Staff Engineer, Applied ML

Circuit

. Build machine learning capabilities that power Circuit’s product .

Posted 11/10/2026full-timeAustin • Texas • United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying machine learning solutions, with a strong focus on model training, evaluation design, and debugging production systems. Proficient in collaborating with cross-functional teams to address customer-specific challenges and improve retrieval quality and latency.

Highest-signal resume keywords

Machine Learning DeploymentModel Training And Fine-TuningData Structures And Data ModelingPython And Go ProficiencyDebugging Production ML Systems

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningModel TrainingData ModelingEvaluation DesignDebuggingSchema DesignNormalizationIndexing StrategyRetrieval SystemsStatistical Models

Soft Skills

CollaborationCustomer EngagementProblem-Solving

Tools & Technologies

LLMsRAG SystemsAI Coding ToolsPyTorchVLLMKServe

Industry Keywords

Industrial WorkflowsConstructionCivil EngineeringOpen-Source ModelsTechnical Documents

Tech Stack

Tools & technologies

PythonPyTorchGo

About the role

Key responsibilities & impact

Build machine learning capabilities that power Circuit’s product
Work directly with Forward Deployed Engineers, Product Engineers, and design customers to understand real-world challenges
Turn customer challenges into robust, scalable solutions
Identify broader product capabilities from individual customer needs
Own solutions end to end with attention to accuracy, reliability, latency, and cost
Apply LLMs, traditional neural networks, and statistical models
Perform customer use-case analysis, data and process modeling, evaluation, production implementation, model training, and fine-tuning
Build solutions with design customers for judgment-heavy industrial workflows
Generalize and package solutions so FDEs can deploy them across customers
Improve models, harnesses, and evaluation methodologies
Debug production systems, including ML pipeline and retrieval-system edge cases and failure modes
Build and maintain evaluation systems, datasets, scoring approaches, and repeatable regression testing
Collaborate with FDEs, Product Engineers, Core ML engineers, and applied ML researchers
Improve retrieval quality, relevance, and latency for customer-specific use cases

Requirements

What you’ll need

Shipped ML-powered software to production
Deep expertise and strong technical opinions in at least one of: retrieval systems, model training and fine-tuning, evaluation design and dataset curation, or debugging production ML systems with AI observability tools
Strong software engineering background with a track record of owning and shipping production systems end to end
Deep experience with data structures and data modeling across relational and graph databases
Strong opinions on schema design, normalization, and indexing strategy
Hands-on experience building with LLMs, RAG systems, and agent harnesses
Experience working directly with customers or field teams
Track record of generalizing a solution built for one customer for broader deployment
Experience building and maintaining features across a multi-service architecture
Strong proficiency in languages like Python and Go
Daily, fluent use of AI coding tools
Bonus: vision and multimodal models, especially for technical documents such as PDFs and CAD
Bonus: construction, civil engineering, or other industrial domains
Bonus: open-source models such as Qwen or DeepSeek, and serving them with vLLM or KServe
Bonus: hands-on PyTorch or similar for model training and fine-tuning

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