Machine Learning Engineer
$200,000+ Offers For Graduates $200,000+ Offers For Graduates 

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

Machine Learning Engineer   $200,000+ Offers For Graduates $200,000+ Offers For Graduates 

Description

Gauntlet is a selective, fully funded 10-week fellowship designed for experienced engineers determined to advance their careers by demonstrating their ability to build and deploy production-grade AI systems under authentic pressure. Throughout the program, fellows deliver weekly shipments, collaborate with seasoned operators, and gain firsthand access to founders, CTOs, and hiring partners.

During our latest cohort, participants generated over 300 first-round interviews with hiring partners. We aim for every graduate to receive offers from our partner network, with baseline starting compensation set at $200,000.

The fellowship spans 10 weeks: an initial 3-week remote phase, then 7 weeks onsite in Austin, Texas. Fellows must prepare for a demanding schedule (80–100 hours weekly) engineered to accelerate learning velocity, demonstrate capability, and optimize career trajectories.

Expected Outcomes:

  • More than 10 deployed AI applications delivered throughout the fellowship
  • Entry into Gauntlet's alumni and hiring partner network
  • Job offers of $200,000 or more from partner organizations upon graduation

If you ship quickly, perform under pressure, and prefer your work—not your credentials—to define your next opportunity, submit your application.

What you will be doing

  • Deliver production-ready AI applications weekly against firm deadlines
  • Work with contemporary AI-first methodologies (agents, tool use, evaluations, retrieval, deployment)
  • Engage in collaboration and competition with elite engineering peers within a high-feedback setting
  • Showcase your work directly to CTOs, founders, and hiring partners
  • Convert authentic project briefs into scoped, dependable, deployable systems

What you will NOT be doing

  • Devoting weeks to theory, tutorials, or passive study without tangible deployment
  • Constructing demonstrations that remain untested by real usage or assessment
  • Refining resumes and portfolios rather than constructing and deploying functional systems
  • Attending extended lectures and coursework before engaging with actual code

Key responsibilities

Build and ship production-grade AI systems on a weekly cadence, converting demonstrated engineering excellence into career outcomes exceeding $200K via Gauntlet's hiring partner network.

Candidate requirements

  • 3+ years of professional engineering experience (or equivalent ability)
  • Have independently designed, implemented, and deployed at least one working software system to production or active users
  • High responsiveness to feedback and extreme execution intensity
  • Authorized to work in the U.S. without visa sponsorship
  • Willing to commit to 80–100 hour weeks and relocate to Austin for 7 weeks

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 (Weeks 1–3) — Foundations in AI-First Engineering

  • Development workflows centered on AI (coding agents, MCP, real-time collaboration)
  • Retrieval-Augmented Generation (RAG), embeddings, and vector database technologies
  • Accelerated project sprints emphasizing delivery under constraints

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

  • Agent architectures, evaluations, verification, and observability tooling (LangChain/LangSmith/LangFuse/CrewAI)
  • Enterprise-level delivery practices: QA, reliability, and high-standards execution
  • Fine-tuning and deployment strategies (LoRA/QLoRA with production integration)
  • Multi-agent modernization applied to real-world codebases
  • Multimodal AI development (image/video/voice) 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.

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