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About This Role
Overview
JST is seeking an AI Engineer to help build a new AI-powered product from the ground up. The Engineer will use advanced AI development harnesses to move from product ideas to working software across the full stack, including cloud infrastructure, application services, data workflows, user interfaces, operational tooling, and software support.
This role is designed for a recent graduate of a technical master's or doctoral program who has strong engineering fundamentals and wants broad product ownership. You will work directly with the Head of AI Engineering, learning how to make sound architecture and product decisions while shipping useful, secure, and maintainable capabilities. You do not need to arrive as an expert in every layer of the stack.
This is a full-time remote, project-based position. You will report to the Head of AI Engineering and collaborate closely with product leaders, domain experts, designers, and other engineers through a distributed working environment. Joint Strategic Technologies's environment rewards practical experimentation, careful reasoning, and the willingness to work across conventional role boundaries. The team will expect you to use AI tools aggressively where they improve speed or quality, and to verify the result with the same care you would apply to code written by hand. JST offers a complete benefits package, a collaborative work environment, and strong company culture. Veterans and military spouses are encouraged to apply.
AI Engineer Responsibilities
- Use advanced AI coding and engineering harnesses to turn product requirements, prototypes, and technical designs into tested production software.
- Build complete product capabilities across cloud infrastructure, backend services, APIs, data pipelines, web applications, and user-facing experiences.
- Design agentic systems that can plan work, use tools, coordinate workflows, retain appropriate context, and automate repeatable product and operational tasks.
- Implement the controls that make AI systems dependable, including human approval points, permissions, evaluations, observability, failure handling, and cost and latency measurement.
- Work with product leaders and domain experts to understand real workflows, identify the smallest useful release, and refine the product from user feedback.
- Own vertical slices of the product from design through deployment and support, with guidance and review from the Head of AI Engineering.
- Evaluate models, prompting approaches, retrieval patterns, and tool-use designs against practical measures of quality, safety, speed, and cost.
- Write clear tests, technical documentation, runbooks, and support notes so that the product can be operated and improved by the broader team.
- Investigate software issues, improve reliability, and turn recurring support needs into better product behavior or safe automation.
What We Are Looking For
- A recently completed or soon-to-be-completed master's or doctoral degree in computer science, artificial intelligence, machine learning, software engineering, data science, robotics, human-computer interaction, or a closely related technical field.
- Strong programming fundamentals and practical experience in at least one modern language. Python, TypeScript, or JavaScript experience is especially relevant.
- A working understanding of APIs, databases, version control, testing, debugging, and the core concepts behind cloud or distributed applications.
- Evidence that you can take an ambiguous problem, break it into manageable steps, and finish a working project. Academic, research, startup, open-source, and professional projects all count.
- Clear written and verbal communication, intellectual curiosity, and comfort asking questions when requirements or evidence are incomplete.
- Good product judgment: you consider the user's workflow, usability, reliability, and support needs alongside the technical implementation.
Preferred Experience
- Two to three years of relevant industry experience. This is preferred rather than required; candidates with less experience and an unusually strong record of building and shipping will be considered.
- Exposure to product development through an engineering, research, design, startup, internship, co-op, or customer-facing project. A previous product-management title is not required.
- Hands-on work with large language models, retrieval-augmented generation, agent frameworks, tool calling, AI evaluations, or AI-assisted software-development workflows.
- Experience with a major cloud platform, infrastructure as code, serverless systems, containers, CI/CD, or production monitoring.
- Experience building modern web interfaces or collaborating on UX, design systems, accessibility, and user research.
- Interest in privacy, security, responsible AI, and the demands of enterprise or government software.
What Success Looks Like
- You ship useful product increments that work across the interface, application, data, and cloud layers.
- Your agentic automations save measurable time while preserving clear permissions, human oversight, and reliable failure paths.
- You use AI engineering tools with discipline, validating generated work through tests, review, and direct evidence.
- You learn the product and its users quickly, communicate tradeoffs clearly, and take on larger technical decisions as your judgment grows.
- You improve the product after release by following operational signals and support feedback back to their root causes.
Required Clearances and Screenings
- MUST be able to successfully pass a drug and background check
About Joint Strategic Technologies (JST)
No objective is beyond reach! Joint Strategic Technologies (JST) enables successful mission outcomes from the back office to the battlefield. Our team includes leading experts from military, government and the private sector, all working together to help federal customers make a difference. Our goal is to Elevate the Impact that our employees have on our customers and treat every employee as a valued member of our team. JST empowers employees to make decisions and take-action, thus improving overall organizational growth and employee development.
Culture isn't something you talk about. It's something you do. JST is committed to creating a positive environment 'that reaches beyond work and careers' to support every employee's professional and personal objectives. JST values the well-being of every employee and encourages healthy lifestyles, family activities and community involvement.
JST is a proud equal opportunity employer. All qualified applicants will be considered for employment without attention to age, race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. JST will not tolerate discrimination or harassment.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At JST, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
JST AI Hiring
JST has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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