Junior ML Engineer: Build Trusted AI in Real-World Systems

Striveworks; Austin, Northern (TX, KY); Hybrid; USD 100,000 - 130,000; Full time

Benefits offered by this job

Medical/dental/vision insurance

Voluntary life, long-term disability,

HSA and FSA (including dependent care)

401(k) plan

Unlimited PTO

Paid parental leave

Job summary

Striveworks in Austin, TX is seeking a Junior Machine Learning Engineer to join our cross-disciplinary team. You will work with data scientists, software engineers, and DevOps to build AI capabilities, analytics, and mission-critical solutions across diverse data types.

You will help orchestrate data pipelines in agentic and traditional architectures, contribute to Chariot, and influence deployments with direct customer contact.

Qualifications

• BS in computer science, machine learning, or a related discipline.
• Strong proficiency in Python with PyTorch, TensorFlow, or scikit-learn.
• Solid software engineering fundamentals and at least one systems language experience.
• Experience with developing and using ML models.
• Familiarity with modern software engineering tools and processes.
• US citizenship, eligibility for US security clearance (Secret or higher).

Responsibilities

• Develop AI capabilities and analytics for customer projects.
• Collaborate with data scientists, software engineers, and DevOps.
• Contribute to Striveworks' Chariot platform and AI deployments.
• Design data engineering pipelines in agentic and traditional architectures.

Skills

Python

PyTorch

TensorFlow

scikit-learn

Systems programming

ML models

Software fundamentals

Education

BS in CS/ml

Tools

Docker

Podman

Kubernetes

Job description

Striveworks in Austin, TX is seeking a Junior Machine Learning Engineer to join our cross-disciplinary team. You will work with data scientists, software engineers, and DevOps to build AI capabilities, analytics, and mission-critical solutions across diverse data types.

You will help orchestrate data pipelines in agentic and traditional architectures, contribute to Chariot, and influence deployments with direct customer contact.