversation and see your work in classrooms within weeks.

Essential Qualifications

• Shipped production ML or high-scale backend systems that handled real user traffic and measurable business/learning metrics.


• Deep Python + systems skills (concurrency, caching, data modeling) and comfort owning services from laptop to production.


• Experience building or integrating recommendation, NLP, or generative systems with evaluation loops, not just notebooks.


• Track record of making pragmatic trade-offs under resource constraints typical of a seed-stage company.


• Genuine curiosity about how kids learn and willingness to sit in classrooms or talk to teachers to validate assumptions.

Role Selling Points

• Direct line of sight from your commit to a student who finally "gets it".


• Green-field architecture decisions that will define the next decade of the product.


• Tiny team, huge ownership---your work is 10--20% of the entire platform.


• Mission that compounds: every improvement multiplies across thousands of classrooms.


• Compensation: $100-140k salary, Equity in the company, Health Benefits