Job Details
Applied AI Engineer
2025-08-01 Vultron San Francisco,CA
Description:
We're applying generative AI and agentic systems to one of the most high-stakes, high-friction domains in the world. As our first AI Researcher, you'll tackle problems that go far beyond the benchmarks.
Model Quality in Real-World Workflows
Improving synthesis, summarization, grounding, and structured reasoning in a government contracting and proposal context.
Custom Evaluation and Feedback Loops
Designing robust ways to evaluate relevance, completeness, hallucination risk, and user-centric performance.
Latency, Scale, and Control
Balancing performance and cost across open and closed models while building a testbed for fine-tuning and long-context optimization.
Role
As a key member of the Vultron applied research team, you will:
Advanced LLM Applied Research
Experience fine-tuning and prompting large models — particularly agentic or task-specific systems — and improving performance through grounding, context window optimization, or RAG.
Deep Expertise in AI Model Development
Experience researching and building LLM-based systems, including both training and deployment.
Strong Publication Record
Significant contributions to the AI field via top-tier conferences or open-source projects.
Excellent Communication & Collaboration
Proven ability to work closely with engineers, product teams, and non-technical experts to ship fast and stay aligned.
Preferred Experience
Applied AI Engineer
Why Vultron
Vultron is building the agentic operating system for federal growth. As an early member of the team, you'll be part of a transformative company from its early stages.
- Exceptional Market Demand: Secured significant contracts with top government and defense contractors across the world.
- World-Class Team: ex-Anduril, Robinhood, Google, Amazon, DoD, etc. Founding team includes early members at $1B+ startups in the defense sector.
- Competitive Compensation: Industry-leading salary and equity offerings.
We're applying generative AI and agentic systems to one of the most high-stakes, high-friction domains in the world. As our first AI Researcher, you'll tackle problems that go far beyond the benchmarks.
Model Quality in Real-World Workflows
Improving synthesis, summarization, grounding, and structured reasoning in a government contracting and proposal context.
Custom Evaluation and Feedback Loops
Designing robust ways to evaluate relevance, completeness, hallucination risk, and user-centric performance.
Latency, Scale, and Control
Balancing performance and cost across open and closed models while building a testbed for fine-tuning and long-context optimization.
Role
As a key member of the Vultron applied research team, you will:
- Build and improve model-powered product features that help federal contractors reason faster and act with confidence.
- Enhance grounding, summarization, and generation systems across Vultron's modular proposal workflows.
- Develop and refine retrieval systems using RAG, vector search, and knowledge graphs.
- Design evaluation pipelines that go beyond BLEU or ROUGE — measuring utility, accuracy, and synthesis quality in noisy, complex documents.
- Work closely with federal domain experts to translate expertise into scalable model strategies.
- Conduct experiments with fine-tuning, synthetic data generation, and in-context learning to improve performance across retrieval and summarization tasks.
Advanced LLM Applied Research
Experience fine-tuning and prompting large models — particularly agentic or task-specific systems — and improving performance through grounding, context window optimization, or RAG.
Deep Expertise in AI Model Development
Experience researching and building LLM-based systems, including both training and deployment.
Strong Publication Record
Significant contributions to the AI field via top-tier conferences or open-source projects.
Excellent Communication & Collaboration
Proven ability to work closely with engineers, product teams, and non-technical experts to ship fast and stay aligned.
Preferred Experience
- Ph.D. or MS in Computer Science, Machine Learning, or a related field (or equivalent industry experience).
- 5+ years of experience in ML research, applied LLMs, or model infrastructure.
- Experience with RAG pipelines and evaluation frameworks like RAGAS, DSPy, or LLaMAEval.
- Experience with scalable LLM systems using LangChain, LlamaIndex, Hugging Face, or similar frameworks.
- Experience designing or running fine-tuning experiments
- Familiarity with GPU-based experimentation and cloud model deployment (e.g., vLLM, DeepSpeed, Ray Serve).
- Prior work in high-trust domains is a plus.
Seniority level
Seniority level
Mid-Senior level
Employment type
Employment type
Full-time
Job function
Job function
Engineering and Information TechnologyIndustries
Software Development
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