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I am hiring for 2 roles: Technical Deep Dive Writer (System Design or AI Systems), and Lead Instructor (Building the World’s Most Useful AI Cohort). Job descriptions below.
1. Technical Deep Dive Writer
ByteByteGo started with a simple idea: explain system design clearly. Over time, it has grown into one of the largest technical education platforms for engineers, reaching millions of engineers every month. We believe it can become much bigger and more impactful.
This role is for someone exceptional who wants to help build that future by producing the highest-quality technical content on the internet.
You will work very closely with me to produce deep, accurate, and well structured technical content. The goal is not volume. The goal is to set the quality bar for how system design and modern AI systems are explained at scale.
The role is to turn technical knowledge into world class technical writing.
What you will do:
Turn complex systems into explanations that are precise, readable, and memorable.
Create clear technical diagrams that accurately represent system architecture and tradeoffs.
Collaborate directly with tech companies like Amazon, Shopify, Cursor, Yelp, etc.
Continuously raise the bar for clarity, correctness, and depth.
Who we are looking for
5+ years of experience building large scale systems
Ability to explain complex ideas without oversimplifying
Strong ownership mindset and pride in craft
Role Type: Part time remote (10-20 hours per week), with the possibility of converting to full time
Compensation: Competitive
This is not just a writing role. It is a chance to help build the most trusted technical education brand in the industry.
How to apply: If you are interested, please send your resume and a previous writing sample to jobs@bytebytego.com
2. Lead Instructor, Building the World’s Most Useful AI Cohort
Cohort Course Name: Building Production AI Systems
This cohort is focused on one of the hardest problems in modern engineering. Taking AI systems from impressive demos to reliable, secure, production ready systems used by real users.
Our learners already understand generative AI concepts. What they want and need is engineering rigor. How to evaluate models properly. How to ship safely. How to scale without blowing up cost or reliability. How to operate AI systems in the real world.
This role is for someone exceptional who has done this work in production and wants to help shape the future of AI engineering education.
Role Type: Part time remote (20+ hours per week), with the possibility of converting to full time
Compensation: Very Competitive
Responsibilities
Develop and maintain a curriculum centered on ai production
Design labs/assignments. Keep them runnable with modern tooling
Teach live sessions (lecture + hands-on lab)
Run weekly office hours
Provide clear feedback on assignments and async questions
Required expertise
Production AI Engineering
You have shipped and maintained AI features used by thousands of users. You have “war stories” regarding outages, cost spikes, or quality regressions.
Deep understanding of the FastAPI + Pydantic + Celery/Redis stack for handling asynchronous AI tasks.
Can articulate the nuances between Latency vs. Cost and Reliability vs. Velocity.
AI Evals
experience in evaluating AI systems like LLMs, RAGs, Agents, or image/video generation models)
Ability to explain standard metrics and design eval datasets (golden, adversarial, regression)
implement scoring (rules, rubrics, LLM-as-judge with guardrails)
Familiar with industry eval patterns and frameworks (e.g., OpenAI Evals)
AI security and guardrails
prompt injection,
insecure output handling, model DoS, supply chain risks.
Threat Modeling: Experience mapping threats to taxonomies like MITRE ATLAS.
Implementing input sanitization and output validation to prevent prompt injection and model DoS.
Deployment and optimization
Infrastructure: Comfort with Docker, Kubernetes, and hybrid routing (using a mix of self-hosted and managed APIs like Azure OpenAI or Bedrock).
Optimization: Hands-on experience with Quantization (FP8/INT8), Prompt Caching, Pruning, distillation, distributed inference, efficient attention variants, and batching strategies to maximize throughput.
Serving Engines like vLLM or NVIDIA TensorRT-LLM.
Comfortable with production deployment patterns (containerization, staged rollouts, canaries).
Backend: Python (Expert), FastAPI, Pydantic v2, and asynchronous programming.
Monitoring and observability
Ability to teach tracing and quality monitoring
Desirable Technical Stack Knowledge
Frameworks: LangGraph, CrewAI, or Haystack.
Databases: Vector DBs (Pinecone, Weaviate, Qdrant) and their indexing strategies (HNSW, IVFFlat).
Observability: LangSmith, Honeycomb, or Arize Phoenix.
Backend: Python (Expert), FastAPI, Pydantic v2, and asynchronous programming.
This is not just a teaching role. It is a chance to help scale the most popular AI cohort and define how production grade AI engineering is taught.
Let’s build the most popular AI cohort together.
How to Apply
Send your resume and a short note on why you’re excited about this role to jobs@bytebytego.com