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roycoding

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Teaching Claude to Teach Claude to Play Chess

jfkirk.github.io
6 points·by roycoding·पिछला वर्ष·1 comments

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roycoding
·पिछला वर्ष·discuss
FinQuery | AI Engineer | REMOTE (US) or Atlanta, GA | Full-time

I'm hiring an AI/ML engineer to join the AI/ML team at FinQuery.

Our team creates services powered by LLMs and traditional ML to enable features across our suite of products. Like many companies, we are trying to separate the real value of the latest AI techniques and tools from the hype (agents?), and unlocking use cases that actually benefit our customers. This role is a rather generalist role for someone with a strong ML background, 2+ years of work experience (post schooling), and a desire to work on both proof of concept and production systems. You would be joining our small AI/ML team as we continue to automate as much as possible and enable new product features and use cases.

FinQuery makes software to help companies comply with new lease accounting regulations, manage financial contracts, and keep track of recurring and one-off payments. We're based in Atlanta, but support fully remote employees in the US. This is an IC role.

https://finquery.com/careers/

We are also hiring for roles in DevOps, marketing, and more.
roycoding
·3 वर्ष पहले·discuss
LeaseQuery | MLOps Engineer | US remote or Atlanta, GA | Full-time

I'm hiring an experienced (senior, staff, + level) machine learning engineer to lead the tooling, infrastructure, and ops efforts for the ML team at LeaseQuery.

Our team is deploying ML-based services to power features across our suite of products. As we grow the scale and number of our ML-driven features, we are looking to build more robust tools, infrastructure, and ops processes to make our modelers more efficient and our model serving more robust. I'm hoping to find an experienced person that can help us make the best decisions, mentor other team members, and lead the design and implementation of new tools and processes as an IC.

LeaseQuery makes software to help companies comply with new lease accounting regulations, manage their SaaS spend (via our recent acquisition of Stackshine [YC W22]), and more generally handle accounting and spending related to recurring costs. We're based in Atlanta, but support fully remote employees in the US.

https://leasequery.com/careers/

We are also hiring for several roles in software, DevOps, and platform.
roycoding
·3 वर्ष पहले·discuss
LeaseQuery | MLOps Engineer | US remote or Atlanta, GA | Full-time

I'm hiring an experienced (senior, staff, + level) machine learning engineer to lead the tooling, infrastructure, and ops efforts for the ML team at LeaseQuery.

Our team is deploying ML-based services to power features across our suite of products. As we grow the scale and number of our ML-driven features, we are looking to build more robust tools, infrastructure, and ops processes to make our modelers more efficient and our model serving more robust. I'm hoping to find an experienced person that can help us make the best decisions, mentor other team members, and lead the design and implementation of new tools and processes as an IC.

LeaseQuery makes specialized accounting software to help companies handle (new) lease accounting requirements and manage their SaaS spend (via our recent acquisition of Stackshine [YC W22]). We're based in Atlanta, but support fully remote employees in the US.

Job ad: https://jobs.lever.co/leasequery/850d1e33-2945-4ab3-aaf2-625...

We are also hiring for several roles in software, DevOps, and product.
roycoding
·3 वर्ष पहले·discuss
I published a book last year with a similar goal: Zefs Guide to Deep Learning

https://zefsguides.com

It's a pretty short book designed to provide a strong conceptual grounding in the most import ideas in deep learning, starting with an intro to ML. The book posted by the OP appears to be a little more oriented towards the math and people who have a strong grasp of CS theory. I am definitely going to read through it!

I was considering going even smaller and cuter with my actual print book, but ended up with a "pocket book" format that's 5.25" x 8" (13.3cm x 20.2cm) and about 160 pages total. This book seems to get the smallness part down pretty well.

I actually considered the exact same title (The Little Book of Deep Learning) when I started out!