Job Description

🤖 Role Overview — GenAI Focus

This is a hybrid Data Scientist + Machine Learning Engineer position focused heavily on:

• Large Language Models (LLMs)
• Diffusion models (Stable Diffusion–type systems)
• Synthetic data generation
• End-to-end ML pipelines

It sits closer to Applied AI Engineer than traditional Data Scientist.

Location: Cupertino, California (USA)
Experience Required: 2+ years
Education: Bachelor’s in CS or related field

🧠 What You’ll Actually Do

1️⃣ Generative AI Development (Core Work)

You’ll:

• Fine-tune LLMs
• Build diffusion-based models
• Optimize GenAI architectures
• Apply models to real business use cases

This is cutting-edge AI work.

2️⃣ Synthetic Data Pipelines

One of the most valuable parts:

• Design pipelines using LLMs to generate synthetic datasets
• Improve training data quality
• Automate dataset creation

Synthetic data is a high-growth AI domain right now.

3️⃣ ML Engineering & Deployment

Responsibilities include:

• Model deployment
• Scalable training pipelines
• Distributed computing workflows
• Automation tooling

So this is not just research — it’s production engineering.

4️⃣ Cross-Functional Collaboration

You’ll work with:

• Research teams
• Data engineers
• Product teams
• Program managers

This means strong communication is required.

🔧 Required Technical Skills

Must-Have

• Python (expert level)
• PyTorch (or similar deep learning frameworks)
• Machine learning fundamentals
• Data preprocessing & evaluation
• LLM experience
• Diffusion model experience

This already places the role at mid-level AI engineer.

Bonus Skills (Implied)

Even if not listed, success often requires:

• GPU training optimization
• Hugging Face ecosystem
• Distributed training (DDP, DeepSpeed)
• Cloud ML platforms
• Vector databases
• Prompt engineering

📊 Seniority Level — Important

Despite only “2+ years” mentioned, the skill depth suggests:

👉 Mid-level ML Engineer / Applied Scientist

Not entry level.

Companies often underestimate experience requirements in GenAI postings.

💰 Salary Expectation (Typical Market)

For Cupertino / Silicon Valley:

Estimated range:

• $130k — $180k base
• Higher if contract through major client
• Could exceed $200k with bonuses/equity (depending on end client)

🚀 Career Value

This role is extremely valuable for future growth:

After 2–3 years you could move to:

• Senior ML Engineer
• Applied Scientist
• GenAI Engineer
• AI Research Engineer
• Staff AI Engineer

GenAI experience is currently among the highest-paid tech skills globally.

⭐ Strengths of This Role

✅ Cutting-edge AI domain
✅ LLM + diffusion exposure
✅ Synthetic data expertise
✅ Production ML experience
✅ Silicon Valley ecosystem
✅ High career leverage

⚠️ Challenges

❗ Fast-paced environment
❗ High technical expectations
❗ Requires strong math + engineering
❗ Likely heavy experimentation cycles
❗ Work authorization required (USA)

🆚 Compared to Traditional Data Scientist Roles

Feature	This Role	Traditional DS
AI Depth	Very High	Medium
Engineering	High	Low–Medium
Statistics	Medium	High
Coding	Very High	Medium
Salary	Higher	Lower
Demand	Exploding	Stable

🌎 Who Should Apply

Good fit if you:

✔ Have ML project experience
✔ Built LLM / GenAI projects
✔ Know PyTorch well
✔ Want AI engineering career
✔ Enjoy research + coding mix

Not ideal if:

❌ You prefer business analytics
❌ You lack deep learning experience
❌ You want beginner roles

📈 Market Insight (Important)

GenAI roles are evolving into 3 categories:

1. Prompt / Application Engineers (low barrier)
2. Applied ML Engineers (this role)
3. Research Scientists (PhD heavy)

This job is category #2 — best long-term ROI.

👍 My Honest Assessment

This is a very strong opportunity if you qualify technically.

It offers:

• High salary trajectory
• Future-proof skills
• Strong industry relevance
• Career acceleration

Much more powerful than typical data analyst jobs.