Machine Learning Engineer
$160K–$200K+ Offers For Graduates $160K–$200K+ Offers For Graduates 

3 weeks remote, 7 weeks onsite in Austin, TX
80–100 hours/week for 10 weeks
In-person
Short-term contract
full-time (90 hrs/week)

Machine Learning Engineer   $160K–$200K+ Offers For Graduates $160K–$200K+ Offers For Graduates 

Description

Most engineers claim they want to build things that matter. Here's your opportunity to back that up: continuous deployment, rigorous assessment, and operational AI systems that directly influence how the U.S. government functions. No resume posturing. No academic hypotheticals. Just working production code delivered weekly, under real constraints.

Gauntlet for America is a fully funded, competitive 10-week fellowship that builds AI-first engineering capacity for the United States government. It operates as a high-pressure proving environment for seasoned engineers who want to show they can construct and maintain production-quality AI systems in settings where reliability, security, and tangible impact are non-negotiable.

Fellows deploy systems weekly, work under continuous evaluation, and train with other high-caliber engineers. After successful completion, graduates transition into federal GS-12 engineering positions (~$150K + comprehensive federal benefits), working on systems that directly affect government operations.

The program spans 10 weeks: 3 weeks conducted remotely, then 7 weeks onsite in Austin, Texas. Fellows should anticipate a demanding schedule (80–100 hours/week) structured to maximize learning velocity, demonstrated capability, and career trajectory.

Outcomes:

  • 10+ production-grade AI systems delivered during the fellowship
  • Direct transition into a federal engineering position (GS-12 equivalent, ~$160K–$200K+ based on experience + full benefits)
  • Contribute to high-impact systems that shape how the U.S. government develops and operates technology
  • Become part of a network of AI-native engineers working at the leading edge of public sector innovation

If you're prepared to be judged on what you ship — not your academic pedigree — apply now.

What you will be doing

  • Deliver production-grade AI applications weekly against strict deadlines
  • Develop using modern AI-native workflows (agents, tool integration, evals, retrieval, deployment)
  • Collaborate and compete with elite engineering talent in a high-feedback environment
  • Engage with authentic, ambiguous problem domains similar to government and enterprise contexts
  • Convert real-world briefs into scoped, dependable, deployable systems

What you will NOT be doing

  • Attending theoretical lectures or passive instruction — every hour is dedicated to building and deploying
  • Waiting months to see your work reach production — you'll ship operational systems every week
  • Depending on credentials, pedigree, or interview polish to secure your position — your production output is the sole measure that counts
  • Operating in a low-risk sandbox — the systems you construct face genuine security and reliability demands

Key responsibilities

Deliver production-quality AI systems under real-world constraints that prove readiness for federal engineering positions.

Candidate requirements

  • U.S. citizenship required (no exceptions; background check required)
  • Demonstrated engineering ability (new grads and experienced engineers considered)
  • Willing to relocate to Austin, TX for 7 weeks (full-time, in person)
  • Willing to relocate to the Washington, DC area upon program completion (no remote roles)
  • Strong problem-solving ability, learning speed, and clear reasoning under pressure
  • High responsiveness to feedback and ability to operate in high-intensity environments

Meet a successful candidate

Watch Interview
Fabiano Lucchese
Fabiano  |  SVP of Software Engineering
Brazil

Does your company encourage your natural creativity? This Brazilian engineering leader rediscovered his purpose after unleashing both his an...

Meet Fabiano

Applying for a role? Here’s what to expect.

Crossover's skill assessment process combines innovative AI power with decades of human research, to take the guesswork, human bias, and pointless filters out of recruiting high-performing teams.

Chat-style
screening interview.
STEP 1

Chat-style
screening interview.

Cognitive 
aptitude test.
STEP 2

Cognitive 
aptitude test.

Prove real-world 
job skills.
STEP 3

Prove real-world 
job skills.

Interview with the hiring manager.
STEP 4

Interview with the hiring manager.

Pass
proctored test.
STEP 5

Pass
proctored test.

Accept job offer.
STEP 6

Accept job offer.

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What you will learn

Phase 1: Remote (Weeks 1–3) — AI-First Engineering Foundations

  • AI-native development workflows (coding agents, MCP, real-time collaboration)
  • Retrieval-Augmented Generation (RAG), embeddings, and vector databases
  • Accelerated project sprints centered on shipping under constraints

Phase 2: Onsite in Austin (Weeks 4–10) — Production AI at Scale

  • Agent systems, evals, verification, and observability (LangChain/LangSmith/LangFuse/CrewAI)
  • Enterprise-level delivery: QA, reliability, and high-standards execution
  • Fine-tuning + deployment patterns (LoRA/QLoRA + production integration)
  • Multi-agent modernization of real-world codebases
  • Multimodal AI builds (image/video/voice) and scalable infrastructure (AWS/Azure)

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The Olympics of work

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Premium pay for premium talent

Premium pay for premium talent

Over 50% of new hires double or triple their previous pay. Why? Because that’s what the best person in the world is worth.

Shortlist by skills, not bias

Shortlist by skills, not bias

We don’t care where you went to school, what color your hair is, or whether we can pronounce your name. Just prove you’ve got the skills.

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