Machine Learning Engineer

As one of our core machine learning engineers, you'll sit at the intersection of applied ML and infrastructure, building the models and systems that power how frontier AI labs develop their next generation of models. You will be one of our first hires, and will get a lot of responsibility, owning entire projects end-to-end – meaning you can see your impact instantly.

Total Compensation: $200K - $500K

Company Overview

AfterQuery is a research lab investigating the boundaries and capabilities of artificial intelligence through novel datasets and experimentation. Our customers are the foundation model labs, and we serve all of the frontier AI labs.

We are based in San Francisco, CA, and have raised funding from top investors, including Y Combinator and BoxGroup, ex-partners from Lightspeed and Index Ventures, and senior leadership at Google DeepMind and Meta GenAI.

Our founding team brings backgrounds from Jane Street, Meta, Citadel Securities, Google, Goldman Sachs, Morgan Stanley, Silver Lake, Berkeley Artificial Intelligence Research (BAIR), and Stanford Artificial Intelligence Laboratory (SAIL).

Why Apply

• Massive Opportunity: We were one of the fastest growing YC companies in our batch, and we believe we can be one of the fastest growing YC companies of all time
• Founding role—you'll define and own our ML systems from day one.
• Equity & Growth – Competitive salary equity. As we scale, you'll have the opportunity to lead and grow a machine learning team.
• Strong Team: Founding team has experience from Citadel Securities, Meta, Google, Silver Lake, and Morgan Stanley – get to work with the best :)
  
  

Responsibilities

• Research, train, and productionize ML models for data quality scoring, evaluation prediction, and search relevance
• Build and scale backend infrastructure and APIs to serve ML models reliably under heavy throughput
• Run experiments, analyze results, and iterate quickly to improve both models and product performance
• Collaborate with the founding team to translate research goals into model-driven systems and ship fast
• Wear many hats: from infrastructure engineer to applied ML practitioner to product problem-solver
• Set ML engineering best practices and lay the groundwork for future team growth
  
  

Required Qualifications

• Strong proficiency in Python, with demonstrated experience training and deploying ML models in production
• Experience building backend infrastructure and APIs that serve models at scale (GCP/AWS)
• Familiarity with modern ML frameworks (PyTorch, JAX, or TensorFlow) and experiment tracking tools
• Statistical rigor—comfortable designing experiments, interpreting results, and making data-driven decisions
• Background in AI research, LLM evaluation, NLP, or human-in-the-loop systems
• Experience getting your ideas into production (founder experience preferred)
  
  

Preferred Qualifications

• Experience with LLM fine-tuning, RLHF, or other alignment techniques
• Familiarity with vector databases and embedding-based retrieval systems
• Experience building recommendation, ranking, or scoring systems at scale
• Publications or research contributions in ML, NLP, or related fields
• Background working with large-scale unstructured datasets
• Experience at a fast-paced startup or research lab environment
  
  

Compensation Range: $200K - $500K