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 committed to proving their ability to build and deploy production-quality AI systems under realistic constraints. Participants ship work weekly, collaborate with seasoned operators, and gain direct access to founders, CTOs, and hiring partners.

In the latest cohort, participants earned 300+ first-round interviews with hiring partners. The program targets 100% graduate placement via the partner network, with minimum starting compensation set at $200,000.

The structure spans 10 weeks: an initial 3-week remote phase, then 7 weeks onsite in Austin, Texas. Participants should anticipate a demanding schedule of 80–100 hours per week, engineered to optimize learning velocity, output quality, and career trajectory.

Program Outcomes:

  • 10+ production AI applications delivered throughout the fellowship
  • Entry to Gauntlet's alumni and hiring partner ecosystem
  • Graduates secure job offers starting at $200,000+ from partner companies

What you will be doing

  • Deliver production-ready AI applications weekly, adhering to firm deadlines
  • Develop systems using contemporary AI-native workflows (agents, tool integration, evals, retrieval, deployment pipelines)
  • Work competitively and collaboratively with top-tier engineering peers in a high-feedback setting
  • Showcase your work directly to CTOs, startup founders, and hiring decision-makers
  • Convert authentic project briefs into scoped, dependable, production-grade systems

What you will NOT be doing

  • Spending multiple weeks on theoretical study, tutorial completion, or passive content consumption without deployment
  • Creating prototype demos that are never tested against real users or rigorous evaluation
  • Depending on past credentials rather than demonstrable, measurable performance
  • Operating in relaxed environments with adjustable schedules or low expectations

Candidate requirements

  • 3+ years of professional engineering experience (or demonstrable equivalent capability)
  • Proven problem-solving aptitude, rapid learning capability, and sound reasoning under time constraints
  • High degree of responsiveness to feedback and commitment to intense execution standards
  • U.S. work authorization without need for visa sponsorship
  • Availability to commit to 80–100 hour work weeks and relocate to Austin for the 7-week onsite phase

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

  • AI-native development workflows (coding agents, MCP, real-time collaborative tooling)
  • Retrieval-Augmented Generation (RAG), embeddings, and vector database integration
  • High-velocity project sprints emphasizing delivery under tight constraints

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

  • Agent architectures, evaluation frameworks, verification, and observability tooling (LangChain/LangSmith/LangFuse/CrewAI)
  • Enterprise-level delivery standards: quality assurance, system reliability, and rigorous execution
  • Fine-tuning techniques + deployment strategies (LoRA/QLoRA + production-ready integration)
  • Multi-agent transformation of legacy and 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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