Preserving Technical Accuracy in Resume Writing

Are you a student or early-career professional struggling to get callbacks after submitting your resume? I’ve been there. During my first year of grad school, I blamed the job market when I didn’t get a single interview for nearly seven months. I started applying for Summer 2024 internships in August 2023, but didn’t receive my first callback until March 2024. Over time, I began refining my resume based on what the industry values and what it takes to stand out. That made all the difference. Here are some of the most important lessons I’ve learned: 1. Keep the Format Simple Avoid horizontal lines, text-heavy formatting, or excessive bolding. They clutter your resume and make it harder to read. Could you stick to one page? If you can’t explain your work clearly and concisely, you’re not ready to present it. 2. Don’t Just List Tools or Describe the Problem, Explain What You Did Many students focus too much on the business problem (“Built a dashboard for retail analytics”) and gloss over the engineering behind it. Even worse, some just list the tools used: “Used Python, Flask, and AWS to build a service that did X.” Instead, go deeper. What did your Flask service do, exactly? What challenges did you face? What decisions did you make? As engineers, we’re expected to show technical depth. If your resume can’t reflect that, you’ll struggle to stand out, especially for technical roles. 3. Be Realistic with Metrics Many resumes include lines like: “Improved model accuracy from 12% to 95%.” This kind of stat, usually influenced by generic advice from career centers or the internet, raises red flags. It often signals that the project wasn’t technically complex to begin with. Instead of inflating numbers, focus on what you improved, how you improved it, and why your work mattered. Strong technical framing > flashy percentages. 4. Clarity > Buzzwords You might write something like: “Leveraged CUDA for token-level optimization of transformer inference under real-time constraints.” It sounds cool, but what does it mean? This happens when people assume the reader will be as familiar with the project as they are. But if someone in your field has to guess what you did, you’ve already lost them. Don’t rely on buzzwords to do the talking; let clarity drive the message. 5. Your Resume Isn’t for You Your resume isn’t meant to impress you. It’s intended to communicate what you’ve done to people who don’t share your background. Most first-round reviewers aren’t ML engineers or CUDA developers. They often rely on keyword checklists and rubrics to decide which resumes move forward. The one thing that matters is: Can you clearly explain what you did and why it mattered? That’s it. Feel free to put your thoughts in the comments. Follow me for more advice!

Career offices at colleges often struggle to create effective tech resumes. This may stem from the fact that many professionals in these offices lack a tech background. I’d like to share some observations that could be beneficial for everyone involved. One of the most significant issues I encounter is resumes that fail to adequately highlight technical skills and accomplishments. Important details such as programming languages, algorithms implemented, third-party technologies and libraries used, collaboration with others, testing processes, documentation, mentoring, computing environments, code reviews, version control, user feedback collected, and resulting decisions are frequently overlooked. I’ve seen many students omit entire internship or research projects simply because they are “still in development,” even when they’ve made substantial progress. For students who handle multiple responsibilities within a single position, resumes often become overly general, lacking the specificity needed to convey their unique contributions. I’ve found that having a brief conversation with candidates can help uncover valuable details: “What did you do day-to-day? Who did you collaborate with? Is there anything else you did that you haven’t mentioned? What was the most challenging aspect of this experience? What excited you the most? What did you work hard on that wasn’t fully completed?” Career centers typically do an excellent job of encouraging candidates to include metrics and frame their bullet points effectively, such as “improved X by Y through Z.” Their emphasis on quantifying achievements is invaluable. However, a lack of technical context can still render bullet points too general. For example, “Increased program efficiency by 22% utilizing and improving open-source repositories” sounds good at first, but it lacks critical details. What specific aspect of efficiency was improved? How did you contribute to the open-source repositories? How many repositories were involved, and what kind of contributions were made? These specifics can make the difference between a good resume and a great one. One useful strategy is to have a few people review your resume. After working with your career office, consider reaching out to alumni or other connections in tech for their input. With their feedback, you might even return to the career office for further refinement! --------------------------------------------- I am a software engineer and mentor, dedicated to helping international students and early career professionals not only survive but thrive in tech. ☀️ Check out the links on my profile 🌟 I am an amazon employee but thoughts are my own. 

📄 SERIES CONTINUES: Tech Resume Mastery – Tip #2 : Quantifying Your Technical Impact – The STAR Method Reimagined📄 After optimizing your resume's architecture, let's talk about the code that runs on it — your bullet points. As technical professionals, we need to quantify our impact with precision: The Technical Achievement Formula Every bullet point should follow this pattern: [Action Verb] + [Technical What] + [Business Why] + [Quantified Result] 1️⃣ What did YOU specifically do? (Not your team — your individual contribution) 2️⃣ Why did it matter? This is critical! Connect your work to business value: • Customer benefits • Operational improvements • Revenue generation • Cost reduction 3️⃣ What was the measurable result? This is where most candidates fall short! Example: Getting Latency Metrics Right Strive to bring the best data you can — especially for performance metrics like latency: “Improved latency” → Too vague “Improved latency by 25%” → Better, but lacks real-world context “Reduced latency by 80ms” → Preferred — clear and measurable “Reduced latency from 850ms to 120ms” → Best — full before-and-after view, high credibility The more specific you are, the easier it is for hiring managers to understand the technical and business value of your work. 👤 Tailoring by Role: What to Emphasize Software Engineers: Emphasize systems built, performance gains, and user impact Product Managers: Highlight strategic outcomes, user metrics, and market growth Technical Program Managers: Showcase delivery efficiency, cross-team execution, and system uptime Engineering Managers: Focus on team scaling, hiring velocity, retention, and organizational impact 📊 Quantification Strategies by Tech Domain: Backend: Throughput, latency, scalability metrics, cost reduction Frontend: Render time, conversion rates, A/B test improvements Infrastructure: Reliability metrics, deployment frequency, cost optimization Product: User metrics, revenue impact, market share Data: Processing time, accuracy rates, business insights generated And remember: It’s okay to guestimate — as long as your numbers are reasonable, grounded in reality, and defensible if asked. What’s one area of your work where quantifying impact could level up your resume? Drop it below — let’s sharpen it together. #TechResumes #EngineeringImpact #DataDrivenCareers #NewGrads #EngineeeringLeadership #TechLeadership

AI-Generated CVs: Are They Accurate Enough? AI tools are crafting CVs more frequently, but not always accurately. 🚫 I've seen CVs highlighting emerging technologies (IoT, Cloud, AI) but omitting crucial ERP implementation experience. One candidate blamed AI for listing an MBA they didn’t have. 😵 ✔ Here’s how to ensure your CV reflects your true experience: 1️⃣ Verify every detail. Double-check all entries for accuracy. AI can fill gaps with assumptions. 2️⃣ Customise, don’t automate. Tailor each CV to the role. Avoid generic, AI-generated content. 3️⃣ Highlight genuine achievements. Use real examples to showcase your skills and experience. Focus on business benefits. 4️⃣ Consider your audience. If your CV will be reviewed by a non-technical individual (CFO, CEO, CPO), will they understand it? Avoid technical jargon and focus on business outcomes rather than IT. 5️⃣ Seek professional review. Have a trusted colleague or mentor review your CV for errors. Ask them to be brutally honest. In a world where AI can misrepresent, integrity is key. Keep your CV honest and precise. How do you ensure your CV stays accurate? Share your tips below! #technology #recruitment #manchester #womenintechnology 🌟📄👥

Once in a while, I help people by giving feedback on their resumes. However, spending more than an hour on Zoom meeting is not necessarily very scalable for me. So I am sharing the main points here. 𝐑𝐞𝐬𝐮𝐦𝐞 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐆𝐮𝐢𝐝𝐞𝐥𝐢𝐧𝐞𝐬 𝐈 𝐮𝐬𝐮𝐚𝐥𝐥𝐲 𝐭𝐫𝐲 𝐭𝐨 𝐜𝐨𝐧𝐯𝐞𝐲: - 𝐀𝐝𝐨𝐩𝐭 𝐚𝐧 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞 𝐏𝐞𝐫𝐬𝐩𝐞𝐜𝐭𝐢𝐯𝐞: When reviewing your resume, try to eliminate personal biases. Aim to view it through the eyes of a third party, ideally a hiring manager. Your goal is to help them quickly assess your potential and reduce any perceived risks in hiring you. - 𝐅𝐨𝐜𝐮𝐬 𝐨𝐧 𝐒𝐮𝐛𝐬𝐭𝐚𝐧𝐜𝐞: Your resume should reflect real achievements and contributions. While clear and concise language is important, the content itself—what you’ve done—matters most. Strong but simple English is usually sufficient. - 𝐄𝐧𝐬𝐮𝐫𝐞 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲: Double-check your resume for any technical inaccuracies. These can undermine your credibility. - 𝐄𝐥𝐢𝐦𝐢𝐧𝐚𝐭𝐞 𝐓𝐲𝐩𝐨𝐬: Typos can leave a negative impression, so make sure your resume is free from them. - 𝐀𝐯𝐨𝐢𝐝 𝐕𝐚𝐠𝐮𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞: Do not use weasel words. Steer clear of using ambiguous or filler words that don’t add value. Each word should be purposeful and impactful. - 𝐁𝐞 𝐃𝐞𝐥𝐢𝐛𝐞𝐫𝐚𝐭𝐞 𝐰𝐢𝐭𝐡 𝐄𝐯𝐞𝐫𝐲 𝐖𝐨𝐫𝐝: Evaluate and question the necessity of every word on your resume. If it doesn’t contribute to your narrative, consider removing it. - 𝐂𝐫𝐚𝐟𝐭 𝐂𝐥𝐞𝐚𝐫 𝐁𝐮𝐥𝐥𝐞𝐭 𝐏𝐨𝐢𝐧𝐭𝐬: Each bullet point should convey a clear message with a specific purpose. Avoid generic statements and ensure that each point highlights your unique contributions. - 𝐃𝐞𝐭𝐚𝐢𝐥 𝐘𝐨𝐮𝐫 𝐑𝐨𝐥𝐞 𝐚𝐧𝐝 𝐈𝐦𝐩𝐚𝐜𝐭: Clearly define your role in projects, specifying your responsibilities and the impact you had. If the project was long-term, you might also mention the techniques or methodologies you explored. - 𝐋𝐞𝐧𝐠𝐭𝐡 𝐅𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲: While the one-page rule is common, don’t feel restricted by it. If you need more space to effectively showcase your skills and experiences, go beyond one page. However, make sure that every word on your resume justifies its place. - 𝐈𝐦𝐩𝐚𝐜𝐭 𝐰𝐢𝐭𝐡 𝐦𝐞𝐭𝐫𝐢𝐜𝐬: Add impact using relevant ML or business metrics vs baselines when possible. Interested in Machine Learning career? visit https://lnkd.in/gYDkxvEQ Follow our page: https://lnkd.in/gCfsrRTW Follow me on YouTube: https://lnkd.in/geqHDsGJ #MachineLearning #AppliedScience #TheCuriousCurator