I would add that in terms of networking events, it's not just events that directly help you get a job that's useful, but even showing up to clubs/fairs is networking. I'm sure there are tons of guest speakers coming to GTech fairs and clubs. Even after you graduate if you know a ton of people then when you need a referral you'll be able to reach out to people you know from clubs

My biggest college regret is trying too hard to get good grades instead of job searching and networking

Sometimes showing up is all you need to do. I know people who got internships because they went to a club and some person who's working in an early stage startup was giving a talk. You never know who can help you get a job. Good luck!

Founding ML Engineer @ Lancey (YC S22)

Sai Shreyas Bhavanasi

For CS/SWE internships, getting really good at Online Assessments (OA) is key. From the looks of it, you already have a strong profile. Since you already have enough projects/experience getting good at leetcode is a good next step.

Most internships send out an OA, and if you do well there's a good chance you'll get an interview. I would focus on grinding leetcode and cracking those OAs

I would also focus on networking. Attend career fairs, clubs, coffee-chats, any and every networking event you can find. You can try cold DMing alumni and getting coffee chats and then asking for a referral

I havent used Spark, but I used Dask which is a python first version of PySpark that also does distributed data processing. Below are my insights for debugging in dask but similar stuff should apply to spark

### Finding Bottlenecks

I found using the dask [dashboard ](https://docs.dask.org/en/latest/dashboard.html)really important (see [spark dashboard](https://docs.databricks.com/en/optimizations/spark-ui-guide/index.html))

- I look for memory issues - is there large spills? this is bad
- Are there too many tiny processes? this means that the spark is being really inefficient with too many small jobs - try increasing the partition size
- Is there too much data transfer? shuffles and joins are terrible so I avoid it as much as possible
- How well utilized are all the nodes? If a lot of nodes are sitting idle then that is bad
- Is the scheduler overwhelmed? this is bad

### Improving Performance

To improve performance without running large jobs I try to benchmark performance on small(ish) datasets and see how each change affects performance. Most spark/dask jobs are groupby + apply.

Spark basically parallelizes multiple tiny jobs. So if you can isolate and benchmark the tiny job to be as efficient as possible then this will carry over to the large jobs as well.

So I test the apply code on a small dataset and test how different changes to my apply function affects performance.

**In my experience 95% of the issues are from partition sizes**. If you're struggling with performance changing partition size is best first approach. Ideally start with 100MB partitions. Ensure that the partitions are not getting bloated throughout your code if you're adding/removing columns. If an individual partition/apply function takes a while, then reduce partition sizes.

Honestly the biggest performance gains came from reframing the problem/rethinking the approach to the actual problem you are solving. Talking with your coworkers and getting outside perspective helps a lot

Sometimes I get too stuck trying to optimize code and looking from an outside perspective you might realize you can trade off a bit of accuracy of the features for a huge performance gain.

For e.g. i remember this story of how instead of trying to make elevators faster they just put mirrors and people were busy looking at the mirrors so they felt the elevators are faster

Jonathan's answer is fantastic. This video was super helpful for me when I onboarded during internships: <https://www.jointaro.com/lesson/ukpEyEsSONZA0n6NFdAP/ask-questions-well/>

Mastering this skill of asking high quality questions will take you far

Hey OP, are you able to share more on how the ML is being applied? In most cases you don't need to spend too much time understanding the low level math for ML unless you're dealing with customizing models.

I would focus on understanding

1. How does this model work -- the intuition behind it
2. What are the assumptions this model makes for e.g. some models assume the data is normally distributed
3. What are the pros of using this model
4. What are the cons of using this model

If you understand this you should be good for most cases.

And if you do need to actually understand how the model works in depth I suggest only learning the parts you need to as you study

E.g. you are learning about Convolutional Neural Networks - CNNs

- Understand how CNNs work intuitively
- Then you want to know what a convolution is so you dive deep into the math/code
- Then you realize you need to understand what a dot product is, so then look at that
- Keep repeating till you understand everything, almost like a DFS tree search

Other things that help:

- Look at minimal code replicas of models. Every model you want will have some version of minimal version of it ([even LLMs](https://www.youtube.com/watch?v=kCc8FmEb1nY))
- Grab the code and walk through what's happening. For CNN you might look at how the image is before and after a convolution is applied. Step through the code and understand what is happening.
- Start small, work with really small data that you can reproduce by hand. For CNNs this might be an "image" that's 5x5 0/1 and you can do the math on paper on what each step should look like

Feel free to DM me on Slack (@Sai B) if you want to chat more

Agree with others here. Think of ChatGPT as a glorified stackoverflow (it's basically a compressed internet). It's important to understand why you're doing the things you're doing and you'll be fine

Ooo that's awsome. I've been sleeping on it. Thanks so much for sharing!!!

Would you be open to sharing how many callbacks you got from it? I've never heard back from easy apply

Honestly this is super relatable. As Rahul said, the rejection rate in recruiting screening is super high. My guess is that it happens bc the HM is looking for a very specific candidate with experience in X,Y,Z tech and maybe you just dont have it. There's thousands of candidates and the HM has no incentive to pick a candidate who isnt already using their tech stack. I am guessing the recruiter probably doesnt have a deep understanding of what specific skills/tech stack the HM wants and how transferrable some of the skills are

Hi OP, when you say dig deeper into ML, what exactly do you mean? are you looking for more R&D role? It seems like you're currently working on platform.

I'm sure at Meta the core ML is reserved for PhDs. But I've heard of MLEs who do more engineering related work of ML can be done with SWE experience. Open AI themselves said that they value SWE expertise more than ML expertise because its easier to teach someone to do ML than write good code.

Generally I think it's better to try and transition and learn on the fly rather than get another degree especially for ML. ML roles are either core ML which at big tech you'll need PhDs because they're specialized roles or they are supporting ML which is basically a different flavor of SWE.

The best option imo is to first try to break into ML by applying or trying an internal transition. Soon you'll realize what you're missing for your career transition. Best case scenario you dont need a masters for what you want.

I'm a bit confused. You graduate this summer and are also interning this summer?

Not mentoring, but I hold regular coffee-chats where you can meet other engineers. Taro engineers are amazing and in the past sessions, people have gotten referrals, career advice and more. I suggest joining! \
The next one is tomorrow (Apr 23) at 5PT: <https://www.jointaro.com/event/taro-virtual-coffee-chat/>

Are you currently interning at the company you want to return full time to? I suggest discussing with your manager about a return offer

The easiest thing you can do is not update your LinkedIn till you get a confirmation. Just say whatever you need to on your resume

Hey OP, here is a blueprint for a project.

It's great you've listed some interests of yours. I suggest going to Kaggle and finding a dataset you're interested in. The bigger the better (think 50/100+ GB) and if it updates everyday even better.

1. run some prefect/airflow or orchestration job to fetch data from some source (reddit/twitter/kaggle...) put it in s3
2. read from s3 and write a [sagemaker processing job](https://docs.aws.amazon.com/sagemaker/latest/dg/build-and-manage-steps.html#build-and-manage-steps-types) to train a model
3. deploy on sagemaker inference endpoint
4. wrap with a flask app
5. Run continuous training and monitoring. As new data comes in how does your model performance change? Are you able to easily retrain a model after time passes?

Always happy to chat over Slack DMs (@Sai B) or LinkedIn if you have more questions

Hi OP, can you share a bit about your background in database and what exactly you're looking to learn? Are you referring to learning more about querying data from DBs aka being a SQL expert, or designing/architecting DB schemas for your team? or maybe connecting/creating data pipelines from the DB to your software?

In my opinion, I suggest to ramp up how many applications you send. From what I've seen you should have a different resume for each genre of job so one or product focused, one for infra focused, or backend/frontend, and so on depending on your skillset. Once you have this when you apply just send in the resume tailored to the job app

I would target minimum 100 applications a week. I use [simplify.jobs](http://simplify.jobs) and I can do 100 in about 2-3 hours. Dont bother sending in cover letters. Getting interviews is a numbers game so mass applying is the way to go. 

Apply to any job you meet 50% of the requirements of. Good luck!

Definitely 50 is too much. Also find out if youre going to need to slot out time for a Q and A as well. I benchmark 1 minute per slide usually. 

For the presentation

- **Remember to talk about the problem you are solving**. The audience (assuming its seniors/managers/tech leads) wont know your problem so make sure to be specific about the problem you're solving
- **Talk about solution in a high level**. These folks likely see a bunch of these and they get fatigued through it. No one can really follow your actual solution, so make sure to keep it high level
  - The level of technical depth should be as if you're presenting to a recruiter
- **Thank the people properly** who helped you out in your internship
- **Emphasize growth and what you learned**. Interns are almost always a net negative for companies. They also know you've likely never had a real SWE job before so growth and learning speed is very important. Make sure to highlight soft/hard skills you learned during your internship
- Even though it seems like your internship was less than ideal, please try to not project it or talk negatively during this time. Try to keep things positive
- When you present try to stay in a positive mood and be a bit energetic. It realy reflects positively

I think generally most companies are flexible with start dates. At my internship, the company was willing to move dates to accommodate my college's international student CPT requirements. 

I would reach out to the recruiter/HR and just ask if you can move the start date by two weeks so that you can finish your conference and start the internship.

May 1st is also really unusual start date for an internship so its not going to be out of the norm to ask to move the dates. Normally its may 2nd week if its 12 week or june first week if its 10 week internship 

AWS is very good at refunding/refuting bills if you accidentally rack it up. I was working on a personal project and somehow launched some ML canvas that runs and charges until you shut it down. I swore I closed it but I kept seeing my bill increase daily. In the end it was $300.

I reached out to AWS support and turns out it was open in a different region. I spent 2 hours working with support. You have to manually toggle to that region and only then it shows up . I was like how is it charging me I literally dont see it running. Luckily AWS helped me through it and refunded everything. Normally for small charges as long as you act promptly they will refund it.

This is mainly for services left running though. I'm not sure how AWS works if you rack up a large bill because of an attack.

It's quite important to get familiar with cloud so my personal suggestion is to go through the flow and get everything running for the learning experience and shut it down. Then deploy it elsewhere like [railway.app](http://railway.app) or heroku. I like railway a lot

As Daniel said, you can also set billing alerts and also alerts for unusual activity.

To be honest what I've learned is that there's so so so many tools and technologies and every company does things a different way -- so it's just best to learn as you need to. I'm not sure how much background you already have in ML. If you've already done some projects then I would not worry too much about keeping skills sharp

### Project Framework

If you havent had any project experience, I would do an end to end AWS project

Pick a dataset of your choice (ideally that's big data - over 10-20ish GB, something that wont fit in RAM) 

Put into S3

Write some ETL scripts to create features

Train a model on it like XGBoost and use some logging tool like wandb

Deploy it using sagemaker endpoint and docker and create a simple flask API/website

setup some continuous testing/monitoring to monitor the quality of predictions in the cloud

Bonus points:

- If someone uses your model and you have users
- You are able to support retraining

I would avoid deep learning and stick to tabular data. Most companies apply ML to tabular data

### Resources

[Summary of Designing Machine Learning Systems](https://github.com/serodriguez68/designing-ml-systems-summary)

A fantastic summary of Chip Huyen's book - Designing ML systems -- The gold standard of MLE

<https://www.ml.school/>

An ML course that focuses on the engineering side of ML (deploying ML, feature engineering, how to tackle ML problems). It's a fantastic course that is super industry oriented and teaches how to use sagemaker and other fundamental ML concepts. I've taken it and it's brilliant and is the best i've seen. Though if you've gotten a master's focusing on MLE it might not be super useful. Check the syllabus and see if it applies for you. If you havent done any cloud work with sagemaker this might be helpful

Congrats on getting your masters! From my understanding, ML infra is generally a SWE for the backend SWE that happens to be for ML but the work is basically SWE.

In terms of skills for MLE, the core things that matter and keep you relevant are

- Are you able to build data pipelines to feed the model the data
- Are you able to train a model that works well
- Are you able to take a model and deploy to production
- Are you able to evaluate, benchmark, and run experiments with the model
- Are you able to monitor the model when it is in production?

You can be an MLE without ever training a model. In my opinion MLE is basically a full stack data scientist

Based on what you said, it seems like you're basically doing SWE work. I would look into transitioning into a different team that does one of what I mentioned above. Is that something possible for you?

My guess is that if your company is building infra, then they should also have a team responsible for training/testing/deploying (unless the company is a pure infra team)

ML Engineer

[simplify.jobs](http://simplify.jobs) was what I used to use all the time looking for new grad roles

- When you hit apply it one click auto completes using the chrome extension allowing me to get through 100 apps in 2 hours
- You can apply their filters to filter roles based on industry and also their level filter is by far the best I've used

On LinkedIn you need to use filters for best results. Here's mine:

```
("machine learning" OR "ML" )(NOT "senior" NOT "sr" NOT "manager" NOT "lead" NOT "principal")
```

I enter a long boolean search directly on google search. I also set a time filter on google search on the top to filter down to posted in last 24 hours

```
site:lever.co ("US" OR "United States" OR "USA") ("ML Engineer" OR "Machine Learning Engineer") -"Staff" -"intern" -"internship" -"Senior" -"Principal" -"Sr"
```

```
site:boards.greenhouse.io ("US" OR "United States" OR "USA") ("ML Engineer" OR "Machine Learning Engineer") -"Staff" -"intern" -"internship" -"Senior" -"Principal" -"Sr"
```

Have to mention [Microservices video](https://www.youtube.com/watch?v=y8OnoxKotPQ)\
This is also what I hear from Taro team when I say "why cant we just filter on all users" 😂

I think having some mental separation between work and not work can help. So this might mean going for a workout/walk or a commute and an unwind routine

My main issue has been having the energy. Work is so draining so I find that working out frequently just generally increased my overall energy levels

I would treat this like any other ML project. I would build an end to end app where you

1. run some prefect or orchestration job to fetch data from some source (reddit/twitter/...)

2. put it in s3

3. read from s3 and write a processing job to fine tune an LLM in sagemaker

4. deploy on sagemaker inference endpoint

5. wrap with a flask app

In my opinion from an ML/Data Science perspective an LLM is just like any other model. It's similar to how you would use BERT for something except that its like BERT on steroids.

I would look into using an API for open source models.

Here are some open source LLM vendors:

[perplexity.ai](http://perplexity.ai)

mistral

[abacus.ai](http://abacus.ai)

[replicate.com](http://replicate.com)

There's a ton more

If you need chatGPT3.5 level stuff it is effectively free in this day and age. GPT4 level stuff is where it gets pricey and mistral medium seems to be the best value LLM rn

If you need to fine tune I've seen <https://monsterapi.ai/> Never used it but might be worth looking into

- Ask thoughtful questions -- things that cant be googled. I like to ask about current challenges the team is facing
- When the HM talks about their job and duties, pay careful attention to this. Use this to ask thoughtful questions
- In my opinion, for internships, your soft skills are way more important bc the expectations for interns in terms of providing business value are much lower than regular engineers. Tho this varies widely between companies. cant speak for FAANG and big tech but I expect they have a higher level of hard skills, but in general your ability to learn and be mentored is whats key for internship interviews.

To wrap it up, being thoughtful, impressionable, and likeable goes a long way and it's important to focus on that. Send a small thank you note after the interview thanking them for their time and a small blurb on what you enjoyed learning about

I find simplify (<https://simplify.jobs/jobs?experience=Entry%20Level%2FNew%20Grad%3BJunior>) really great. Using the extension i can easily apply to 100 jobs in 2 hours

I've found LinkedIn not that great for looking for roles. It has a ton of ads, most of them are irrelevant or a bit shady contracting postings. It also doesnt recommend roles of the right level. I use boolean search to filter out senior roles to make it better. Here is the command I used when looking for ML roles

```
("machine learning" OR "ML" )(NOT "senior" NOT "sr" NOT "manager" NOT "lead" NOT "principal")
```

You can also do a google search with boolean search to search for greenhouse and [lever.co](http://lever.co)

I use something like this:

```
site:lever.co ("US" OR "United States" OR "USA") ("ML Engineer" OR "Machine Learning Engineer") -"Staff" -"intern" -"internship" -"Senior" -"Principal" -"Sr"
```

ML Engineer Intern

From my experience, there's 2 avenues for SWE's to explore ML

1. **ML Platform:** Big tech companies like Uber have custom infra for training/testing/deploying ML models and need software engineers to build this. This would be a great segue point to shadow and work with ML teams

2. **ML Engineering/Data Infra:** ML especially in big tech companies have ML flows that are super segregated. This means that the folks who do feature engineering, and the folks who do training, and the folks responsible for deploying and monitoring are all distinct teams (unlike smaller companies where fewer people are responsible for the end-to-end pipeline).

   So, ML Engineering in big tech tends to refer to the more deploying and integrating ML models side. This is like 80% SWE and 20% ML. and the ML is generally domain understanding and it would make sense to try and transition to those teams

**The core idea is to get a SWE role in an ML team.** The core ML part (modeling) is only 10% of what ML teams do and generally in big tech these roles are handled by data scientists generally with advanced degrees. But since you're interested in the Engineering part it would make sense to look for SWE roles in the ML team. This transition should be similar to any other transition in SWE. Once you're there then I would look into upskilling. You might find that you dont need to be an ML wizard with expertise in PyTorch to be an MLE

Cant attach pics here but check out this <https://youtu.be/Z3-HddkYgyI?si=WX7BKREIe2zzoOlU&t=103> to get an idea of what is the breakdown of the roles. MLE seems to be basically "full stack" ML. But notice how the deploy and predict is handled by SWEs. So my suggestion is to look into roles at Uber for the deploy/predict part of the ML workflow

For more context on the ML at Uber, check out this [post ](https://www.linkedin.com/posts/b-sai_machinelearning-ml-ai-activity-7142957455877038080-ELuD?utm_source=share&utm_medium=member_desktop)I made on Linkedin breaking down Uber's ML platform.

I highly recommend this course: <https://www.ml.school/home>\
\
This course teaches you everything you need to know about ML that is not about training ML models, but rather ML in production. I am currently working through that course and it's awesome\
\
Feel free to DM on Slack (@Sai B) if you want to chat more about ML

Coming from someone who would like a mentor, I agree with the responses here, an important thing in my opinion is to find someone with a similar background/experience/shared struggles to yours

At the end of the day, we are all a product of our own experiences/struggles. Trying to mentor someone with a really different background/struggles can be challenging

Often when mentors are able to relate to what I'm struggling with, I notice that they work with me the best

In my experience, Leetcode style questions are not common for Data Science interviews. However, for MLE/Applied Scientist/ML Scientist they can occur sometimes.

For me personally, I am working more on the Engineering side, so I am trying to master a baseline level Leetcode competence (not trying to grind leetcode but atleast familiar enough so that if I have an interview with LC, I can spend 1-2 weeks brushing up what I need) by spending 3-5 hours a week on LeetCode\
\
Regarding data science interviews, I recommend this book: [**Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street**](https://a.co/d/8xzuY0f)

In my opinion, AI is great for getting 60% of the understanding in half the time. It is not a magic bullet that can answer all questions, but it definitively saves me time to give me a high level understanding of code, especially if the codebase is brand new

I get the best results when using GPT4, adding to the prompt things like "what is the purpose" or "please provide a high level takeaway"

Another note is there is a really really huge difference between gpt4 and gpt3.5 in my opinion

I view AI as a tool to accelerate your progress rather than a substitute for your work.

Using tools like github copilot and [cursor.sh](http://cursor.sh), it's worked really great for me when I need to jump into a code base I've never seen before and need to figure out where a certain logic is. These tools use something called semantic search which scans your entire codebase for the most relevant parts. I've used it before when I had an ML codebase with 20 ish files for a GAN model (one that I've never trained before) and had to figure out how to update a certain parameter, what a certain param does. Or to see what transforms are being applied to the images in the training pipeline

I will say though most AI coding tools only work well for python and in smaller projects

I can understand your experience. I feel like sometimes when you try to do something of everything you can get in decision paralysis.

At the very least you want to have a general foundation in the field you're targeting, (ML/SWE). Once you're there my suggestion is to make a list of priorities of your goals (I presume getting internship/new grad roles) and focus your attention on that and ignore the noise.

Since you already have a foundation, should you need to learn whatever you're feeling FOMO about you'll be able to learn fairly quickly.

This video by Steve Hyunh does an excellent job going in depth on this topic: <https://youtu.be/UAzQZdttdsA?si=m8GOkW82tL58VNQ5>

To summarize the video in a sentence: The goal should be a clear mind to think creatively and make good decisions, not perfect retention of facts

ML Engineer Intern @ Intradiem

Your post resonates with my experience. I'm also graduating this December as an international student with a Master's looking for MLE roles and I'm also finding it challenging to land interviews. Here's how I am approaching job searching:

**Create content on LinkedIn:** Create posts to distill ideas/lessons learned from projects you've worked on. I try to publish content that would have helped me if I had learned of this before starting the project. By doing so, you'll increase the visibility of your projects and can get more inbound DMs from recruiters. 

**Reach out to Hiring Managers/CEOs of startups:** I try to look for people/companies that are hiring on LinkedIn and reach out to get in touch and learn more about the company. The more relevance you have to the company the better it is. Relevance can be in terms of -- same city, same college, or is a similar field to some of your prior work

Software Engineering Intern at Series C Startup

A virtual 1:1 to meet other Tarodactyls! This is a great opportunity to meet other engineers and build deep relationships in tech

**What's in store?**

Join the zoom meeting and then I create breakout rooms and pair everyone with another random person

Feel free to exchange Slack/LinkedIn to keep the conversation going!

An awesome thread to level up your 1:1s: [How to have more impactful/deeper Taro 1:1 member match convos?](https://www.jointaro.com/question/NUW8bsmcHjm8RXCQLv9k/how-to-have-more-impactfuldeeper-taro-11-member-match-convos/)

Taro Virtual 1:1 Matching

A virtual meetup for Taro members. If you can't make it to an in-person meetup, then this event is for you! This is a great opportunity to meet other Tarodactyls and build deeper relationships in tech

**What's in store?**

- We break out into groups of 3-4
- For the next 30-40 minutes we chat about anything e.g.
  - Learning about other's career paths
  - Helping someone make an important career move
- Feel free to connect with others on LinkedIn and make a group chat on Slack to keep in touch

*Once you join the event please look for the breakout rooms!*

Thanks to anyone joining this event and taking the time to chat :)

Taro Virtual Coffee Chat

sai's playlist

Software engineers can have impact in many ways outside of coding, but make no mistake: Writing tons of very high-quality code quickly will always be useful. Follow the resources here to learn how to become a "code machine".

[Taro Top 10] Becoming A Better Coder

Starting at a new team or company? Or maybe you're just picking up a new tech stack? Go through these resources to learn how to ramp up fast.