Applied AI Engineer

Atomicwork Inc•Palo Alto, CA

39d•Onsite

About The Position

We’re looking for an Applied AI Engineer to join our Engineering team to design, build, evaluate, and deploy intelligent AI systems that power enterprise workflows, autonomous assistants, and machine-assisted task automation. This role is based out of our US office and comes with competitive pay and practical benefits for you and your family. We’re looking for strong problem solvers who’ve shipped impactful AI-driven functionality before and can scale our platform intelligence with quality and ownership.

Requirements

Strong programming experience in Python and familiarity with ML/AI frameworks like PyTorch or TensorFlow.
Hands-on experience with transformer-based models (LLMs), embeddings, vector search, and retrieval augmentation.
Experience building or integrating semantic search, RAG, or multi-agent workflows.
Demonstrated ability to build reliable ML/AI systems in production environments.
Understanding of scalable backend systems, cloud infrastructure, and model serving techniques.
Good software engineering practices: clean code, testing, version control (Git), and collaboration.
Curious mindset, product empathy, and willingness to learn new technologies quickly.

Nice To Haves

We don’t require specific degrees — practical skills, curiosity, and ownership matter most.

Responsibilities

Design, build, and fine-tune transformer-based and retrieval-augmented models for natural language understanding, intent classification, summarization, and context-aware reasoning.
Develop AI components that power automated knowledge search, semantic retrieval, agentic task orchestration, and conversational assistants.
Integrate AI systems with production data pipelines in a scalable and secure way.
Build evaluation pipelines, define metrics, and establish performance and reliability benchmarks for AI features.
Collaborate with engineering, data, and product teams to productize models with observability, monitoring, and continuous improvement.
Participate in code reviews, architecture discussions, and cross-team problem solving.
Take end-to-end ownership of features — from problem definition to deployment and performance monitoring.