7 posts tagged with "career"

A senior engineer walks into the promotion calibration meeting. They shipped a fine-tuned reranker that lifted retrieval quality eight points. They built the eval harness that turned a two-week QA cycle into a one-hour CI gate. They authored the prompt change that drove a two-point conversion lift. By any reasonable measure, they had a defining year.

They don't get promoted. The packet, as written, reads like "I tuned some numbers." The colleague next to them — who shipped a CRUD feature behind a launch banner with QPS, latency, and a Friday demo — gets the nod instead. The committee is not malicious. It is using a vocabulary it has, applied to a packet that didn't translate the work into that vocabulary.

This failure mode is now common enough to be a pattern. AI engineering work doesn't decompose cleanly into the artifacts that calibration committees were trained to evaluate. The packet template was written for deterministic systems shipped in deterministic ways, and the engineers who do the most leveraged work in the AI stack are paying the tax.

In early 2023, a flood of corporate AI training programs launched with the same selling point: we will teach your engineers prompt engineering. By the time most of them finished their first cohort, the specific techniques they were teaching had already been automated away by the models themselves. By 2025, the role of "prompt engineer" — briefly advertised at $200,000 salaries — was effectively obsolete. The training programs are still running.

This is the AI curriculum trap. It is not a problem of effort or budget. Organizations invest heavily in structured AI training, certification programs, and hiring rubrics built around tool proficiency. But the tools change faster than any curriculum can track, and the result is a permanent, structural lag: training programs are always teaching the AI engineering of 18 months ago.

In 2023, "prompt engineer" was one of the most searched job titles in tech. LinkedIn was full of engineers rebranding their profile summaries. Job postings promised six-figure salaries for people who knew how to coax GPT-4 into behaving. What the job descriptions didn't say was that many of the skills they listed were already on borrowed time — and that the engineers who noticed the difference between durable and decaying skills would end up in very different places by 2026.

The prompt engineering career trap is not that the field went away. It's that it changed so fast that skills built over 12 months became liabilities by the 18-month mark. Engineers who invested heavily in the wrong layer and ignored the right one found themselves holding expertise in things the next model revision made irrelevant.

The AI engineer with the strongest case for promotion on your team has a promotion packet that looks empty. Two quarters of work and the impact graph is a flat line. The eval-regression rate that used to spike to 12% on every model swap now sits at 4%. The $40k/month cost spike that finance was about to escalate never reached finance because somebody added a budget guard to the gateway. The P0 incident that would have made the company's status page never happened because a kill-switch tripped and routed traffic to the previous prompt version.

The packet has nothing to write in the "shipped X" column. The calibration committee sits down with two engineers side by side: one who shipped two visible features this half, one who quietly absorbed the load that made those features possible. The committee, doing what it has always done, rates the shipper higher. The infra-shaped engineer either takes a "meets expectations" rating they don't deserve and quits inside a quarter, or learns to write the packet in a language the committee can actually read.

A senior engineer at a mid-sized startup recently got a mediocre performance review. Their velocity was inconsistent — some weeks they shipped a ton of code, others almost nothing. Their manager, trained on traditional SWE frameworks, marked them down for output variability. Six weeks later, that engineer left for a competing team. What the manager didn't understand: the engineer's "slow" weeks were spent building evaluation infrastructure that prevented three categories of silent failures. Without it, the product would have been subtly broken in ways nobody would have noticed for months.

This pattern is playing out across engineering orgs right now. Teams that built their career ladders for deterministic software systems are applying those same frameworks to AI engineers — and systematically misidentifying their best people.

Every engineering organization has them. They don't own a product. They don't ship features users see. But without them, nothing works. They're the engineers who write the ETL pipeline that moves data from the billing system to the analytics warehouse. The ones who build the webhook handler that keeps Salesforce in sync with the internal CRM. The ones who maintain the API adapter layer that lets the mobile app talk to three different backend services that were never designed to talk to each other.

They are the glue engineers, and their work is the first category of software engineering being fully absorbed by AI agents.

The great plateau is the career state that the maintenance of the status quo consumes all your time and energy so that you cannot break through and reach the next level of your life.

Here is some advice to help you move forward.

1. Analyze for yourself based on lessons from others. Set clear and long-term goals.​

What are the factors that are pumping or dumping you? Are those factors serving your goals instead of contradicting your goals? Are those goals aligning with each other instead of violating each other?

Be realistic and refer to the base rate ratio when analyzing and setting the goal. For example, Jeff Bezos told a story about a handstand coach’s experience - most people believe that they can learn handstand in two weeks; however, it usually takes six months. When you get stuck, the answers are more likely to be from others, from reality, not from yourself.

Stop aiming at a moving target. Your current situation is possibly what you desired four or five years ago. Don’t be too greedy :)

2. Release burdens and optimize the process.​

More specific to those dumping you and wasting your time and energy, could we remove those costs? Maybe that will hurt the upside temporarily; however, could that be the hockey stick growth afterward?

Take a sheet to list your daily operations and mark them as burdens or not. If yes, how to remove them?

3. Make time for deliberate curiosity.​

To shake yourself to leave the local optima, you need to set aside some time specifically for something new. The process seems useless at the beginning and may take long time.

You have to be patient. Like what Steve Jobs did when he returned to Apple after the exile, he cut less profitable product lines and waited for the next big wave.

4. Invest in infrastructures.​

Business is an infinite game, and you can always accumulate comparative advantages over time. If you have some extra time and are not sure of a clear goal for now, you could always invest in yourself - better mental and physical health to help you fight through battles in the future. Keep learning and knowing more to increase the probability of success. Optimize the business to work more efficiently and live longer on the market.

Finally, do not underestimate your growth. There is a deception phase even with prominent technologies like AI or 3D printing; they seem not to progress for a long time and then suddenly improve at an exponential rate.