Datakami | ML Engineer | Netherlands (within 2hrs of Nijmegen) | Remote (NL, DE, BE) | No visa sponsorship | https://datakami.com
We're a software engineering consultancy specialized in generative AI. Small team of senior ML engineers offering remote expertise to startups across US/Europe, solving unique challenges of running generative AI in production.
Looking for ML engineers who are a mix of ML expert, software engineer, researcher, and hacker. You'll work embedded in client projects on:
- Converting bleeding-edge open source AI models to production
- Building production LLM pipelines from scratch
- Improving models on speed, robustness, and performance
- Designing custom LLM benchmarks and evaluation
- Building and scaling ML infrastructure
- Setting up monitoring, tracing, and prompt management
We value: self-starters, quick learners, strong communication skills, software quality, open source. Role involves engineering, talking to clients and outreach.
Requirements: Strong Python, experience with production LLM systems, cloud platforms, MLOps. Must be EU work eligible and live within 2 hours of Nijmegen, Netherlands. Remote work with occasional meetups.
Benefits: 25 days PTO, home office budget, professional development budget
It looks like you can use the gemma tokenizer to count tokens up to at least the 1.5 models. The docs claim that there's a local compute_tokens function in google-genai, but it looks like it just does an API call.
To provide some counterweight to all the overwhelmingly positive reviews:
I've used kagi for 6 months and have over 7500 searches with them. It mostly works, but there are a few downsides compared to Google:
- The latency is a lot higher than google, taking over a second to display any results.
- The results are often not as relevant, I have to frequently retry my search in Google.
- The results for anything local (I'm not in the US) are abysmal. Searching for anything in my city instead only gives me results for the city's history.
Still, I persist in using Kagi, mainly because it's not Google and I want them to succeed. The results are frequently good enough for me to stay with them.
Adding `busybox` and `bashInteractive` to the container contents gives you enough of a comfortable environment to work in without losing too much space.
I'm not sure that the answers that the model provides have anything to do with what it's actually doing.
The way they seem to be prompting it also exhibits this issue, where they first have it arrive at a conclusion and then come up with an explanation for this conclusion. LLMs do not have an inner voice to reason with, and tokens generated later do not influence earlier tokens (unless you're doing beam search, but you mostly aren't). It would be much improved if asked to do reasoning first and then arrive at a conclusion.
- input emulation, doable via uinput but not great
- standard way to query the list of windows and active focus
- for dwell-click support, you need to be able to know if the user is moving their mouse or clicking so you can cancel your autoclick
Sourcegraph seems to have collected a bunch of these (now leaked) email addresses from signups on self-hosted instances.
I remember being very surprised when I was signed up to their mailing list after I made an account on my self-hosted instance, and I'm not sure about the ethics (and legality) of collecting these in the first place.
We're a software engineering consultancy specialized in generative AI. Small team of senior ML engineers offering remote expertise to startups across US/Europe, solving unique challenges of running generative AI in production.
Looking for ML engineers who are a mix of ML expert, software engineer, researcher, and hacker. You'll work embedded in client projects on: - Converting bleeding-edge open source AI models to production - Building production LLM pipelines from scratch - Improving models on speed, robustness, and performance - Designing custom LLM benchmarks and evaluation - Building and scaling ML infrastructure - Setting up monitoring, tracing, and prompt management
Tech stack: Python, LLMs, AWS/GCP, MLOps tools, Docker, Git, Nix
We value: self-starters, quick learners, strong communication skills, software quality, open source. Role involves engineering, talking to clients and outreach.
Requirements: Strong Python, experience with production LLM systems, cloud platforms, MLOps. Must be EU work eligible and live within 2 hours of Nijmegen, Netherlands. Remote work with occasional meetups.
Benefits: 25 days PTO, home office budget, professional development budget
Apply: https://datakami.com/careers
Recruiters/freelancers/agencies: we're not working with recruiters or considering freelancers or agencies at this time.