Junior Machine Learning Engineer

Machine Learning, Artificial Intelligence (AI), Information Technology & Information Systems, Artificial Intelligence (AI) Programming Languages, Government, Testing, Problem Solving Skills, Use Cases, Systems Maintenance, Performance Analysis, * 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 either coursework or practical experience
• Familiarity with modern software engineering tools and processes
• US citizenship and eligibility and willingness to obtain and maintain a US security clearance (Secret or above)

The following isn’t required, but we’d love to see it:

• Demonstrated software development through personal, academic, or open source projects
• Advanced degree in computer science, data science, machine learning, or a related discipline
• Excellence in Python and deep knowledge of machine learning libraries
• Experience performing exploratory data analysis on structured and unstructured data
• Experience with agentic workflows, LLM evaluation, testing, observability, or guardrails
• Familiarity with vector databases, embeddings, retrieval-augmented generation (RAG), or other modern AI application patterns
• Knowledge of relevant architectures and design patterns for client-server systems
• Experience implementing and deploying software in containerized or cloud environments, using tools such as Docker, Podman, or Kubernetes (K8s)
• Experience working with asynchronous programming, concurrency, or event-driven architectures
• Active Secret (or above) US security clearance

This position offers a hybrid/on-site work environment at our office in Austin, TX. You will be expected to travel up to 25% of the time.

The anticipated base pay range for this position is $100,000-$130,000/year. Striveworks’ total compensation package includes a competitive base salary, equity grants, and cash bonuses.

Benefits include:

• Medical/dental/vision insurance
• Voluntary life, long-term disability, accident, and hospital indemnity insurance
• HSA and FSA (including dependent care FSA) plans
• 401(k) plan
• Unlimited PTO
• Paid parental leave

Ready to build systems that work for a mission that matters? Let’s talk.

“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.