Checklist & Templates

One page if you have less than 5 years of experience. Two pages maximum for senior roles. Research scientists with extensive publications may use a separate publications page. Never go beyond two pages for the main resume — hiring managers will not read it. Every line must earn its space with quantified impact.

Yes, if you are a new graduate or career changer with limited professional ML experience. Label them honestly as "Academic Projects" or "Personal Projects." Focus on projects where you went beyond the assignment requirements — extended the model, deployed it, or achieved notable results. Once you have 2+ years of professional experience, replace coursework projects with work experience.

Quality over quantity. Pin 3–6 excellent repositories. Each should have a professional README, clean code, and documented results. Three well-documented, end-to-end projects are worth more than thirty abandoned repositories with no documentation. Delete or archive old, low-quality repos that do not represent your current skill level.

Not strictly necessary, but helpful. A clean personal website with your resume, project portfolio, blog posts, and contact information provides a single link you can share everywhere. GitHub Pages is free and sufficient. Do not spend weeks building a fancy website — a simple, clean site with your work is better than a beautiful site with no content.

Yes, if you have notable achievements (Expert, Master, or Grandmaster tier, or medal-winning solutions). Include your tier and best competition results with brief descriptions of your approach. However, Kaggle alone is not enough — complement it with end-to-end projects that include deployment, as hiring managers want to see production skills alongside modeling skills.

Be honest. If you took time off, spent it learning, or had personal reasons, a brief note is fine. Fill gaps productively: contribute to open-source ML projects, build portfolio projects, take relevant courses, or participate in Kaggle competitions. These demonstrate continuous learning even during employment gaps. Never fabricate experience.

Absolutely yes. Job descriptions describe the ideal candidate, not the minimum viable candidate. If you meet 60–70% of the technical requirements, apply. Focus your resume and cover letter on the requirements you do meet, and show a learning trajectory for the gaps. Many successful hires match 60–80% of the listed requirements. The only exception: if the role explicitly requires a specific credential you do not have (PhD, specific years of experience for visa purposes).

Research market rates on levels.fyi, Glassdoor, and Blind for your specific role, level, and location. AI/ML roles typically command a 10–20% premium over general software engineering. Always negotiate — the first offer is rarely the best offer. Negotiate base salary, equity/RSUs, signing bonus, and remote work flexibility independently. Have competing offers if possible, as they significantly strengthen your position.

Lead with your software engineering strengths — production systems, code quality, scalability, and deployment experience are highly valued in ML roles. Many ML teams desperately need people who can ship reliable production code. Build 2–3 ML projects that demonstrate your learning, take an online ML specialization, and frame your transition as "bringing production engineering discipline to ML," not "learning ML from scratch."

For experienced ML practitioners, 2–4 months of active searching is typical. For career changers, 4–8 months is realistic. The timeline depends on your target role level, location flexibility, and how targeted your applications are. Start building your portfolio and network 3–6 months before you plan to actively apply. The job search itself should be treated like a project with weekly milestones.