Navigating the AI Job Market: 20 Rules for the Road

POSTED ON Aug 12, 2025

Janvi Kalra recently shared insights about her experience after transitioning from a software engineer to an AI engineer at her previous company.

Before joining OpenAI as a member of the technical staff, she interviewed with 46 different AI startups. In a video shared on social media, she explained her approach to navigating the AI job market.

Janvi categorized AI companies into three segments: product companies, infrastructure companies, and model companies.

She found it helpful to classify each company to identify which segment excited her the most, as there are currently many AI companies available.

Product companies build applications on top of AI models, such as Cursor and Codeium. Infrastructure companies create the tools that enable AI product companies to effectively utilize large language models (LLMs), including companies like Fireworks AI and vector database providers like Pinecone.

Model companies form the foundation of the ecosystem by developing the intelligence itself; these include major tech giants like Google and Meta, as well as smaller firms like OpenAI and Anthropic.

Janvi decided to focus her job search on model companies because she wanted to continue growing in her career. However, she acknowledged that this choice came with its challenges, making her path more difficult.

No doubt about it, breaking into the competitive fields of artificial intelligence (AI), machine learning (ML), and data visualization—particularly in roles involving algorithms, neural networks, and digital dashboards—requires a combination of strong technical skills, practical experience, and strategic networking.

Here is a roadmap for new graduates aiming to land their first job in these exciting and rapidly evolving domains:

Build a strong technical foundation by mastering the fundamentals: A solid understanding of computer science fundamentals, algorithms, data structures, and statistics is essential.

Developing proficiency in Python is crucial, along with familiarity with frameworks and libraries such as TensorFlow, PyTorch, NumPy, and Scikit-learn, according to the SkillUp Coalition.

Understand the core concepts of supervised and unsupervised learning, various neural network architectures, and the model training process.

Learn to clean, analyze, and visualize datasets effectively using tools like Tableau, Power BI, or libraries such as Matplotlib and Seaborn.

Work on diverse AI/ML projects that showcase your abilities in areas like natural language processing (NLP), computer vision, predictive analytics, or recommendation systems.

Create a portfolio website or use platforms like GitHub to showcase your projects, emphasizing the problems addressed, your approach, the technologies used, and the outcomes achieved. Ensure your documentation is clear and includes visualizations.

Seek out internships in AI/ML or data science roles to gain valuable real-world experience and industry exposure.

Participate in hackathons or ML competitions on platforms like Kaggle to hone your skills and gain experience with real-world datasets.

Contribute to open-source AI/ML projects to demonstrate your collaboration skills and expertise.

Customize your resume for each job application by highlighting skills and experiences relevant to the specific role. Use keywords from the job description to optimize for Applicant Tracking Systems.

Craft a concise objective statement or summary that highlights your qualifications, experience, and career goals.

Quantify your accomplishments whenever possible to demonstrate the impact of your work.

Include a link to your online portfolio or GitHub profile on your resume and LinkedIn profile.

Attend industry events, meetups, and webinars to connect with professionals and expand your network.

Reach out to university alums working in the field for guidance and connections.

Find a mentor who can offer valuable advice and introduce you to their professional contacts.

Engage in continuous learning through courses, research papers, and industry publications is crucial.

Look for entry-level positions such as “Junior AI Engineer,” “Machine Learning Engineer,” “Data Scientist,” or “Algorithm Engineer.”

Apply as soon as jobs are posted to increase your visibility.

Practice coding challenges, problem-solving questions, and discussions about your projects.

By focusing on these key areas, new graduates can significantly improve their chances of landing a job in the exciting and in-demand fields of algorithms, neural networks, and digital dashboards.

What does it take to land a job as an AI Engineer—and thrive in the role?@janvikalra_ works at OpenAI in this role: and she transitioned from software engineer to AI Engineer at her previous company, Coda. Before joining OpenAI, she interviewed with 46 different AI startups… pic.twitter.com/0KeK53V2vi

— Gergely Orosz (@GergelyOrosz) May 28, 2025

this is the easiest way to build a solid ai resume. i am surprised how so many people miss this out. it’s so much fun!

your future self in 6-months will scare you.

start building the aura by locking in on @github or @huggingface and share what you learn along the way.

find an… pic.twitter.com/BHySu6k2fv

— ℏεsam (@Hesamation) May 26, 2025