Engineering Career

If you’re AI-curious but can’t decide where to start, this one’s for you 👇 The AI space is vast. Buzzwords fly. Roles overlap. And it’s easy to get stuck wondering: 👉 Should I become a Data Scientist, ML Engineer, or Product Manager? Instead of chasing titles, map your strengths and figure out where you fit best in the AI lifecycle. 📌 I put together this infographic + a blog post to help you find your lane, with 10 clear roles you can actually train for (even without a PhD or a Stanford badge). 🚀 The 10 Career Paths in AI, Simplified: ➡️ AI/ML Researcher or Scientist – creating new algorithms, publishing papers, pushing the frontier ➡️ Applied ML Scientist / Data Scientist – solving real-world problems with models and experimentation ➡️ ML Engineer / MLOps / Software Engineer (ML) – taking models to production and scaling them ➡️ Data Engineer – building the infrastructure to move and manage data ➡️ Software Engineer – writing core product code with ML components ➡️ Data Analyst – analyzing data to drive insights and business impact ➡️ BI Analyst – working with KPIs, reporting, and decision frameworks ➡️ AI Consultant – advising teams and clients on adopting AI responsibly ➡️ AI Product or Program Manager – aligning AI capabilities with user needs and business goals ➡️ Hybrid Roles – wearing multiple hats across technical and strategic functions 🧭 How to choose the right one for you: → Start with your natural strengths: coding, communication, business thinking, or data sense → Identify the part of the AI lifecycle you enjoy most: research - build - deploy - iterate → Stack the right skills intentionally: • Coders: Python, PyTorch, prompt design, eval frameworks • Data Infra: SQL, Spark, Airflow, Lakehouse, vector DBs • Insights: Analytics, causal reasoning, dashboard tools • Translators: AI roadmap building, governance, storytelling → Focus on shipping evidence of work: demo apps, notebooks, open-source PRs, or experiments → Develop a T-shaped skill profile – go deep in one role, but stay conversational across others 💡 A few truths to keep in mind: → You don’t need to be a “10x coder” to work in AI → Problem-solving > job titles → Projects > perfect resumes → Cross-functional skills are a force multiplier – clear writing, ethical reasoning, and stakeholder empathy go a long way → There’s no “entry-level” in AI – just entry-level impact 📖 Curious to explore deeper? Check out the full blog, and save the infographic to use as a compass for your AI journey: https://lnkd.in/daQNHPyg

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

Engineering research and development-related (ER&D) hiring has jumped almost 60% over last year, reports The Economic Times, citing data from Teamlease. Driving this trend is the demand from the manufacturing and automotive industries and global capability centres (GCC) of multinational companies and service providers. The ER&D sector employs around two million people in the country, according to industry estimates. Manufacturing and automobile companies with existing manufacturing setups in India are extending their R&D, technology, and design centres across the country. The result has been an increase in demand for contract employees in the field as companies setting up GCCs and centres of excellence (CoEs) look for talent on a trial basis, said Sunil C, CEO at Teamlease Digital. Companies have also been keen on converting contract roles to full-time ones. Top profiles in demand across IT services and GCCS include embedded C, cad/cam, automotive domain tools, and data engineering. What makes the ER&D sector a meaningful career prospect for professionals? Entire teams within a geography get to work on larger and more critical parts of projects, said Snehil Gambhir, Partner, Director-Transformation at BCG India, adding that automotive, aerospace, electronics, and semiconductor sectors are driving the demand in India. He also says companies are looking at India to build an alternative supply chain and delivery pool. The space constituted around 16% of the $245 billion Indian technology sector revenue, and grew faster over the previous year compared to the overall growth, as per FY23 Nasscom data. The demand for talent is also expected to grow at a compound annual rate of 12-15% over the next five years, according to Rohit Gupta, Head of Technology Center India at Thyssenkrupp. Source: https://lnkd.in/ewWR452G ✍️: Isha Chitnis 📸: Getty Images #AutomotiveIndustry #Engineering #ResearchandDevelopment #Aersopace #Manufacturing

Demand for software engineers is as bad now as it was during the peak of the pandemic. In 18 months, the number of data engineering job openings on LinkedIn has been cut in half. It’s not the end of technical roles, but the data shows demand is changing. Trying to replace engineers with low-code tools and #AI code generators fails. However, most platforms now support technical and nontechnical co-development environments. A new type of technical role has gained traction in businesses. Smaller software and data teams build frameworks and tools for nontechnical developers on a co-development platform that’s available to anyone in the business. Meta and JPMC implemented enterprise-wide co-development platforms and are seeing massive benefits. One or two technical resources are embedded into the nontechnical team to support their development efforts. Solutions are developed faster and more closely meet customer and business needs because domain experts build them. A few advanced R&D teams still operate in the business but focus on building larger, more innovative products. Teams supporting incremental features and internal operations initiatives are going away. While demand is falling in some areas, it’s rising for the embedded, business-facing technical roles. There are three levels: 1️⃣ Domain Expert Technical ICs: Value-centric #data engineers, data analysts, and software engineers who support nontechnical developers and are embedded into their organizations. 2️⃣ Product Manager Engineers: Technical capabilities with deep product strategy expertise. They know what to build and can implement high-value features independently or with a team. 3️⃣ Technical Strategists: Technical experts who work with executive and C-level leaders. They bring data, models, and rapid product development capabilities to the strategy planning and implementation processes. I have taught data and AI strategy, #ProductManagement, and value-centric capabilities to technical ICs for 8 years to meet today's demand shift. Technical roles are evolving, and amazing opportunities exist for people who adapt.

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!

As more Australian companies move from experimenting with artificial intelligence to deploying it at scale, hiring for AI Engineers is surging. AI Engineer was ranked the fastest-growing job in LinkedIn's Jobs on the Rise report, with experts saying the demand is being fuelled by urgency. "Every leader is either challenging their current 'AI strategy' or scrambling for one, which has led to a surge of vague 'AI Engineer' roles that nobody, not even the hiring company, can properly define," says Ellis Taylor, Director at tech recruitment consultancy Real Time. He explains the "smartest demand" is coming from sectors with tangible problems, such as healthtech companies applying AI to diagnostic imaging or fintech firms using AI to solve fraud detection.  "The companies winning are the ones hiring to solve a business problem, not just to have an AI team," he says. Amid the hiring boom, expectations of what the role delivers are changing, with companies prioritising real‑world delivery over technical knowledge. Dr. Thomas Kelly, CEO and Co-Founder of AI healthcare startup Heidi, says the strongest candidates clearly explain their impact. "We look for concrete examples of what was built, why it mattered and the individual's role in delivering it. It also helps to draw clear links between past experience and what we do at Heidi, particularly around applied AI and healthcare," he says. According to Chipo Riva, Senior Consultant at recruitment agency Talenza, employers want engineers who can deploy AI in environments "where data is messy, stakeholders are sceptical and commercial outcomes matter more than technical elegance". "What truly differentiates candidates is the ability to translate business problems into AI solutions and explain trade-offs clearly to non-technical stakeholders," she says. "The market is saturated with people who can code. It's desperately short of people who can code and collaborate effectively across business functions." What does the rise of AI engineers say about how the workforce is changing? Share your thoughts in the comments below. See our full LinkedIn Jobs on the Rise list: https://lnkd.in/JOTR26Au By Brendan Wong #JobsOnTheRise

𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐜𝐚𝐫𝐞𝐞𝐫 𝐚𝐬 𝐚 𝐥𝐢𝐧𝐞𝐚𝐫 𝐬𝐜𝐫𝐢𝐩𝐭 𝐢𝐬 𝐚 𝐛𝐮𝐠. It’s actually a 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐬𝐲𝐬𝐭𝐞𝐦 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 requiring high availability and fault tolerance. I realized that choosing a specialization in tech—be it Cloud Architecture, DevOps, or Full Stack—follows the same heuristics we use for 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝐬𝐢𝐠𝐧. Here is the breakdown of the "𝐂𝐚𝐫𝐞𝐞𝐫 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞" protocol: 1. 𝗗𝗮𝘁𝗮 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻 (Know What You Like): Just as we analyze logs to understand system behavior, analyze your history. What topics do you advocate for during lunch? What GitHub repos do you star? This is your baseline telemetry. 2. 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗣𝗿𝗼𝗳𝗶𝗹𝗶𝗻𝗴 (Heatmaps): In the sketch, I drew a heatmap matching "Good At" vs. "Like." In engineering terms, this is finding the sweet spot between 𝗧𝗵𝗿𝗼𝘂𝗴𝗵𝗽𝘂𝘁 (volume of work you can handle) and 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 (how much drag you feel doing it). 3. 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗗𝗲𝗯𝘁 𝗔𝘃𝗼𝗶𝗱𝗮𝗻𝗰𝗲 (The 'Yuck' Stuff): This is crucial. Just because you are efficient at cleaning up messy legacy code doesn't mean you should specialize in it. If a task has high proficiency but low satisfaction, it represents future burnout—essentially, 𝒄𝒂𝒓𝒆𝒆𝒓 𝒕𝒆𝒄𝒉𝒏𝒊𝒄𝒂𝒍 𝒅𝒆𝒃𝒕. Deprecate these tasks early. 4. 𝗘𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗔𝗣𝗜 𝗖𝗮𝗹𝗹𝘀 (Ask the Big Kids): Don't rely on cached data. Poll external nodes (Seniors, Principals). Ask about their daily stack, their leadership exposure, and their context switching overhead. 5. 𝗧𝗵𝗲 𝗖𝗔𝗣 𝗧𝗵𝗲𝗼𝗿𝗲𝗺 𝗼𝗳 𝗖𝗮𝗿𝗲𝗲𝗿𝘀 (Pick 2 & Look Closer): You usually have three metrics: 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝗙𝘂𝗻, and 𝗣𝗮𝘆. It is rare to get strong consistency across all three immediately. Analyze your "Career Castles" (A vs. B) and decide which trade-off is acceptable for this specific epoch of your life. 6. 𝗥𝗼𝗹𝗹𝗶𝗻𝗴 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 (Start): Analysis paralysis is the enemy of uptime. If the metrics are close, deploy the instance that you are leaning toward. You can always rollback or re-architect later. Your career isn't a waterfall model; it's agile. Iterate often. Don't worry about a path not working out, you can always roll back :) #CareerPath #SystemDesign #SoftwareEngineering #TechCareers #Sketchnote

🚀 Which Job Roles Are in Highest Demand in Semiconductor Companies? 🧪 1. Process Engineers (Highest Demand in Manufacturing) What they do: Optimize each step of chip fabrication: lithography, etching, deposition, CMP, ion implantation. Why in demand: A modern fab has 1,500+ process steps. Every percentage gain in yield = millions saved. Analogy: Like chefs who fine-tune the recipe so the bakery (the fab) produces perfect cakes every time. 🔍 2. Equipment Engineers What they do: Maintain and optimize multi-million-dollar machines: EUV lithography Etchers Deposition tools Why in demand: Each EUV machine costs $200M+. Downtime = millions lost per hour. Analogy: They’re the F1 pit crew of the fab — equipment must run at perfect performance. ASML added 14,000 new engineers in the past few years, primarily equipment specialists. 🏭 3. Yield Engineers What they do: Identify defects, improve yield, reduce scrap. Why in demand: A single 300mm wafer can hold thousands of chips. 1% yield improvement = millions of dollars. 📐 4. Design Engineers (Chip Designers) Includes: RTL engineers Digital/Analog designers ASIC engineers SOC architects Memory designers Why in demand: AI, 5G, EVs, and cloud computing need new chip designs every year. Analogy: These are the architects designing skyscrapers (chips) before builders construct them. Example: NVIDIA and Apple hire hundreds of SoC and GPU design engineers annually for next-gen chips. 🧠 5. Verification Engineers (Critical in Chip Design) Role: Test the chip design thoroughly before fabrication. In demand because: Verification consumes 60–70% of total design time in large SOC projects. Analogy: They’re like test pilots ensuring the airplane is safe before passengers fly. 🔌 6. Test Engineers Role: Develop strategies to test finished chips: E-test Wafer-level test Final test Why in demand: Testing accounts for up to 25% of chip production cost. Example: Qualcomm and MediaTek employ massive test engineering teams for smartphone SOCs. 🔧 7. Packaging & Assembly Engineers (OSAT Roles) Includes: Advanced packaging 2.5D/3D integration TSV, chiplets, CoWoS Thermal management Why in demand: Packaging is now as important as transistor scaling. Analogy: Like building multi-storey buildings with tight plumbing/electrical systems stacked on top. Data: TSMC’s CoWoS capacity demand increased 3× in 2023–24 due to AI chips. 🌡️ 8. Materials Engineers & Chemists Role: Develop gases, photoresists, slurry, deposition materials, CMP chemicals. Why in demand: Every advanced node (5nm, 3nm, 2nm) needs new materials. Analogy: Just like Michelin-star chefs need specialty ingredients, fabs need ultra-pure electronic chemicals. Example: JSR, BASF, and Shin-Etsu actively hire material scientists for EUV photoresists. ~~~~~ If you are looking to invest in semiconductors and need expert insights, drop us a DM.

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

You do not get promoted to Staff by doing Senior work perfectly. You get there by making Staff-level thinking impossible to ignore. I have coached hundreds of mid-senior engineers through this exact transition. The gap is almost never technical. It is almost always about how visible that thinking is to the people making the decision. Here is how to close that gap before the title catches up: 𝗦𝘁𝗲𝗽 𝟭. 𝗢𝗽𝗲𝗿𝗮𝘁𝗲 𝗼𝘂𝘁𝘀𝗶𝗱𝗲 𝘆𝗼𝘂𝗿 𝗮𝘀𝘀𝗶𝗴𝗻𝗲𝗱 𝘀𝗰𝗼𝗽𝗲 Weak: Executing within your current scope and waiting to be given bigger problems. Strong: Proactively identifying gaps, risks, or dependencies across adjacent teams and bringing a proposed solution before anyone asks. Staff engineers are not given broader scope. They demonstrate it until the title catches up. 𝗦𝘁𝗲𝗽 𝟮. 𝗠𝗮𝗸𝗲 𝘆𝗼𝘂𝗿 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝘃𝗶𝘀𝗶𝗯𝗹𝗲, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝘆𝗼𝘂𝗿 𝗼𝘂𝘁𝗽𝘂𝘁 Weak: Delivering results and trusting the work to speak for itself. Strong: Writing up your decision-making process, sharing architectural tradeoffs in design reviews, and documenting the reasoning behind the calls you made. Senior engineers are remembered for what they built. Staff engineers are remembered for how they thought. 𝗦𝘁𝗲𝗽 𝟯. 𝗕𝗲𝗰𝗼𝗺𝗲 𝘁𝗵𝗲 𝗽𝗲𝗿𝘀𝗼𝗻 𝗼𝘁𝗵𝗲𝗿 𝘁𝗲𝗮𝗺𝘀 𝗿𝗲𝗮𝗰𝗵 𝗼𝘂𝘁 𝘁𝗼 Weak: Building a strong relationship with your manager and staying within your team's orbit. Strong: Making yourself a trusted technical voice across teams. Being the engineer others consult on architecture decisions, technical risks, and cross-team dependencies. A Staff engineer's influence does not stop at the team boundary. Neither should yours. 𝗦𝘁𝗲𝗽 𝟰. 𝗢𝘄𝗻 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗹𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 𝗼𝗳 𝗮 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 Weak: Solving the problem you were handed and moving to the next one. Strong: Identifying the problem, aligning stakeholders, driving the solution, measuring the outcome, and communicating the impact to the right people. Seniors solve problems. Staff engineers own them. The engineers who make it to Staff are not the ones who waited to be promoted. They are the ones who made it impossible not to promote them. Save this before your next performance conversation. If you are doing Staff-level work but your title has not moved, comment NEXT LEVEL. Let me show you exactly how to close that gap.