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

Engineers often speak of creating meaningful systems. Here is an opportunity to demonstrate it through continuous delivery, rigorous assessment, and operational AI infrastructure that influences how the United States government functions. No reliance on academic pedigree. No abstract case studies. Only production deployments every week, executed under real conditions.

Gauntlet for America is a fully funded, competitive 10-week fellowship created to develop AI-first engineering capacity for federal institutions. It serves as an intensive validation environment for accomplished engineers prepared to build and maintain enterprise-grade AI systems where dependability, security, and measurable impact are essential.

Participants deliver weekly releases, work under continuous evaluation, and collaborate with other top-tier engineers. Those who complete the program successfully transition into federal GS-12 engineering positions (~$150K + comprehensive federal benefits), contributing to systems that directly affect government operations.

The fellowship structure spans 10 weeks: an initial 3-week remote phase, then 7 weeks onsite in Austin, Texas. Participants should anticipate a demanding schedule (80–100 hours/week) structured to accelerate skill acquisition, performance clarity, and professional advancement.

Program Outcomes:

  • Delivery of 10+ production-grade AI systems throughout the fellowship period
  • Immediate placement into federal engineering roles (GS-12 level, ~$160K–$200K+ based on background + full benefits)
  • Contribution to high-impact infrastructure influencing U.S. government technology strategy and operations
  • Access to a professional network of AI-native engineers working at the leading edge of public sector technology

If you are prepared to be assessed on delivery — not academic background — submit your application.

What you will be doing

  • Deliver production-grade AI systems weekly against firm deadlines
  • Develop solutions using modern AI-native methodologies (agent frameworks, tool integration, evaluation systems, retrieval architectures, deployment pipelines)
  • Work alongside and compete with high-caliber engineering professionals in a continuous feedback loop

What you will NOT be doing

  • Attending theoretical lectures or passive training sessions — all time is allocated to development and deployment
  • Experiencing long delays before production deployment — you will release working systems on a weekly cadence
  • Depending on academic credentials, institutional background, or interview scores to secure placement — production output is the sole evaluation criterion
  • Operating in a consequence-free test environment — the systems you create must meet actual security and reliability standards

Key responsibilities

Deliver production-ready AI systems under authentic operational constraints, demonstrating preparedness for federal engineering responsibilities.

Candidate requirements

  • U.S. citizenship mandatory (no exceptions; background check will be conducted)
  • Proven engineering capability (both recent graduates and experienced professionals are eligible)
  • Availability to relocate to Austin, TX for the 7-week onsite phase (full-time, in-person participation required)
  • Availability to relocate to the Washington, DC metropolitan area following program completion (remote work not available)
  • Demonstrated problem-solving skills, rapid learning capability, and sound judgment under time constraints
  • Strong receptiveness to critical feedback and capacity to perform in high-pressure settings

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.

Frequently asked questions

About Crossover

What you will learn

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

  • Development workflows centered on AI tooling (coding agents, Model Context Protocol, real-time collaborative systems)
  • Retrieval-Augmented Generation (RAG) techniques, embedding models, and vector database implementations
  • Accelerated project cycles emphasizing delivery under resource and time constraints

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

  • Agent architectures, evaluation frameworks, verification methods, and observability tools (LangChain/LangSmith/LangFuse/CrewAI)
  • Enterprise-standard delivery practices: quality assurance, system reliability, and high-expectation execution
  • Model fine-tuning and deployment strategies (LoRA/QLoRA + production-ready integration)
  • Multi-agent approaches to modernizing legacy systems and existing codebases
  • Multimodal AI implementations (image/video/voice processing) and scalable cloud infrastructure (AWS/Azure)

Meet some people who've landed similar jobs

Why Crossover

Recruitment sucks. So we’re fixing it.

The Olympics of work

The Olympics of work

It’s super hard to qualify—extreme quality standards ensure every single team member is at the top of their game.

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.

Crossover Logo White
Follow us on
Have a question?

Get answers to common questions using our smart chatbot Crosby.

HELP AND FAQs

Join the world's largest community of  AI first Remote WorkersAI-first remote workers.