{"slug": "research-engineer-afterquery", "title": "Research Engineer — AfterQuery", "summary": "AfterQuery, an applied research lab serving frontier AI labs, is hiring a Research Engineer in San Francisco with a salary range of $210k–450k/yr. The role involves designing and running training experiments to measure dataset impact on model behavior, and building post-training infrastructure for SFT, RL, and evaluation workflows. AfterQuery recently raised a $30M Series A at a $300M valuation and has grown to over $100M revenue run rate.", "body_md": "# Research Engineer\n\n- Salary\n- $210k–450k/yr\n- Location\n- San Francisco\n- Work type\n- On-site\n- Posted\n- today\n\n[Apply on company site (opens in new tab)](https://jobs.ashbyhq.com/afterquery/ae665a77-b88d-418e-941a-6c3da870f052/application)\n\nAbout AfterQuery\n\nAfterQuery is an applied research lab curating data solutions for foundation model development.\n\nWe serve every frontier AI lab with the mission of delivering the best data to power the best models. In doing so, we can make expertise that once took a lifetime to build available to anyone who needs it. Our customers are the ones building the foundation models themselves and our work sits directly in the loop of how those systems improve.\n\nThis is a rare opportunity to join a company at a defining moment in AI. Since raising our $30M Series A at a $300M valuation, AfterQuery has grown well over a $100M revenue run rate.\n\nWe're based in San Francisco and backed by leading investors including Altos Ventures, BoxGroup, and Y Combinator and angels from Google DeepMind, OpenAI, Anthropic, Meta Superintelligence Labs, and Microsoft AI.\n\nWhy Apply\n\n- Massive Opportunity:\n\nWe are one of the fastest-growing YC companies in our batch, and we believe we can become one of the fastest-growing YC companies of all time.\n\n- Founding Impact:\n\nYou will own and architect core infrastructure systems that power our platform from the ground up.\n\n- Equity & Growth:\n\nCompetitive salary and meaningful equity. As we scale, you’ll have the opportunity to shape the engineering organization and lead major technical initiatives.\n\n- Strong Team:\n\nOur founding team has experience from Citadel Securities, Meta, Google, Silver Lake, and Morgan Stanley — work alongside world-class engineers and researchers.\n\nOverview\n\nYou will design and run training experiments that isolate the impact of our datasets on model behavior. Through controlled SFT and RL - base post-training experiments, you will measure how different data sources, structures, and selection strategies affect capability, generalization, and alignment. You will own the full experiment loop: formulate the hypothesis, build the pipeline, run the model, analyze the results, identify confounders, and determine what should happen next. Working with partner labs and internal teams, you will turn our datasets into clear, defensible evidence: this data -> this improvement -> under these conditions. This is empirical, high-leverage work for someone who likes building quickly and extracting signal from noisy results.\n\nResponsibilities\n\nDesign and build our post-training infrastructure for SFT, RL and evaluation workflows.\n\nBuild reliable pipelines for data preparation, dataset versioning, sampling, training, checkpoint management, and evaluation.\n\nDevelop experiment orchestration and tracking systems that make runs reproducible, comparable, and easy to debug.\n\nCreate reusable abstractions that allow researchers to launch experiments quickly across datasets, models, and training recipes.\n\nIntegrate our systems with partner-lab training stacks, model APIs, compute environments, and evaluation infrastructure.\n\nRequired Qualifications\n\nAt least 2 years of professional experience in machine learning engineering, research engineering, ML infrastructure, or a closely related field; 2-4+ years preferred.\n\nStrong Python and software-engineering skills.\n\nHands on experience with PyTorch, JAX, Ray, and Slurm.\n\nExperience building production-quality ML training, evaluation, or data infrastructure.\n\nHAnds-on experience with LLM fine-tuning, post-training, and evaluation.\n\nAbility to build reliable, reproducible systems for launching and comparing ML experiments.\n\nStrong debugging skills across distributed systems, data pipelines, training infrastructure, and model behavior.\n\nUnderstanding of experimental design and the ability to extract actionable conclusions from noisy results.\n\nAbility to move quickly between infrastructure engineering and hands-on experimentation.\n\nA bias toward building, testing, and shipping.\n\nCompany Benefits (For Eligible Employees):\n\n- Health Insurance\n\nMedical, Vision, Dental\n\n- 401(k)\n\nWith Employer Match\n\n- Daily Meals\n\nDaily UberEats Stipend\n\n- Wellness Stipend\n\nMonthly - Covers Equinox Membership\n\n- Commute Covered\n\nWe are an equal opportunity employer committed to providing a workplace free from discrimination and harassment. Employment decisions are made without regard to legally protected characteristics under applicable federal, state, or local law. We comply with applicable pay transparency requirements and provide compensation ranges based on the position, qualifications, experience, and other relevant factors. Reasonable accommodations are available to qualified individuals with disabilities and for sincerely held religious beliefs, as required by law. This job description is intended to describe the general nature and level of work performed and is not an exhaustive list of all duties, responsibilities, qualifications, or working conditions associated with the position. We reserve the right to modify this job description as business needs change.", "url": "https://wpnews.pro/news/research-engineer-afterquery", "canonical_source": "https://frontierroles.com/jobs/afterquery-research-engineer-3feebe/", "published_at": "2026-08-27 23:18:04+00:00", "updated_at": "2026-08-28 11:20:00.498550+00:00", "lang": "en", "topics": ["ai-research", "ai-infrastructure", "ai-startups", "machine-learning"], "entities": ["AfterQuery", "Altos Ventures", "BoxGroup", "Y Combinator", "Google DeepMind", "OpenAI", "Anthropic", "Meta Superintelligence Labs"], "alternates": {"html": "https://wpnews.pro/news/research-engineer-afterquery", "markdown": "https://wpnews.pro/news/research-engineer-afterquery.md", "text": "https://wpnews.pro/news/research-engineer-afterquery.txt", "jsonld": "https://wpnews.pro/news/research-engineer-afterquery.jsonld"}}