The AI/ML field has changed dramatically since 2022. If you have been away from the field for 2+ years — career break, sabbatical, parental leave, or pivot to something else — you are returning to a meaningfully different industry. The interview reality has shifted; so has what counts as “modern AI/ML work.”
What has changed
Major shifts since 2022:
- LLMs went from research curiosity to industry default
- “Building from scratch” pre-trained models is now the domain of frontier labs
- Most ML engineers in 2026 work on RAG, fine-tuning, or AI-product engineering
- Eval discipline has become the differentiating skill
- Multimodal models and agents are mainstream in interviews
The skill refresh
For coming back into AI/ML:
- Read the latest papers in your domain (skim NeurIPS / ICML proceedings, watch Two Minute Papers / Yannic Kilcher)
- Build a small project with the modern stack (LangChain or DSPy + a vector DB + an evaluation harness)
- Use the major API platforms (OpenAI, Anthropic, Gemini, Mistral) to build something real
- Learn to write prompts well — both as a user and as part of a system
The “new ML jobs” reality
The job titles have proliferated:
- ML Engineer: traditional — train and deploy models
- Applied AI Engineer: build products on top of foundation models
- AI / Forward-Deployed Engineer: customer-facing, high-touch, build on partner systems
- Research Engineer: at frontier labs, do cutting-edge work
- Research Scientist: publish; design new models or methods
- AI Product Manager: product role, AI-specific
- Prompt Engineer: often a misnomer; rolled into broader roles
Pick the lane that fits your background and current state.
If you were a “classical ML” engineer
If you were doing scikit-learn, XGBoost, traditional models in 2020:
- Those skills still matter for many production problems (recommendation, search, ranking)
- Industry has not fully replaced them with LLMs
- Add LLM/RAG familiarity to make yourself versatile
If you were a deep-learning researcher
If you trained models from scratch in 2020:
- Understanding scales beyond what you had — frontier models are 10K+ GPUs
- Most production work is now on top of foundation models, not from scratch
- Your background remains valuable; broaden into using these models in product
Companies hiring AI/ML in 2026
Categories:
- Frontier labs: Anthropic, OpenAI, DeepMind, Mistral, Cohere, xAI — research engineers and scientists
- AI-native startups: Cursor, Glean, Harvey, Hex, Sierra, Decagon, etc. — applied AI engineers
- Big tech AI orgs: Google, Meta, Apple, Microsoft, Amazon — both research and applied
- Vertical AI: Tempus (medical), Recursion, Insitro (drug discovery), legal AI, etc.
- Traditional companies adopting AI: Big banks, insurers, retail — applied AI / ML platform
Comp ranges
- Frontier labs: $400K–$1.5M+ for senior research roles
- AI-native startups: $250K–$500K typical, with significant equity upside
- Big tech AI: $300K–$700K depending on level
- Traditional companies: $200K–$400K
Interview prep specific to AI/ML
Beyond standard DSA + system design:
- ML system design: how would you build the recommendation engine for X?
- Eval design: how do you measure if your AI feature is working?
- Prompt design: write a prompt for the following task, then critique it
- Failure mode analysis: what could go wrong, how would you detect
The “I have not used LLMs professionally” gap
If you have never shipped an LLM-based feature, address it directly:
- Build a small project demonstrating end-to-end LLM use (RAG, fine-tuning, or agent)
- Open-source it
- Reference in your resume and during interviews
This is the new “I have done a Kaggle competition” — a small but credible signal.
Frequently Asked Questions
Should I learn JAX or stick with PyTorch?
PyTorch is the dominant industry framework in 2026. JAX shows up at frontier labs and Google. Pick PyTorch unless targeting JAX-heavy companies.
Do I need to know transformer internals?
For research and platform engineering: yes. For applied AI: helpful but not critical. Be able to explain attention at a high level.
Is “AI” hiring still hot in 2026?
Hot at the top, normalized in the middle. Frontier labs and serious AI startups still pay aggressively; “AI features at a SaaS” hiring has cooled to typical mid-tier comp.