For better or worse, Meet the new age of the AI wingman: The ultimate interview sidekick for tech pros
- Sam Cannon
- Apr 10
- 7 min read
The technical interview process for machine learning (ML), artificial intelligence (AI), and data science (DS) roles has long been a high-stakes ordeal—think whiteboards covered in algorithms, brain-teasing puzzles, and the pressure to perform flawlessly in a vacuum. Enter AI assistants, tools that candidates are quietly using to navigate these gauntlets, sparking a heated debate: Are they cheating, or are they simply adapting to a world where such tools are workplace staples? This post dives into the pros and cons of candidates using AI assistants during technical interviews, outlines how these tools should be used, argues for a long-overdue overhaul of the ML, AI, and DS interview process, and compares five real-time AI interview assistants, including LockedIn AI, to highlight their strengths and quirks.
Pros of AI Assistants in Technical Interviews
Mirrors Real-World working the wild, ML engineers, AI researchers, and data scientists don’t solve problems in isolation. They lean on tools—Jupyter notebooks, Stack Overflow, or AI assistants—to debug code, validate models, or spark ideas. Allowing AI assistants in interviews tests a candidate’s ability to wield these tools effectively, reflecting the collaborative, resource-rich reality of the job.
Reduces Anxiety, Highlights Thinking, Technical interviews can feel like a pressure cooker, where nerves trip up even the most capable candidates. AI assistants can act as a lifeline—say, for recalling a tricky Pandas function or clarifying a neural network concept—letting candidates focus on problem-solving and creativity rather than sweating syntax.
Rewards Resourcefulness, Knowing how to craft a precise query for an AI assistant is a skill, not a crutch. Candidates who can quickly extract relevant insights from these tools demonstrate adaptability and efficiency, traits that are gold in fast-paced ML, AI, and DS roles.
Democratizes Opportunity - Not everyone can afford pricey bootcamps or dedicate months to LeetCode marathons. AI assistants, often accessible for free or low cost, level the playing field, giving under-resourced candidates a shot to shine without gatekeeping preparation.
Cons of AI Assistants in Technical Interviews
Risk of Over-Dependence - Leaning too hard on an AI assistant, and a candidate might churn out slick code or answers without grasping the why behind them. This muddies the waters for interviewers trying to gauge true technical depth.
Ethical Gray Zone - In virtual interviews, it’s easy to secretly tap an AI assistant for full solutions, bypassing critical thinking. This raises thorny questions about honesty and whether the candidate’s performance is their own.
Uneven Playing Field - If some candidates use AI assistants while others don’t—or if some tools are better than others—it creates disparities. Interviewers might struggle to compare candidates fairly when tool proficiency overshadows raw ability.
Obscures Communication Skills - ML, AI, and DS roles demand clear communication, whether explaining models to stakeholders or collaborating with teams. Overusing AI assistants could limit opportunities to showcase these soft skills, as candidates might lean on pre-baked responses.
How Candidates Should Use AI Assistants
To keep things fair and productive, candidates should use AI assistants thoughtfully, with clear boundaries:
Prep Like a Pro: Use AI assistants during practice to simulate interviews, debug code, or explore ML concepts. For example, ask for a breakdown of gradient descent or mock questions on A/B testing to build confidence.
Limited Live Support: In interviews, candidates could be allowed to query assistants for quick clarifications—like “What’s the syntax for a Keras callback?”—but not for entire solutions. This mimics real-world tool use without handing over the reins.
Be Upfront: If permitted, candidates should disclose when they’re using an assistant. Transparency builds trust and lets interviewers focus on how the candidate applies the tool’s output.
Explain, Don’t Echo: Interviewers should ask candidates to walk through their reasoning, even if an AI assistant helped. This ensures the candidate can connect the dots and isn’t just parroting.
These guidelines let candidates harness AI’s power while proving their own chops, aligning interviews with how work actually gets done.
Why the ML, AI, and DS Interview Process Needs a Reboot
The current interview playbook—LeetCode grinds, whiteboard marathons, and trivia-style questions—is a relic that’s increasingly out of sync with ML, AI, and DS realities. Here’s why it’s time to rethink it:
Misaligned Metrics - Cranking out a perfect binary tree inversion in 20 minutes doesn’t prove you can build a recommendation system or debug a neural network. Real work involves iteration, experimentation, and tools—not timed stunts.
Coding Overkill - While coding matters, ML and DS roles often hinge on data wrangling, model tuning, or business insights. Interviews that obsess over algorithmic purity miss the mark on assessing these core skills.
Unrealistic Pressure - The do-or-die vibe of interviews rewards test-taking hacks over genuine talent. Many brilliant candidates flinch under artificial constraints but thrive in realistic settings.
Tool Blindness - Modern workflows lean on libraries like PyTorch or tools like AI assistants. Banning these in interviews ignores how candidates actually solve problems, creating a disconnect between evaluation and execution.
Equity Issues - The grind-heavy process favors those with time, money, or elite pedigrees, sidelining diverse talent. A more practical approach would open doors for unconventional backgrounds.
A Better Way Forward:
Practical Projects: Give candidates a dataset to analyze or a model to tweak over a few hours, using any tools they choose. This tests end-to-end skills, from data cleaning to interpretation.
Team Simulations: Have candidates discuss a problem with interviewers, mimicking real collaboration.
Tool-Friendly Tasks: Allow limited AI assistant use to see how candidates integrate tech into their process.
Impact Focus: Ask candidates to tie their solutions to business or research outcomes, reflecting the strategic side of ML, AI, and DS.
This shift would prioritize skills that matter, ease artificial stress, and make hiring more inclusive.
Comparing Five Real-Time AI Interview Assistants
To see how AI assistants can aid candidates, let’s compare five tools designed for real-time interview support: LockedIn AI, Interview Sidekick, Final Round AI, Yoodli, and Ninjafy AI. Each has its own flavor, but they vary in focus, features, and fit for ML, AI, and DS interviews.
LockedIn AI
What It Does: A real-time co-pilot offering live answers, code solutions, and coaching for technical interviews, with features like Coding Copilot for coding challenges and OA Copilot for online assessments.
Pros:
Lightning-fast responses (claimed 116ms), ideal for high-pressure coding rounds.
Tailored for technical roles, with strong ML/AI/DS support (e.g., system design, scalability).
Works across Zoom, Teams, and Meet, with no app download needed.
Cons:
Heavy focus on live assistance could tempt overuse, risking ethical concerns.
Premium features may be pricier than some competitors.
Best For: Candidates tackling intense ML/DS coding or system design interviews who want real-time coding support.
Interview Sidekick
What It Does: Analyzes resumes and job descriptions to provide tailored answers and follow-up questions in real time, with Zoom integration.
Pros:
Shines for behavioral questions and explaining ML/AI projects.
Suggests smart follow-ups to impress interviewers.
User-friendly for non-coders.
Cons:
Weak on coding or deep technical support, limiting its ML/DS utility.
Responses can feel generic without precise inputs.
Best For: Candidates prepping for behavioral or mixed interviews where project storytelling matters.
Final Round AI
What It Does: Offers mock interviews, coding exercises, and real-time feedback, with a focus on tech roles like ML and DS.
Pros:
Robust question banks for algorithms, ML, and DS scenarios.
Detailed post-interview breakdowns to improve weak spots.
Simulates realistic interview pressure.
Cons:
Feature overload can overwhelm beginners.
Higher cost for full access may deter some.
Best For: Seasoned candidates grinding for top-tier ML/AI roles with heavy coding demands.
Yoodli
What It Does: A speech-focused AI that analyzes tone, pace, and clarity in real time, giving feedback to polish communication.
Pros:
Perfect for nailing whiteboarding or model explanations in ML/AI interviews.
Free tier is generous, making it accessible.
Real-time analytics help adjust delivery mid-interview.
Cons:
No technical support for coding or ML concepts.
Limited to verbal interviews, not async or written tasks.
Best For: Candidates who need to sharpen how they explain complex DS/AI ideas.
Ninjafy AI
What It Does: A discreet assistant providing real-time prompts and performance tracking, tailored to virtual interviews.
Pros:
Subtle interface minimizes distraction, blending into live settings.
Offers coding and ML prompts based on job specs.
Tracks progress across interviews for long-term growth.
Cons:
Stealth mode could raise ethical flags if undisclosed.
Less robust language support than others.
Best For: Candidates wanting low-key support for virtual ML/DS interviews.
Comparison Snapshot:
Technical Depth: LockedIn AI and Final Round AI dominate for ML/AI/DS coding and system design, with LockedIn edging out for real-time speed.
Communication Polish: Yoodli is unmatched for verbal clarity, critical for explaining models or experiments.
Discreet Support: Ninjafy AI excels at subtle prompts, but candidates must use it openly to stay ethical.
Behavioral Fit: Interview Sidekick leads for storytelling and non-technical rounds.
Cost vs. Value: Yoodli’s free tier and LockedIn’s mobile-friendly design win for accessibility, while Final Round’s premium features target serious grinders.
The right tool hinges on your interview’s demands—coding-heavy roles lean toward LockedIn or Final Round, while communication-focused ones favor Yoodli.
Wrapping Up: A New Era for Interviews
AI assistants in technical interviews are a game-changer, offering a chance to align hiring with reality while exposing risks like over-reliance or ethical slips. By allowing transparent, limited use, companies can test how candidates wield tools in context, rewarding resourcefulness without sacrificing rigor. But the bigger fix lies in scrapping the outdated ML, AI, and DS interview model—ditch the LeetCode traps and embrace projects, teamwork, and tool-friendly tasks that mirror the job. Tools like LockedIn AI, Interview Sidekick, Final Round AI, Yoodli, and Ninjafy AI show how candidates can prep smarter, each bringing something unique to the table.
As AI reshapes work, interviews must evolve too. Let’s build a process that values practical skills, creativity, and inclusivity over memorized algorithms. Candidates deserve it, and so do the teams hiring them.
Comments