Espresso AI | Staff ML, Staff Infra, FDE | Brooklyn or San Francisco | Full time
We're using LLMs to build neural optimizers, neural scheduling systems, and neural workload tuners. (If you're ex-Google, you can think of it like Borg powered by LLMs.)
Today we use ML to make data warehouses and spark jobs more efficient. We're hiring staff ML engineers to train models that can understand how much compute a job needs, how it scales to larger machines, whether a machine can run more jobs, and so on; and staff infra engineers to take those models and deploy them on real-world production systems.
We're also looking for FDEs who can help us talk to users and run pilots. This is a pretty technical role (you need to be able to do data analysis and debug in prod) that's also user-facing - it should be a good fit for a former (or future) technical founder.
If this sounds cool, please email me: ben [at] espresso [dot] ai
Espresso AI | Staff ML & Staff Infra Engineers | Brooklyn or San Francisco | Full time
We're using LLMs to build neural optimizers, neural scheduling systems, and neural workload tuners. (If you're ex-Google, you can think of it like Borg powered by LLMs.)
Today we use ML to make data warehouses and spark jobs more efficient. We're hiring staff ML engineers to train models that can understand how much compute a job needs, how it scales to larger machines, whether a machine can run more jobs, and so on; and staff infra engineers to take those models and deploy them on real-world production systems.
If this sounds cool, please email me: ben [at] espresso [dot] ai
Espresso AI | Staff Engineers | NYC ONSITE | Full Tim
We use ML to make data warehouses and spark jobs more efficient. We're hiring staff ML engineers to train models that can understand how much compute a job needs, how it scales to larger machines, whether a machine can run more jobs, and so on; and staff infra engineers to take those models and deploy them on real-world production systems.
If this sounds cool, please email me an intro and a resume: ben [at] espresso [dot] ai
This is what my company does (https://espresso.ai/), I'm taking advantage of the end of year quiet time to hack on some more R&D-style projects we have.
Chiming in, I'm one of the founders of Espresso AI - we do both query optimization and warehouse optimization, both of which are hands-off. In particular we're beta-testing a fully-automated solution for query optimization (it's taken a lot of engineering!).
Based on the responses here I think we're a superset of where baselit is today, but I could be wrong.
We have better tech. For our customers, this translates directly into more savings.
We also have less setup and overhead than most of the other companies in the space. many of them come in with recommendations for system changes that you need to implement, and which they then charge you for; we take about ten minutes to set up and then generate savings automatically.
Espresso AI | https://espresso.ai/careers | Founding Engineer | NYC Onsite| Full-Time
Espresso AI is hiring founding engineers to automate performance engineering, starting with Snowflake data warehouses. Our team worked on ML and performance engineering in Google Search and Google Cloud, and we're applying our expertise to build the world's first neural optimizer.
We're well-funded with paying users, but early enough for you to have significant ownership and impact. Reach out to me directly: [email protected].
Espresso AI is hiring founding engineers to automate performance engineering, starting with Snowflake data warehouses. Our team worked on ML and performance engineering in Google Search and Google Cloud, and we're applying our expertise to build the world's first neural optimizer.
We're well-funded with paying users, but early enough for you to have significant ownership and impact. Reach out to me directly: [email protected].
How's your health? The brain fog, in particular, jumps out as something that may have other causes. In particular:
* Are you exercising?
* Sleeping well? Sleeping consistently?
* Eating well?
* Getting enough vitamins? Vitamin D is a common, easily fixed deficiency that can cause trouble concentrating; you can get your doctor to test it with a blood draw.
* Any chance you have long covid?
If you physically don't feel good on a daily basis, I would absolutely dial back your work and focus on getting in shape for, say, 2 months. 70+ hours per week clearly isn't getting you where you want, so aim for 40 and put in a hard cap at 50, and get used to the idea that some stuff won't get done. Once you're feeling better, continue keeping reasonable hours and resume studying then.
Even if everything else is fine, you might just be working too much. I think the vast majority of people would have trouble studying after a month straight of oncall and 12-hour days.
First, a reality check - it’s great that you can ship defect-free code, but that’s table stakes for a good senior engineer. You’re locally a 10x engineer because you wrote most of the code, so you’re naturally going to be a lot more effective than the other people on your team. This probably won’t translate to new projects.
If you joined a new project where someone else had written 80% of it, it would take you years to catch up to their productivity; if they were controlling and continued to write 80% of everything, it would be impossible. The next step in becoming a better engineer for this project is figuring out what you need to do differently for everyone else to be more productive; for example, if other people are pushing bugs, you need to add tests to make that impossible. If people take a long time to ramp up, you need to refactor the code so that someone doesn’t need to understand -all- of it to start contributing; you might also need onboarding docs.
The next step in becoming a better engineer generally is to switch teams and learn new skills. If you enjoy hard technical work, miss feeling challenged, and genuinely feel like you’re a much better dev than average, try switching to a hard field that’s new to you: distributed systems, ml, performance engineering, etc.
You have a heat reservoir, i.e. a well-insulated and very hot object, that stores energy as heat. If you insulate it with mirrors, that can look like bouncing photons back into the reservoir.
When you want to generate energy, you open the insulation and let heat out to hit this chip.
It’s like opening an oven door to let some hot air out.
It's a reasonable comparison, but it's probably more like being great at a sport: a professional tennis player is not going to become a professional bicyclist with a few weeks of effort.
Academics are not, and don't need to be, good software engineers, because the tools and skills one person needs to build a proof-of-concept are different from the skills a large team needs to build production code.
Functional code and immutable data are fundamental ideas for managing complexity in big systems, irrespective of language. Even modern C++ tries to be functional until it has a good reason not to be.
(I also went to MIT, and I work on low-level systems at Google.)
From the hn guidelines: "On-Topic: Anything that good hackers would find interesting. That includes more than hacking and startups. If you had to reduce it to a sentence, the answer might be: anything that gratifies one's intellectual curiosity."
The proliferation risk of nuclear fuel is overblown. Reactors don’t use weapons-grade fuel, and purifying it to weapons-grade material is harder than making the fuel to begin with; if you can take fuel and refine it into a weapon, you might as well start with unprocessed uranium.
We're using LLMs to build neural optimizers, neural scheduling systems, and neural workload tuners. (If you're ex-Google, you can think of it like Borg powered by LLMs.)
Today we use ML to make data warehouses and spark jobs more efficient. We're hiring staff ML engineers to train models that can understand how much compute a job needs, how it scales to larger machines, whether a machine can run more jobs, and so on; and staff infra engineers to take those models and deploy them on real-world production systems.
We're also looking for FDEs who can help us talk to users and run pilots. This is a pretty technical role (you need to be able to do data analysis and debug in prod) that's also user-facing - it should be a good fit for a former (or future) technical founder.
If this sounds cool, please email me: ben [at] espresso [dot] ai