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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in AI/ML engineering with a focus on developing and fine-tuning models for production, including proficiency in Python and relevant ML frameworks. Capable of analyzing algorithm performance, improving data quality, and mentoring engineering teams.
Highest-signal resume keywords
AI/ML Engineering ExperienceModel Training And Fine-TuningML Evaluation PipelinesData Curation And ProfilingProficiency In Python
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Algorithm DevelopmentModel Fine-TuningData StructuresDeep Learning SystemsMultimodal ModelsProduction ML Systems DebuggingAI Observability ToolsComputational GeometrySoftware Engineering PrinciplesTechnical Document Analysis
Soft Skills
Ownership MindsetClear CommunicationCuriosityMentoring AbilitySystems Thinking
Tools & Technologies
PythonPyTorchVLLMLangfuseOpik
Industry Keywords
ConstructionCivil EngineeringTechnical DocumentsVector GeometryCAD
Tech Stack
Tools & technologiesPythonPyTorchGo
About the role
Key responsibilities & impact- Develop algorithms that extract meaning from complex technical documents containing text, vector geometry, CAD, and raster content
- Build document-understanding capabilities into tools powering Circuit’s long-running agentic systems
- Improve reliability, determinism, and effectiveness of agentic systems, including planning, tool use, and failure recovery
- Build and extend training and evaluation pipelines for core models and agents
- Analyze production algorithm performance, identify failure causes, and drive improvements
- Identify dataset gaps and bias through curation, labeling, or synthetic data
- Track new research and techniques and bring promising approaches into production
- Mentor and elevate other engineers
- Trace extraction failures, improve models or algorithms, and validate gains through evaluations
- Fine-tune specialized models, including LoRA on in-house models
- Improve retrieval quality, relevance, and latency
- Improve agent determinism by changing tools or the agent loop
- Join customer conversations when technical depth is needed
Requirements
What you’ll need- Hands-on AI/ML engineering experience with a track record of shipping models in production
- Experience training and fine-tuning models for production, including datasets and pipelines
- Experience building and operating ML evaluation pipelines, including datasets, metrics, and regression testing for models and agents in production
- Experience profiling data distributions, identifying gaps and bias, and curating high-quality datasets for training and evaluation
- Experience with vision and multimodal models, especially on technical documents (PDFs, CAD) with text, vector geometry, and raster content
- Strong knowledge of data structures, algorithms, and software engineering principles
- Proven expertise with LLMs, deep learning systems, and applied ML
- Experience debugging production ML systems using AI observability tools (e.g. Langfuse, Opik, etc.)
- Proficiency in Python and relevant ML frameworks (PyTorch, vLLM, etc.)
- Daily, fluent use of AI coding tools
- Strong software engineering fundamentals and ability to lead technical decisions
- Bonus: Computational geometry or experience with vector drawing formats such as CAD and vector PDFs
- Bonus: Go
- Bonus: Familiarity with construction, civil engineering, or related industries
- Ownership mindset, product judgment, curiosity, bias toward shipping, clear communication, systems thinking, measurement rigor, and mentoring ability
Benefits
Comp & perks- Competitive compensation
- Meaningful equity
- Employer-paid benefits
- Flexible PTO
- Access to the latest AI tools
- Values-driven culture that rewards ownership, curiosity and velocity