Run it as a process. Track applications and three conversion rates: applications to screens, screens to onsites, onsites to offers. Calculate each rate from resolved stage transitions, show the denominators, and keep pending decisions separate. Compare cohorts with a similar time to resolve. Different response delays can change resolved-only rates without any real improvement. Use each stage to find a question to investigate before choosing more preparation.
A tracker you can actually use. One spreadsheet or markdown file, updated weekly:
| Date | Company | Role | Source (referral / community / cold) | Stage | Outcome | Notes |
|---|---|---|---|---|---|---|
| 2026-09-01 | Example Co | AI Engineer | Referral from blog post | Screen | Waiting | Sent portfolio link to RAG project |
If few applications become screens, review role fit, eligibility, CV and portfolio signals. If few screens or onsites move forward, ask for feedback on the actual rounds, including coding, SQL, design and communication where assessed. Review level or pay expectations when those were a stated obstacle. A funnel rate alone cannot identify the cause.
Before weeks of preparation, review a few current postings for your target city and level. Record the date, actual responsibilities, required skills, interview format if known, and which project supplies evidence. Revisit this monthly. Titles alone can mislead. An "AI Engineer" role can mean product integration, retrieval, modelling, infrastructure or research.
| Posting requirement | Evidence to prepare | Gap to close |
|---|---|---|
| Offer, pricing or retention modelling | Chapter 9 decision brief, realistic validation and experiment design | Explain prediction versus intervention, support and net contribution |
| LLM application engineering | Chapter 5 held-out evaluation, failure analysis and access controls | Explain retrieval, tool reliability and cost/quality tradeoffs |
| ML platform or backend engineering | Chapter 6 release, monitoring and rollback exercise | Explain compatibility, freshness, retries and operational ownership |
Track referrals, community contacts and targeted applications separately, and use your own funnel to decide where to spend time. Small counts jump around, and a low response rate can reflect fit, location, timing or application quality. Change one thing, then review a new batch. One rejection is no reason to redesign your whole portfolio.
Consider nearby roles that match your experience: data engineering, backend work on an AI product, contract projects or an internal transfer. Check the actual mentorship, work and transfer policy, because a backend offer does not promise a move into ML within a year. Employers outside technology may also have relevant tabular and document projects.
On pay, compare your level, location and pay components. Indeed's September 2026 US analysis finds seniority affects advertised-pay comparisons, though its seniority-specific evidence is only suggestive. It studies US postings with an advertised salary, not individual offers or Indian salaries. Self-reported pay trackers and aggregate AI wage premiums cannot predict your offer. Check current local ranges, and separate base pay, equity, bonuses and contract terms.