Mahdi Khojasteh @Idhami4

PhD Research Intern @ #Apple! Focus: Personalization, preference-based learning, RLHF, LLM etc. Prefer candidates with strong ML/RL/OL theory backgrounds w/ coding skills Apply - lnkd.in/gSFDWkj4 Pls fwd in your circles! #MachineLearning #LLM #RL #neurips #ICLR #ICML

I’m hiring software engineer new grads: Pay: $180,000 - $220,000 Cohort: Summer 2025 Location: Seattle/Bay Area Tech stack: Golang, Python, Typescript -Direct referrals offered -AI/ML roles available -Newly opened headcount -Free breakfast/lunch/dinner -RTs appreciated

My group has multiple openings both for PhD and Post-doc positions to work in the area of optimization for ML, and deep learning theory. We are looking for people with a strong theoretical background (degree in math, theoretical physics or CS with strong theory emphasis).

My lab has multiple openings for those interested in doing some very cool science in beautiful Colorado! Research assistants (tech/lab manager level): cu.taleo.net/careersection/… Postdoctoral fellows: cu.taleo.net/careersection/… DM or email me if interested and 🙏 share broadly 🪃

@huggingface 🤗

@armankhon Hi Arman, is there any chance for visa sponsorship especially on ML engineer role

@veebrandt Hi, ML engineer, let's connect 😃

# on technical accessibility One interesting observation I think back to often: - when I first published the micrograd repo, it got some traction on GitHub but then somewhat stagnated and it didn't seem that people cared much. - then I made the video building it from scratch, and the repo immediately went through hockey stick growth and became a verty often cited reference for people learning backpropagation. This was interesting because the micrograd code itself didn't change at all and it was up on GitHub for many months before, stagnating. The code made sense to me (because I wrote it), it was only ~200 lines of code, it was extensively commented in the .py files and in the Readme, so I thought surely it was clear and/or self-explanatory. I was very happy with myself about how minimal the code was for explaining backprop - it strips away a ton of complexity and just gets to the very heart of an autograd engine on one page of code. But others didn't seem to think so, so I just kind of brushed it off and moved on. Except it turned out that what stood in its way was "just" a matter of accessibility. When I made the video that built it and walked through it, it suddenly almost 100X'd the overall interest and engagement with that exact same piece of code. Not only from beginners in the field who needed the full intro and explanation, but even from more technical/expert friends, who I think could have understood it if they looked at it long enough, but were deterred by a barrier to entry. I think as technical people we have a strong bias to put up code or papers or the final thing and feel like things are mostly self-explanatory. It's there, and also it's commented, there is a Readme, so all is well, and if people don't engage then it's just because the thing is not good enough. But the reality is that there is still a large barrier to engage with your thing (even for other experts who might not feel like spending time/effort!), and you might be leaving somewhere 10-100X of the potential of that exact same piece of work on the table just because you haven't made it sufficiently accessible. TLDR: Step 1 build the thing. Step 2 build the ramp. 📈 Some voice in your head will tell you that this is not necessary, but it is wrong.

@sesigl Free

📢 I am hiring a postdoc to work with me on human-AI interaction and health equity @EmoryUniversity. Exciting opportunity to work on community-centered research, and join Atlanta's vibrant health and HCI ecosystem! faculty-emory.icims.com/jobs/119949/po…

@mterstegen1 Welcome back captain 🤗

📢Looking for a Ph.D. position in Natural Language Processing/ AI?!📢 We offer a fully-funded position for three years with the possibility of choosing from a broad range of topics! 🥳 Reach out to me if you are interested or apply directly! uni-hamburg.de/stellenangebot…

Hiring Ph.D research interns at FAIR. Interested in LLM self improvement and alignment, synthetic data, efficient architecture and training algorithms? Please reach out or apply at metacareers.com/jobs/627997209…

@FCBarcelona keep Felix

Hiring Research Scientists within @GoogleDeepMind - Toronto to join our team & advance the next generation of medical AI, develop cutting-edge LLMs & Multi-modal models to tackle real-world healthcare challenges. Please submit your interest through: forms.gle/2cSbBotUwSfVfu…

📢 Seeking 🔍 students (BS, MS, Ph.D.) with NLP research experience to collaborate 🤝 on Tabular LLM research. 🚀 Have a strong academic & passion 🤩 for NLP fill out the form: forms.gle/so7m1r2AyyELm9… Know someone? Share the opportunity!🌟Questions? Reach out. Want the best!

I'm hiring visiting students/RAs for Summer 2024! ☀️ Focus: scaling unsupervised RL & understanding emergent properties. 🚀 Who: PhD, MS, rising undergrad seniors w/ prior research experience. Apply by emailing me a resume. Details below👇

I'm hiring research engineers for several alignment/technical safety teams at Anthropic!

A reason we need beneficial AGI: After five years of pain across many systems in her body (a broken foot from stepping off a curb, debilitating migraines, fatigue, joint pain and instability, etc), my wife was recently diagnosed with a genetic disorder called Hypermobile Ehlers-Danlos Syndrome (hEDS). Because the medical system is designed for individual specialties while hEDS affects every system in her body (orthopedics, cardiology, neurology, gastroenterology, dermatology, etc), we spent five years seeing more doctors and specialists than in her whole life prior. Most doctors would only focus on what was relevant to their own specialty. We were lucky that her allergist (!!) put together the pieces after observing and hearing her full set of symptoms and issues. As human medicine has progressed, it seems like we increase doctors’ depth at the expense of breadth. We need better tools to be able to deliver depth and breadth simultaneously to patients. This is one promise of AGI if built right — reliable, individualized, affordable healthcare in your pocket, like a panel of today’s top doctors across every speciality working together in concert to keep you healthy (and without you needing to fax forms between them). There’s still a long way to go on the technology and on learning how to deploy it beneficially along with appropriate professional human oversight in high-stakes areas like medicine, but the promise is getting increasingly clear. Thoughtfully approached by technology developers, healthcare providers, governments, and society, there’s hope for much better care for every member of all of our families (including our non-human furry ones).

@zlatov_cristina True if you change db with BE