🚀 AI/ML Engineer (Applied AI - Government & Enterprise)
Location: Hybrid 3 days/week on-site in San Francisco or NYC
Compensation: $200,000 – $350,000 Base Salary + Competitive Equity
Visa Status: Open to US Visa Transfers (OPT, H-1B, etc.)
🌟 The Company
Our client is an elite, high-growth applied AI startup ($55M Series A backed by top-tier tech leaders including Andrej Karpathy, Patrick Collison, and Elad Gil).
In less than two years, they have grown to over 140 employees and hit $20M+ in revenue by deploying production-grade AI systems directly into high-stakes environments—automating complex, real-world workflows for international governments, healthcare systems, and Fortune 500 energy leaders.
💡 The Role
This is an applied, product-driven AI engineering role. You will bridge the gap between cutting-edge LLM research and real-world deployment, building AI agents, reasoning systems, and complex data pipelines that solve critical, manual problems globally.
You will own projects end-to-end—from post-training and prompt engineering down to production code and direct engagement with government officials and enterprise leaders.
🎯 Key Responsibilities
• Design & Deploy: Build and ship advanced LLM architectures, AI agents, and RAG systems into production environments for global nation-states and enterprise clients.
• Optimize & Scale: Build scalable data pipelines, design robust ML eval frameworks, and optimize models for real-world reliability and accuracy.
• Direct Engagement: Interact directly with customer leadership and government stakeholders to understand domain challenges and deliver custom AI solutions.
• Full-Stack Impact: Wear multiple hats across software engineering, product direction, and customer engagement in a high-velocity startup setting.
💻 Tech Stack
• Languages & Frameworks: Python, PyTorch, JAX, TensorFlow
• AI/ML Architecture: LLMs, RAG, AI Agents, Reasoning Models, Data Pipelines
• Eval & Testing: Modern ML Evaluation Frameworks, CoderPad
🛠️ What We're Looking For
• 3 – 10 Years Experience: Applied, product-focused AI/ML engineering background (building production applications in Python).
• Applied Product Focus: Hands-on experience deploying LLMs, RAG, or AI agents to external end-users (this is NOT a pure research, MLOps, or platform infra role).
• Proven Business Impact: Ability to clearly articulate and quantify the commercial or operational impact of your ML systems (e.g., revenue generated, time saved, accuracy gains).
• Startup Credential: Experience in high-velocity startup environments (e.g., Glean, Cohere, Together AI, Databricks) or fast-paced product teams at select tech firms (e.g., DoorDash, Amazon, TikTok, Stripe). Ex-founders and founding engineers are highly valued.
• Education: BSc/MSc in Computer Science (top CS programs preferred for junior/mid-level profiles).
🔴 Red Flags / Out of Scope
• Purely research-heavy or PhD-focused profiles with no product/production shipping experience.
• MLOps, platform, or infrastructure-only engineers.
• Candidates exclusively from traditional corporate/legacy engineering cultures (e.g., Oracle, Salesforce, big banks).
🎁 Why Join
• Explosive Growth: Joined a 140-person team that hit $20M+ revenue in year one.
• Elite Backing: Backed by legendary Silicon Valley founders and investors ($55M Series A).
• Real-World Footprint: Your code directly powers critical government, energy, and healthcare infrastructure globally.
• End-to-End Autonomy: High accountability, zero bureaucracy, and high-impact equity.
🧩 Interview Process
1. Recruiter Screen (30 mins): High-level screen with the internal team assessing background, startup velocity fit, communication skills, and project impact.
2. ML Technical Interview (60 mins): Real-world ML problem-solving session testing how you translate a business scenario into an ML problem, define evaluation metrics, and architect the solution.
3. Live Coding Interview (60 mins): Virtual CoderPad session testing Python fundamentals, debugging, and practical engineering skills (applied, non-Leetcode style problem; AI tools allowed).
4. Onsite Interview (3.5 Hours):
• Two technical screen rounds
• Past project deep-dive with an Engineering Manager (evaluating startup pace, technical depth, and cross-functional collaboration)
• Lunch, office tour, and culture alignment chat with the team