maximz

Immunera | Principal Machine Learning Engineer | NYC area (hybrid) | Full-time | immunera.ai

Hey! I’m Maxim, cofounder/CEO of Immunera.

We're building blood tests that use gene sequencing and machine learning to help patients with autoimmune disease. Immunera spun out of years of research at Stanford, and our technology has been published in Science and covered by The New York Times.

We're hiring a Principal Machine Learning Engineer to shape the core models and infrastructure that power our blood tests. We are partnering with hospitals to generate training data from real patients for our language models and other biological sequence models.

This is a senior engineering role with significant autonomy at an early-stage, venture-backed startup. We're looking for strong experience building and operating ML systems in production (Python, PyTorch/TensorFlow, cloud platforms). Prior biology or healthcare experience is NOT required; we care much more about your ability to reason about data, models, and systems.

Full JD: https://www.immunera.ai/jobs

Contact: maxim@immunera.ai (subject: “HN ML job”)

Immunera | Principal Machine Learning Engineer | NYC area (hybrid) | Full-time | immunera.ai

Hey! I’m Maxim, cofounder/CEO of Immunera.

We're building blood tests that use gene sequencing and machine learning to help patients with autoimmune disease. Immunera spun out of years of research at Stanford, and our technology has been published in Science and covered by The New York Times.

We're hiring a Principal Machine Learning Engineer to shape the core models and infrastructure that power our blood tests. We are partnering with hospitals to generate training data from real patients for our language models and other biological sequence models.

This is a senior engineering role with significant autonomy at an early-stage, venture-backed startup. We're looking for strong experience building and operating ML systems in production (Python, PyTorch/TensorFlow, cloud platforms). Prior biology or healthcare experience is NOT required; we care much more about your ability to reason about data, models, and systems.

Full JD: https://www.immunera.ai/jobs

Contact: maxim@immunera.ai (subject: “HN ML job”)

Immunera | Principal Machine Learning Engineer | NYC area (hybrid) | Full-time | immunera.ai

Hey! I’m Maxim, cofounder/CEO of Immunera.

We're building blood tests that use gene sequencing and machine learning to help patients with autoimmune disease. Immunera spun out of years of research at Stanford, and our technology has been published in Science and covered by The New York Times.

We're hiring a Principal Machine Learning Engineer to shape the core models and infrastructure that power our blood tests. We are partnering with hospitals to generate training data from real patients for our language models and other biological sequence models.

This is a senior engineering role with significant autonomy at an early-stage, venture-backed startup. We're looking for strong experience building and operating ML systems in production (Python, PyTorch/TensorFlow, cloud platforms). Prior biology or healthcare experience is NOT required; we care much more about your ability to reason about data, models, and systems.

Full JD: https://www.immunera.ai/jobs

Contact: maxim@immunera.ai (subject: “HN ML job”)

Immunera | Principal Machine Learning Engineer | NYC area (hybrid) | Full-time | immunera.ai

Hey! I’m Maxim, cofounder/CEO of Immunera.

We're building blood tests that use gene sequencing and machine learning to help patients with autoimmune disease. Immunera spun out of years of research at Stanford, and our technology has been published in Science and covered by The New York Times.

We're hiring a Principal Machine Learning Engineer to shape the core models and infrastructure that power our blood tests. We are partnering with hospitals to generate training data from real patients for our language models and other biological sequence models.

This is a senior engineering role with significant autonomy at an early-stage, venture-backed startup. We're looking for strong experience building and operating ML systems in production (Python, PyTorch/TensorFlow, cloud platforms). Prior biology or healthcare experience is NOT required; we care much more about your ability to reason about data, models, and systems.

Full JD: https://www.immunera.ai/jobs

Contact: maxim@immunera.ai (subject: “HN ML job”)

Hi @zeagle, sorry to hijack the thread (didn’t see a way to DM you)

I'm a PhD student working on a new lupus diagnostic blood test approach [1]. Hoping to steer the project towards true clinical needs.

I'd love to ask for your feedback as a technologist + rheumatologist on a few lupus + RA diagnostic directions we're considering. Would they actually be useful in your practice?

Would you be open to a quick chat? My email is maximz@stanford.edu.

Many thanks!

[1] https://www.biorxiv.org/content/10.1101/2022.04.26.489314v5

Love this. I appreciate your building on ripgrep versus my own bulky lucene-based approach a while back (https://github.com/maximz/sift), and that you don’t require pre-indexing but build up a cache as you go.

Rent a server: do you mean dedicated hosting or something else?

I may have what you're looking for: I've been working on exactly this problem, because I grew tired of the headaches from maintaining a "snowflake server" (http://martinfowler.com/bliki/SnowflakeServer.html), which I configured once in the past and would be very difficult to reproduce.

Here's a super-simple Docker host stack I put together that you can install on a cloud machine fairly easily. It runs Docker containers, does automatic backups, and has monitoring and alerting built-in. I've run around 5 webapps, split between 10 containers, on this infrastructure for about 6 months with significantly fewer headaches. Monitoring alerts go straight into a personal Slack channel.

The implementation is deliberately low-level -- you'll be running bare docker commands -- because the goal is to have a trivial infrastructure and to learn Docker in the process. There's no reliance on the "magic" in big wrappers like Kubernetes. Because there's nothing fancy in here, it's simple to understand and use.

My write-up is rough around the edges right now, but eventually this will turn into a simple blog post with an Ansible playbook attached. Here's a rough draft of how to set it up manually: https://github.com/maximz/docker-host/blob/master/public_doc...

There's an nginx reverse proxy fronting the Docker containers. You can do a zero-downtime deploy pretty easily. Happy to answer any questions if they arise.

I saw David Donoho give this talk live in September at Princeton's Tukey Centennial conference -- fantastic, and well worth a read. IIRC, gives a good history of data analysis, how to think about the different definitions of and roles for data science, and an introduction to Tukey's work.

For more on the history of data science, here are references from a similar talk by Chris Wiggins: http://bitly.com/icerm

The Roborumble competition for Robocode is similar and still active: http://robowiki.net/wiki/RoboRumble

I think this is because of different risk preferences, which make the conclusion obvious.

These teens have higher risk preferences. Entrepreneurship is a high-risk, high-reward endeavour.

Since we count only the successful ones, those who take higher risks and are successful naturally are more successful than those who take smaller risks.

Here are the most recent FAA Advisories: http://www.fly.faa.gov/adv/adv_list.jsp?WhichAdvisories=ATCS...

And here are three relevant ones already published:

* http://www.fly.faa.gov/adv/adv_otherdis.jsp?advn=26&adv_date...

* http://www.fly.faa.gov/adv/adv_otherdis.jsp?advn=27&adv_date...

* http://www.fly.faa.gov/adv/adv_otherdis.jsp?advn=29&adv_date...

My major gripe is that some formatting is completely hidden, making it impossible to deal with.

I've often tried to copy-paste from an email thread to a new compose window, which looks like it works. But then the resulting email has a line break after each word!

The clear-formatting button doesn't reveal this, nor does plain-text mode - this has led to a number of embarrassing emails.

I'm currently trying to choose between Berkeley (Electrical Engineering Computer Science major) and Princeton for undergrad.

Has anyone here gone through the Princeton CS program?

Completely agree. Do you happen to have a link to pg's essay about changes from s12 to w13? Thanks.

This isn't surprising. Let's look at how many people applied and how many acceptances were issued over the last few years:

* 2012-2013: 38,828 applied, 2210 accepted

* 2011-2012: 36,631 applied, 2427 accepted

* 2010-2011: 34,348 applied, 2427 accepted

* 2009-2010: 32,000 applied, 2300 accepted

There's a constant amount of spots and they take the same amount of people.

The reason they are now more selective (percentage-wise) is simply that more people applied.

I remember there were some organizational issues - is that what you mean by "the disaster that was S12"?

Great site! Out of curiosity, are you planning to eventually open-source this, by the way? Thanks.

Cool app! Besides crossfilter, is the source available somewhere? Thanks.

There aren't many startups down here, but there are definitely wireless health and engineering firms.

It'd also be nice to have the option to create "elections" that use STV/instant runoff: http://en.wikipedia.org/wiki/Single_transferable_vote

Absolutely. It's impossible to gain traction unless you simply your product and start with one feature. The problem that then arises is how to choose the "killer app" out of your entire model, something that is groundbreaking, evaluates the market opportunity, and leaves you with a cliffhanger to prepare for the next step in increasing complexity.

what was it?

Is your accelerometer-based code, or something similar, available somewhere? I'm curious as to how that works.

Could you please elaborate on how this works or point to some resources on this application of the algorithm (if you know of any)?

Here's their supplemental video: http://www.youtube.com/watch?v=3s6Yv9pVXZc

Fool me once, shame on you; fool me twice, shame on me. Facebook does this routinely, and yet people go back.

Would you be surprised if news broke that Facebook has been selling your private messages to third parties? Not really. What if Google did that? That would cause uproar, so G+ is competitive in terms of user control.

Is there a tradeoff between user control and social connections that users face when using Facebook? Were this the case, G+ would have much higher market share and users would have already started flooding away, with the network effect in play.

But that's not the case. Control is relatively negligible to the majority of Facebook users. Competitors won't succeed until values change.

Personally, I value control. I'd be done with Facebook were it not for its White Pages functionality - with a name, you can find someone and contact them. That's not the case for the majority though, so having other competitive systems won't do much good.

And a somewhat relevant Code Golf: http://stackoverflow.com/questions/2568090/code-golf-word-se...

voila: http://hertzen.com/experiments/jsfeedback/