Junior Machine Learning Engineer

🏢 Striveworks · 📍 Austin, Texas, United States 💰 $100,000 - $100,000 / yr

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Position Summary & Responsibilities

“In 36 months, agentic AI systems will be an operating reality across major institutions. We intend to be central to it.” — Dr. Jim Rebesco, Cofounder and CEO, Striveworks

The government’s demand for AI is growing far faster than the systems required to support it. Fewer than 15% of federal AI programs have reached sustained production, despite billions of dollars invested. The models perform in testing, but they degrade in the real world. And when performance drops, trust goes with it.

Striveworks was built to solve that problem.

What you’ll build

Since 2018, we have delivered the most trusted AI systems operating in real-world use cases—providing a layer of assurance underneath hundreds of deployed models that monitors performance, manages drift, and sustains systems long after they leave the lab.

As a Junior Machine Learning Engineer, you will be a core contributor to both customer-driven projects and the enduring products of the company. Working alongside data scientists, software engineers, and DevOps engineers, you’ll develop AI capabilities, custom analytics, and mission-critical solutions applied to image, video, text, geospatial, time series, structured, and other data. You will orchestrate complex data engineering pipelines in agentic and traditional architectures. Your work will inform the future of Chariot, our proprietary AI operations platform, and your impact will be felt across mission-critical deployments and direct customer contact.

What it’s like here

We lead with trust, treat each other with respect, and use candor consistently, kindly, and constructively. We care deeply about our work, and we find genuine satisfaction in doing it well. Above all, we take ownership—because we feel the weight of collective results personally. We are looking for people who share these values and are eager to put them into practice.

What we’re looking for

A BS degree in computer science, machine learning, or a related discipline and 0–2 years of relevant experience
Strong proficiency in Python including experience with libraries and frameworks like PyTorch, TensorFlow, and/or scikit-learn
Strong software engineering fundamentals and experience with at least one systems programming language
Exposure to the development and use of machine learning models, through ei

🎯 Candidate Preparation Guide For Striveworks Applicants

Key Role Competencies & Evaluation Highlights

Key Role Focus:

Evaluating MLOps mastery, secure data operations in high-security environments, and scalable deployment of machine learning pipelines.

📋 Preparation Priorities (Key Areas to Clarify):

1. Jupyter notebook scientist mentality with no production-grade software deployment experience.
2. Hand-waves data security, provenance, and model governance concerns.
3. Rigid adherence to a single ML stack rather than adapting to mission requirements.

✨ Recommended Role Alignment Hook:

I operationalize robust MLOps platforms that turn chaotic operational data into secure, deployed AI intelligence at the edge.

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