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Applied AI Engineer
Applied AI EngineerVultron • San Francisco, CA, United States
Applied AI Engineer

Applied AI Engineer

Vultron • San Francisco, CA, United States
[job_card.30_days_ago]
[job_preview.job_type]
  • [job_card.full_time]
[job_card.job_description]

Join to apply for the Applied AI Engineer role at Vultron

Vultron is an early-stage company building at the intersection of cutting‑edge AI and real‑world business impact. We are a small, fast‑moving team where every person has a direct impact and the ability to shape both the product and the culture. Joining now means getting in on the ground floor of a company with bold ambitions and the resources to pursue them. If you are motivated by ownership, speed, and the chance to make your work matter right away, Vultron is the place to do it.

At Vultron , we value curiosity, creativity, and collaboration. We move quickly, but we also take the time to learn from each other and celebrate wins along the way. You will be surrounded by people who are deeply passionate about building something new and are committed to supporting one another as we grow. We believe the best work happens when people feel trusted, empowered, and excited to bring their ideas to life.

Vultron is bringing general intelligence to government contracting. As an early member of the team, you’ll be part of a transformative company from its early stages.

Company Highlights

  • Exceptional Market Demand : Secured significant contracts with 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.

Challenges

  • 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

  • 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.

    • Qualifications

  • Experience fine‑tuning and prompting large models — particularly agentic or task‑specific systems — and improving performance through grounding, context window optimization, or RAG.
  • Experience researching and building LLM‑based systems, including both training and deployment.
  • Significant contributions to the AI field via top‑tier conferences or open‑source projects.
  • Proven ability to work closely with engineers, product teams, and non‑technical experts to ship fast and stay aligned.
  • Ph.D. or M.S. in Computer Science, Machine Learning, or a related field (or equivalent industry experience).
  • Must be located in the SF Bay Area or be willing to relocate.

    • Experience

  • 5+ years of experience in ML research, applied LLMs, or model infrastructure.
  • Experience with scalable LLM systems using LangChain, LlamaIndex, Hugging Face, or similar frameworks.
  • Familiarity with GPU‑based experimentation and cloud model deployment (e.g., vLLM, DeepSpeed, Ray Serve).
  • Experience with RAG pipelines and evaluation frameworks like RAGAS, DSPy, or LLaMAEval.

    • Bonus

  • Background as an early engineer in a rapidly growing startup.

    • Join us in shaping the future of government contracting with autonomy.

    #J-18808-Ljbffr

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