Comment by ikjasdlk2234
21 days ago
My path went from engineering-aligned (math) to engineering management back to engineering to product to program management to solutions engineering to account executive.
Honestly I had a negative connotation about sales for most of my career, but turns out I really love it. The exposure to different problems every day is awesome and more like a puzzle than work to me. I feel a bit of reverse imposter syndrome though, like I should feel bad that I didn't "make it" as a real engineer. So that's a weird feeling.
One thing I try to do in my company is pull engineers into sales calls and proofs-of-concepts if I can. I think that exposure to both real users and unique environments is important for their growth and novelty in the job.
Sales is amazing but if your companies sales people require engineering to build POCs a lot of the times or always have to sell some custom solutions, then it wastes a lot of resources and it usually indicates the company is losing product market fit.
That is true. My current work is in bespoke environments with mainly non-technical buyers who have been burned in the past. Our POCs are pretty minor lifts to build credibility and have worked extremely well.
If you're working in SaaS or commodity products and have to run POCs a lot, you're totally correct.
Your company is selling the 'valet service' more than the products.
There are people willing to pay for convenience AND security at the same time (hire someone else to manage the problem, and they're the 'key').
I'm wondering about that. I think with the advent of AI, we might see a new kind of successful software company - one that doesn't sell a single solution to many customers, but instead has the building blocks, prompts (agents skills etc) & processes to quickly build very custom solutions for each customer - using a new blend of engineers that are not exactly "customer support" nor traditional "sw eng", but more around the emerging "forward-deployed engineer" role.
Sometimes, but if your product is a platform (or anything beyond a niche solution in a broader problem domain) then you're going to spend some time showing people how to make it work best for them.
I love hearing this.
My story: mostly business analytics (2005-2022), sales engineering, sales (both at same tech start up), and now running a solo consulting business.
I also really liked sales. Updating a CRM, not so much. But sales allowed me to spend my day talking with people about problems. No day the same, and lots of focus on finding different/better ways to communicate.
In what industries did these roles happen? Same industry/domain or have you changed that as well?
Domain is all government, but the tech is different across each of them.
I love talking too, part of why I think pre-sales is a lot of fun. And I actually love my CRM work from a data perspective, but my background is in synthesizing data and optimization. Once I turned my sales process into a network optimizing problem, it became extremely interesting to me and imperative to keep the data current.
Fair point. I too like the data side of CRM. So many interesting possibilities.
Did you always enjoy talking or did you discover it at some point prior to your current sales role?
Would you be willing to tell me more about your path to account executive and sales? I am considering such a path myself and it would be wonderful to talk with someone whose done this.
I hope you like being on call and having to tell management why you're not hitting your number and "forecasting" your way to close the gap.
I also hope you like golf! And drinking!
There can be UNREAL amounts of drinking as an AE.
You'll also be on a plane A LOT. The folks in suits and sneakers waiting first in line to board with their roller bags? Almost all of them are AEs.
Most of your customers will see you as a walking credit card.
You can also make 6+-figure commission checks and deep friendships if the deals are right, so there's that.
How do you get to sales from engineering? Must it take place laterally in the same company, or did you sell yourself differently to recruiters, or did you know someone?
A fairly easy role to help bridge that gap is to work in a consulting company as a technical IC. The sales team is always looking for technical folk to help build more robust and achievable projects as well as provide a more accurate estimate for number of resources and amount of time the project will take. They need someone who can push back on impossible client demands and help the sales team understand what those are and why. Ideally in these scenarios a senior tech person would be helping build out the proposal and plan for a project they will actually run and execute on. This gives the client continuity throughout the process and in the course of working within the client environment you’re in a great position to spot other problem areas the client struggles with and help build proactive proposals to help the client address them.
Can you share one such puzzle?
I am a solution engineer mostly on the traditional ML side of things but have good knowledge of K8S/GKE. The most fun I had last year was helping a customer serve their models at scale. They thought it was cost prohibitive (500k inferences/second and a hard requirement of 7ms at p99) and so they were basically serving from a cache which was lossy (the combinatorial explosion of features made it so that to have full coverage you needed exabytes of ram) and was stale prone. We focused on the serving first. After their data scientists trained a New pytorch model (small one, 50k parameters more or less) we compiled to onnx (as the model is small and CPU inference is actually faster), grafted the preprocessing layers to the model so that you never leave the ONNX C++ runtime (to avoid python), and deployed it to GKE. A 8 core node using AMD genoa cpus managed to get 25k/inferences per second. After a bit of fiddling with Numa affinity, GKE DNS replication, Triton LRU caches and few other things we managed to hit 30k inferences per second. If you scale up to the traffic it would cost them few thousands per month, which is less than their original cache approach.
Now they are working on continuous learning so that they can roll out new model (it is a very adversarial line of business and the models get stale in O(hours)). For that part I only helped them design the thing, no hands on. It was a super fun engagement TBH
Are they paying you as well as your comment makes it sound? That was a ton of lingo and I'm used to lingo!
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