Applied AI Engineer, Federal
Snorkel AIUSA2d ago
AIEngineeringremotecurated-jd
Job description
Applied AI Engineer, Federal at Snorkel AI.
About the role
Snorkel AI focuses on data-centric development to help organizations build custom artificial intelligence. This role involves working directly with federal customers to design and deploy machine learning solutions while refining our internal platform capabilities.
Key facts
What you'll do
- Partner with clients to scope, build, and deploy generative AI and machine learning systems.
- Implement RAG pipelines, fine-tuning workflows, prompt engineering, and agentic systems.
- Create evaluation frameworks and synthetic datasets to ensure model reliability.
- Manage relationships with technical and executive stakeholders to drive project success.
- Collaborate with product and pre-sales teams to translate customer needs into platform improvements.
- Lead workshops to educate stakeholders on AI capabilities and best practices.
- Address complex National Security challenges through technical delivery.
Requirements
- Active Top Secret (TS) clearance.
- B.S. degree in Computer Science, Engineering, Mathematics, Statistics, or equivalent experience.
- 3+ years of experience in customer-facing AI/ML solution implementation.
- Strong Python skills, including modular design, testing, and packaging.
- Proficiency with modern Python tooling: pydantic, mypy, pytest, poetry, FastAPI, msgspec, Ray, and Airflow.
- Experience with the Applied AI stack: scikit-learn, PyTorch, Hugging Face, FAISS, pandas, Spark, Chroma, Weaviate, LlamaIndex, LangGraph, and CrewAI.
- Ability to present technical concepts to both engineering and executive audiences.
- Up to 25% annual travel.
Nice to have
- Experience drafting responses to federal RFPs, RFIs, and RFCs.
- Background working with Civilian, DOD, or NATSEC agencies, including military logistics, federal law enforcement, or regulatory sectors.
Skills & tools
- Python ecosystem (FastAPI, Ray, Airflow, etc.)
- Deep learning frameworks (PyTorch, Hugging Face)
- Data processing and vector search (Spark, FAISS, Weaviate)
- LLM orchestration and agent frameworks (LangGraph, LlamaIndex)
Practical notes
- Compensation is determined by role, level, and location.
- All offers include employee stock options.
- This role requires the ability to work across multiple projects in a fast-paced environment.