Full Stack Software Engineering - Team Lead - Lever

Mitek Systems

San Diego, Californiaai

Mitek Systems logo Full Stack Software Engineering - Team Lead Mitek (NASDAQ: MITK) is a global leader in digital & biometric identity authentication, fraud prevention, and mobile deposit solutions. Our verified identity platform and advanced image capture solutions are built on the latest advancements in biometric recognition, artificial intelligence, computer vision and machine learning, and trusted by over 7,500 organizations worldwide. We are headquartered in San Diego, California, with oper

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Skills in this role

AI fundamentals
Machine learning
Cloud

What the employer asks for

What Would be Nice (Preferred Skills & Experience):
What Success Looks Like:
You will be successful in this role when:
LIFE AT MITEK
At Mitek, meaningful work and practical impact matter. Our culture is shaped by people who deliver for customers, keep learning, challenge convention, and care for one another.

Courses relevant to this posting

Relevance is based on skills and concepts — not a guarantee that you qualify.

Closed-Loop AI Systems: Validation, Feedback, and Workflow Design
Covered skills:AI fundamentalsMachine learningCloud
Skills to close:None listed

Build the skills for this job

Coming-soon courses matched to this posting — outline, waitlist, and skill path.

Course DNA for this role

Closed-Loop AI Systems: Validation, Feedback, and Workflow Design

7 chapters · 33 lessons

1. From Predictions to Outcomes: Closed-Loop System Fundamentals
4 lessons
Understand why feedback loops matter more than model accuracy and map the components of production AI systems.
2. Hypothesis Generation and Context Retrieval Architecture
5 lessons
Design the front end of your loop: grounding model outputs in trusted knowledge and domain constraints.
3. Execution Pipelines: Connecting Models to Real-World Actions
5 lessons
Build the infrastructure that takes model outputs and executes them safely in software, hardware, or human workflows.
4. Measurement and Validation in Regulated and Physical Domains
5 lessons
Design measurement systems that capture outcome quality, not just output fluency, across diverse validation contexts.
5. Feedback Loops and Continuous Model Improvement
5 lessons
Close the loop by using execution results to improve prompts, datasets, evaluation criteria, and model training.
6. Human-in-the-Loop and Governance Patterns
4 lessons
Integrate human review, escalation, and oversight into automated workflows without creating bottlenecks.
7. Deployment Architectures: Cloud, Edge, and Hybrid Systems
5 lessons
Choose and implement deployment patterns that support closed-loop learning across infrastructure constraints.

Want the full course when it launches? Join the waitlist and we will notify you.

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Skills in this role

What the employer asks for

Courses relevant to this posting

Build the skills for this job

Closed-Loop AI Systems: Validation, Feedback, and Workflow Design

1. From Predictions to Outcomes: Closed-Loop System Fundamentals

2. Hypothesis Generation and Context Retrieval Architecture

3. Execution Pipelines: Connecting Models to Real-World Actions

4. Measurement and Validation in Regulated and Physical Domains

5. Feedback Loops and Continuous Model Improvement

6. Human-in-the-Loop and Governance Patterns

7. Deployment Architectures: Cloud, Edge, and Hybrid Systems

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