Senior Applied LLM Engineer

We are a fast-moving, top tier VC funded Silicon Valley startup, building ambitious products at the intersection of web apps and modern AI systems. We are creating a next-generation personalization experience that seamlessly enhances everyday life. Our mission is to deliver truly magical, meaningful interactions through the services consumers already know and trust.

‍

We are looking for a Senior Machine Learning Engineer to play a foundational role in building and scaling our core AI platform. You will work closely with the founders, product, and engineering teams to design, build, and productionize ML systems that power highly performant, real-world applications.

‍

This is a true 0→1 role. You’ll be expected to operate with a strong sense of ownership, technical judgment, and pragmatism—choosing not just how to solve problems, but whether ML is the right solution at all.

• Design, build, and deploy production-grade ML systems, including scalable pipelines and low-latency/production-ready inference
• Build, fine-tune, or train domain language models (e.g., LLaMA, Mistral, T5) using techniques such as instruction tuning, LoRA/QLoRA, or reinforcement learning when appropriate
• Build, fine-tune and RL-train models that generate high-quality parsing and extraction logic for specific formats (including HTML and other structured / semi-structured data), where general-purpose LLMs often fall short
• Make principled decisions about the right technical approach for each problem – fine-tuning, RAG, reinforcement learning, hybrid systems, agent-style workflows, or non-ML solutions altogether
• Design, deploy, and optimize LLM-powered systems such as RAG pipelines, evaluation frameworks, and low-latency inference services
• Own the full ML lifecycle—from data collection and feature engineering through training, evaluation, deployment, monitoring, and aggressive optimization for low latency and low cost
• Collaborate closely with product and platform engineers to ship ML-powered features, mentor junior engineers, and help shape the technical direction of the ML stack

• 10+ years of experience in machine learning, applied AI, or related engineering roles
• Strong fundamentals in machine learning, statistics, and algorithms
• Demonstrated experience deploying ML models into production environments
• Deep hands-on experience with Python and ML frameworks such as PyTorch, TensorFlow, and scikit-learn
• Hands-on experience fine-tuning or training transformer-based language models (e.g., LLaMA, Mistral, T5, GPT-style) using techniques such as LoRA, QLoRA, instruction tuning, or RL-based methods
• Experience building production LLM systems such as RAG pipelines, agent workflows, or evaluation/serving infrastructure
• Strong intuition for when not to use ML, and comfort designing deterministic or heuristic-based systems when they are the better solution
• Experience optimizing systems for performance, latency, and cost in production
• Familiarity with modern ML infrastructure and data stacks (e.g., cloud platforms, feature stores, vector databases, orchestration tools)
• Ability to thrive in ambiguous, fast-moving startup environments with minimal structure

• Experience training or continuing pretraining of domain-specific language models
• Experience building or optimizing parsing, extraction, or transformation systems
• Background in personalization, search, recommender systems, or NLP
• Experience at an early-stage startup or building systems from scratch
• Prior mentorship or technical leadership experience
• Experience working with privacy-sensitive or regulated data

• Be part of the core technical team at an early-stage, well-funded AI startup
• Work directly with experienced founders and top-tier investors
• Solve hard, real-world problems where thoughtful engineering matters more than hype
• High ownership, high impact, and rapid growth opportunities
• Competitive compensation, meaningful equity, benefits, and strong long-term upside

We are backed by a top-tier Silicon Valley venture capital firm and led by two seasoned Silicon Valley entrepreneurs and executives who have scaled startups from inception to IPO, led successful exits to Fortune 500 companies, and tripled the valuation of publicly traded firms in record time.