Engineering Career

Your robotics degree isn’t about ROS, Python, Arduino or the latest NVIDIA SDK. It’s about the fundamentals. Control theory. Linear algebra. Differential equations. Kinematics. Probability and stochastic processes. Writing a ROS2 navigation node is simple and takes 30 minutes. Designing a new SLAM algorithm that outperforms the current state of the art? That takes deep math, system design, and time. Understanding why the robot behaves the way it does, why that PID loop oscillates or why a Kalman filter diverges all takes years of math, experiments, and mistakes. A while back, I had a conversation with an engineering manager at Amazon Robotics. She told me something that stuck: “I’d rather hire someone deep in mathematics than someone who just codes fast. I can teach anyone to program, but mathematics takes years to build. When the real hard problems show up, it’s the math person who usually cracks it.” That line never left me. Because it’s true. When systems start failing in unexpected ways, when you’re debugging sensor noise, latency, or state estimation drift. It’s rarely about syntax. It’s about understanding the underlying models. The quiet engineers who build these core systems rarely make the headlines. They may not always earn startup-style salaries. But they’re the ones pushing the boundaries of robotics—making it possible for others to build the next generation of products on top of their work. We need both kinds of engineers. The ones who build the core systems, and the ones who build on top of them. One drives the innovation. The other drives adoption. Both move robotics and the society forward. That’s how the robotics ecosystem thrives. What kind of engineer do you want to be?

Most GIS professionals are learning the wrong skills. That might sound harsh, but it’s the truth. The job market has changed, and the geospatial skills many programs teach are stuck in the past. The tools and techniques that got you a GIS job five years ago won’t cut it today. Here’s what no one tells you: Python, SQL, and cloud-native workflows are the new must-haves for geospatial professionals. These aren’t just “nice to know” skills—they’re the difference between landing a job in modern GIS or being left behind. 💡 Let’s break it down: 1️⃣ Python – Automate workflows, analyze geospatial data, and tap into powerful libraries like Geopandas, Rasterio, Apache Sedona, and PySAL. Python is the language of data, and GIS is no exception. 2️⃣ SQL – Query, transform, and analyze spatial data at scale. Employers want analysts and engineers who can handle databases, not just files. 3️⃣ Cloud – Cloud-native tools like cloud storage, Apache Iceberg, scalable processing systems, and GeoParquet/Cloud Optimized GeoTIFFs are now the backbone of geospatial work. Handling large data volumes just isn't feasible any more (neither is waiting hours or days for jobs to process) But here’s the problem: GIS education hasn’t caught up. Too many programs still teach workflows that rely on outdated desktop tools and ignore the skills that job listings actually demand. 🎯 What does this mean for you? It means you need to take charge of your own learning. Employers aren’t waiting for schools to modernize their curriculums. Start learning the tools and techniques that are driving geospatial careers forward: Explore modern Python libraries like Geopandas and Leafmap. Learn SQL for geospatial databases like PostGIS and Apache Sedona. Experiment with cloud-native formats like GeoParquet and COGs. The good news? These tools are accessible. Many are open-source, and there’s an incredible community online willing to share knowledge. The geospatial industry is transforming. The question is: Are you ready to evolve with it? Let’s talk: What GIS skill do you wish you had learned earlier? What do you see as the future of geospatial careers? Drop your thoughts below.

After appearing in 20+ interviews, I have figured out how much Coding skills exactly required for each of Data Scientist, ML Engineer and AI Engineer roles. Do not get confused — these are separate roles and requires different coding expertise. 1. Data Scientists (product/analytics roles): - SQL is non-negotiable. - Python scripting + Pandas/NumPy are your daily tools. - DSA easy-medium (Leetcode enough!) Interviews often include: • SQL case studies • Data wrangling challenges (I got it many times!) • One Leetcode easy/medium — often string or array manipulation 2. Machine Learning Engineers: - You're expected to think like an engineer and a data scientist. - Writing production-quality code matters. - You’ll be tested on coding patterns, not just scikit-learn usage. Interview Expectations: • Leetcode medium (sometimes hard - but less chance) • Algorithmic thinking (e.g., optimizing training loop performance) • ML system design (batch vs streaming, deployment strategies) 3. AI Engineers / Applied Scientists: - Especially in LLM/Deep Learning-focused teams, system design + performance-aware coding is key. In this role you’ll deal with: - Large-scale data - GPU memory optimization - Custom training loops - Vector search, graph traversal, and more Coding rounds often include: • DSA-heavy problems (graphs, trees, recursion, DP) • Code optimization tasks • Python internals, multi-threading + processing, OOPs, memory & complexity analysis Coding is totally non-negotiable in every case. No matter how much theoretical knowledge you hold, if you can't solve live coding in interview you are straightaway rejected. For system coding I have started writing ML system design articles, feel free to check-out. [Link in comment]

Mechanical, hardware, and chemical engineers are among the hardest-to-fill/hardest-to-hire roles for climate tech companies, with time-to-fill times longer than even machine learning engineering roles. ClimateTechList teamed up with data scientist/engineer Jason Zou to analyze our dataset of ~60,000 job posts from 900 climate tech companies posted in the last 6 months. Specifically, we found that the time-to-fill for the following roles were: - Sales: 31.9 days - Marketing: 35.9 - Analyst: 36.0 - Design: 38.5 - Data Science: 40.3 - Product Management: 41.5 - Operations: 42 - Electrical Engineer: 47.1 - Software Eng: 48.2 - Machine Learning Eng: 48.3 - Mechanical Eng: 49.0 - Hardware Eng: 50.2 - Chemical Eng: 51.5 Engineering jobs associated with physical production are hard to hire, namely mechanical engineering, hardware engineering, and chemical engineering, all of which take almost 2x as long to fill (50 days) as sales jobs. Even machine learning engineering positions, in high demand from the AI boom, are filled at a slightly faster rate than these 3 positions Possible reasons for this effect - many of these jobs require in-person work, which makes job matching jobs to candidates inherently more difficult - Federal legislation of the last few years- Bipartisan Infrastructure Law, Inflation Reduction Act, CHIPS Act are all driving massive investments into U.S. physical infrastructure and manufacturing. These investments disproportionally require talent with physical-product engineering skills more than software engineering skills. 👉 For more insights on hiring trends by company, country and climate tech vertical, see our latest climate tech hiring trends report here: https://lnkd.in/gpMCaSZ6 #climatetechlist #decarbonization #energytransition #chemicalengineering #mechanicalengineering #hardwareengineering #hiringtrends

Embedded software engineering isn’t just about writing code in an AC room… sometimes, it means testing your ESP32 firmware under the hot sun, with dust in your laptop bag. As an IoT firmware developer working with ESP32, I’ve come to realize something very real: What works perfectly in the IDE… can crash and burn in the real world. In the lab: ✅ Stable power supply ✅ Reliable serial monitor ✅ Fast Wi-Fi In the field: ⚡ Power drops ❌ Wi-Fi range issues ☀️ Harsh environments 🤯 And random resets that make no sense I remember one field test where my ESP32-based sensor node randomly rebooted every few minutes. I thought it was a bug in my code… Turns out — brownout detection was kicking in due to voltage dips. That’s when I learned: IoT firmware isn’t about perfection in the lab — It’s about survival in the real world. What ESP32 field testing taught me: 1️⃣ Watchdog timers and brownout handling are non-negotiable 2️⃣ Retry logic for Wi-Fi and MQTT is a must 3️⃣ Use NVS (non-volatile storage) to save crucial data between reboots 4️⃣ Add OTA updates — once deployed, field updates can be life-saving 5️⃣ Battery management matters — especially in remote IoT deployments Why it excites me: Because it’s not just about blinking LEDs. It’s about building connected intelligence that can sense, communicate, and endure. ESP32 has shown me how powerful and fragile embedded systems can be — all at once. Have you ever taken your ESP32 project out into the real world? What’s the most unexpected issue you faced? Let’s share those stories — they help all of us grow. #ESP32 #IoT #FirmwareDevelopment #EmbeddedSystems #RealWorldTesting #Microcontrollers #EdgeComputing #CProgramming #WiFi #OTAUpdates #EngineeringLife

You can waste years in your engineering career if you don’t learn these eight lessons early. I learned them the hard way — you don’t have to. 👇 1⃣ Technical Excellence is the Minimum Your qualification gets you in the door. What gets you promoted? - Solving big problems - How you communicate, collaborate, and innovate - Having the right sponsors 2⃣ Find Mentors, Fast A good mentor will save you time from "solving big problems" that have already been solved or are unnecessary. You don't always have to formalise the mentorship, instead: - Write down your problems - Try finding solutions online or from books - When all fails, ask the mentor for guidance The engagement is usually better this way. 3⃣ Find Sponsors, Super Fast Many of the great opportunities I've had in my career have been from people I didn't even know vouching for me in rooms that I wasn't in. These are called sponsors. How do you get sponsors? - Be known to be great at something (aka niche down) - Communicate your aspirations - Build genuine relationships - Don't be afraid to share your ideas 4⃣ Sort out the Gatekeepers One of the advantages of niching down is that you will know the experience, qualifications, certifications, and training that may hinder your career progress. - Focus on the key competencies - Pursue relevant certifications 5⃣ You don't Need a Title to Lead Being great at something (aka niching down) will give you the confidence to articulate your solutions more effectively and drive teams to achieve results. And you don't need a title to do the above. Moreover, the above will help you gain sponsors who may vouch for you when leadership positions, that come with fancy titles, become available. Build credibility, and the fancy titles will come. 6⃣ Learn the Business Side The "big problems" you solve need to translate into business success. Business success means protecting the sustainability of the company. In engineering terms, this means ensuring: - Profitability - Return on investment - Compliance - Safety 7⃣ Document Your work I don't know how many times I have gone back to previous work to assist with new designs or projects. Keeping a record of all reference documents, calculations, notes, and templates saved me a lot of time from reproducing work I had done previously. As the saying goes: Today's work becomes tomorrow's evidence. 8⃣ It's a Marathon, Not a Sprint Race Every great engineer and leader was once a novice who invested time to gain competency. Be patient with yourself and take one day at a time. Some things just take time. __ 💬 Which of these lessons resonated with you? Or, what would you add from your journey? ___ 📌 Want to receive more content like this? Follow me -> Nkululeko Thusini I am also working on my weekly newsletter. Subscribe here: https://lnkd.in/dgVzxPkb

If you're in tech, you're sitting on a goldmine right now. While everyone's debating AI job displacement, the engineering sector is quietly becoming the biggest AI beneficiary. The World Economic Forum projects 78 million net new jobs by 2030, and IT and Engineering is leading the charge. This shift is creating entirely new job categories that didn't exist two years ago. Here are five emerging growth areas for IT and Engineering: 1. AI-native product development → AI Product Managers who understand ML lifecycles and enterprise pain points. 2. AIOps infrastructure → MLOps engineers are moving companies from AI experiments to production. Every enterprise needs these skills. 3. AI cybersecurity → Red teamers for LLMs are literally paid to break AI systems.  4. Enterprise data infrastructure → Vector database engineers managing RAG pipelines are helping AI systems access the right information at the right time. 5. Vertical AI specializations → LegalTech AI specialists, FinTech AI analysts, HR tech AI specialists—domain expertise + AI fluency is the new superpower. The numbers back this up: $632 billion in AI spending (including applications, infrastructure, and IT services) by 2028. This will lead to new AI roles in engineering, product, data, and operations to maintain these AI systems. Bottom line: The engineers who adapt fastest will have the most opportunities. In my latest newsletter, I break down exactly how to transition into each of these roles, plus the specific tools and skills that matter most. What AI role are you most curious about? #AI #Engineering #IT #FutureOfWork

The long road to career success is a two-way street between the efforts of the manager and the individual employee. We traversed one way in a recent post discussing ways in which managers can help their teams and employees succeed. Now, I would like to take a stroll to the other side and share some insights from my own experiences as well as suggest some ways people can forge their path.   The most important way to take charge of your own career is self-advocacy. It starts by picking a destination or at least direction. Then looking at the different roads that lead toward the industry or discipline of your choice so you can start advocating for opportunities to learn and to take responsibilities that will get you there.   While a “road map” is important, I also recommend keeping an open mind in the face of an unexpected detour or fork in the road. In my own career there were several pivotal moments where I faced choices that seemed less than ideal at first. But these detours turned out to be invaluable learning experiences that shaped my professional journey. One such moment came early in my career. I was working on payload fairings for rockets, a role that I thoroughly enjoyed and found engaging, but one that landed squarely in the middle of my comfort zone. Sure enough, discomfort came shortly, in the form of the Berlin Wall falling. The event triggered a domino effect of restructuring, program cuts and workforce reductions. I was asked to shift my focus to working on boosters — a task I perceived as far less exciting.   Reluctantly, on my manager’s advice, I decided to give it a shot. I embraced the work with curiosity and immersed myself into learning about composites design, stainless steel tank design, and leading a comprehensive test and development program. The decision proved to be a turning point in my career. We presented our findings from the test program I led to NASA and the Air Force, and the experience broadened my perspective and skill set in ways I never anticipated.   A well-prepared traveler also keeps abreast with the conditions not only on their planned path but also alternative routes. For example, having knowledge about manufacturing and products makes for a better engineer. Another aspect that determines the quality of one’s journey is their fellow travelers. As vast as the industry space seems, it can sometimes be a small world. Maintaining good relationships and not burning bridges keeps you from getting lost with nowhere to go and no one to help.   For anyone embarking a journey for career advancement, my advice would be to stay open to embracing new skills, opportunities, and people. Who knows where the road may lead? In the famous words of Dr. Suess - “You’re on your own. And you know what you know. And you are the one who’ll decide where to go.” I look forward to your comments on your own career journeys! Happy travels!

I became an Amazon VP 20 years into my career. Meet Ryan Peterman, who became a Meta Staff Engineer in 3 years! He and I have each discovered and used the same process by different names: 1) He says "Exceed expectations at your level." I have called this "Do your job well." Whatever you call it, you cannot approach your manager about growth without first nailing your current job. 2) He says "Be direct with your managers about promotion." I have said, "Ask your manager how you can help the group that helps you grow." Both are conversations about your desire to do more and move up. 3) He says "Find next-level scope." I call my approach the Magic Loop and tell you to repeat asking for growth each time you finish a project or master a new responsibility. Both are about growing your scope to the next level. 4) He says "Maintain next-level behaviors and impact." Again, I say "repeat" the Magic Loop, which includes "Do your job well." Once you have expanded your responsibilities to new, harder challenges, then you must again demonstrate mastery. Ryan is today's Newsletter guest author, and he provides 12 pages of deep detail on how to "Speedrun" the promotion path from entry level to Staff Engineer. Read his article here: https://lnkd.in/g95v2SiW For the IC engineer track, it's hard to imagine going faster than Ryan did, so read his advice. For leaders, here is my actual career in summary: 1993: Engineer 1995: Lead Engineer / TPM 1996: Manager 1998: Director (midsize company) 2000: VP (startup, ~30 team members) 2001: VP (startup #2, ~15 team members) 2004: VP (startup #3, ~15 team members) 2005: Sr. Manager (Amazon, 6 team members) 2007: Director (Amazon, 22 team members) 2013: VP (Amazon, 500 team members) 2020: “Retired” to build my business, age 50 I made it to VP relatively young because I moved up quickly and consistently. Here is how you can move up as fast as possible: 1) Get recognized early. The first 30–180 days in a new role are crucial. Enter with a clear learning plan and work hard. First impressions last. 2) Understand what your manager needs. Do your job well. Ask what else your manager needs, then take care of it. As you get familiar, anticipate those needs without asking. Repeat this. 3) Get recognized. People who share their wins get promoted. Share your wins with your manager, skip-level, and others. 4) Take risks. Big wins require risk. Sometimes you’ll fail and need to recover--but no one builds a standout career by playing it safe. 5) Get specific guidance. This advice is general. To move faster, get targeted help: courses, coaching, and expert materials. For those aiming at executive leadership, enroll in one of my cohorts of Break Through to Executive: https://lnkd.in/gJ-HgWdk

Throughout my career placing professionals across organizational levels, I've observed a counterintuitive pattern: the most productive employees often experience slower advancement than their more strategically visible counterparts.   This disconnect occurs because organizations promote based on perceived value rather than task completion volume.   The Visibility Gap: Most daily work remains invisible to decision-makers who determine advancement opportunities. Being exceptionally busy often signals poor prioritization rather than exceptional value.   Strategic Positioning Over Task Execution: Advancement requires demonstrating impact on organizational priorities rather than individual productivity metrics.   Cross-Functional Relationship Building: Promotion decisions often involve input from multiple stakeholders beyond immediate supervisors, making broader organizational visibility crucial.   Solution-Oriented Communication: Contributing meaningfully to strategic discussions and problem-solving initiatives creates more advancement opportunities than silent execution of assigned tasks.   The professionals who advance most rapidly understand that career growth requires intentional visibility management alongside excellent performance.   This doesn't diminish the importance of quality work, but recognizes that career advancement operates on different metrics than productivity optimization.   For those feeling stuck despite strong performance, the solution often lies in shifting focus from task completion to strategic contribution and ensuring that value creation is visible to advancement decision-makers.   What strategies have you found most effective for translating excellent work into career advancement opportunities?   Sign up to my newsletter for more corporate insights and truths here: https://lnkd.in/ei_uQjju   #deepalivyas #eliterecruiter #recruiter #recruitment #jobsearch #corporate #promotion #promotions #careeradvancement #careerstrategist