Machine Learning Engineer Pathway
Launch yourself into machine learning engineer jobs at top-tier tech companies with confidence, technical skills and a strong portfolio.
About Our AI/Machine Learning Engineer Pathway
Qwasar’s AI/Machine Learning Engineer career pathway focuses heavily on algorithms and the application and improvement of different types of algorithms with large and complex datasets. The pathway is comprised of multiple courses that cover machine learning, data engineering, fundamentals in data structures and algorithms, and the basics of neural networks and deep learning. Our applied, top-level pathway is entirely project-based, meaning learners develop a strong technical portfolio as well as desired hard and soft skills.
Overall, the comprehensive courses and pathway is designed to train learners to Silicon Valley standards in machine learning with an emphasis on applied algorithms, critical thinking, and extensive preparation for technical interviews and a technical portfolio.
Find the Program Option that Works For You
Full-time and part-time and flexible pathway options available remotely.
Part-time Professional Flex
12-24 months
Monthly Sat. meetings
Study on a part-time basis with somewhat flexible options on course meetings. No project deadlines.
Start dates:
Sept 14, 2024
Nov 2, 2024
Jan 25, 2025
March 29, 2025
June 21, 2025
Intensive Bootcamps - Part Time
12-24 months
Tues/Thurs meetings
Study part-time in a highly structured intensive bootcamp. Attendance is required and projects have deadlines.
Start dates:
Sept 10, 2024
Oct 29, 2024
Jan 21, 2025
March 25, 2025
June 17, 2025
Intensive Bootcamps - Full Time
9-12 months
Mon – Fri meetings
Study in a highly structured intensive bootcamp. Attendance is required and projects have deadlines.
Start dates:
Sept 10, 2024
Oct 29, 2024
Jan 21, 2025
March 25, 2025
June 17, 2025
- Join a specific cohort
- Virtual meetings 1-3x per day
- Learn in a community
- 100% hands-on learning
- Accountability & motivation
- Stack courses back to back
- OR take breaks between courses
- Duration estimates are based on back-to-back courses
Set Yourself on a Path for Top Machine Learning Jobs
Companies are pretty clear about what they want in their candidates.
Here’s a list of companies or types or companies looking for candidates with the specific skill set and experience that aligns with this program’s curriculum:
- Financial industry – Capital One, Wells Fargo, JP Morgan Chase, regional banks, etc.
- Automotive – Tesla, Lucid Motors, Zoox, Ford, etc.
- Healthcare – Glaxo-Smith Klein, Walgreens, etc.
- Tech: Apple, Adobe, Google, Facebook, NVIDIA, PayPal, etc.
- Consulting: Deloitte, PwC, McKinsey, Accenture, etc.
Languages and Tools
Languages and tools covered throughout the AI/Machine learning engineer program:
Machine Learning Engineer Pathway Courses
Courses and programs that make up the Full Stack Pathway include:
Introductory to Intermediate C Programming
This course focuses on software engineering principles, as well as strong fundamentals in data structures and algorithms. Learners will cover fundamental computer programming concepts including arrays, strings, algorithms, pointers, hash data structures, and software architecture. This course gives you a solid foundation in computer programming fundamentals!
Intermediate Data Science
This intermediate level course is designed to equip learners with practical skills and knowledge to advance their careers in data science. It covers essential topics such as data cleaning, scraping, formatting, visualization, analysis, and SQL for database connectivity and queries. Learners will achieve intermediate competency in Python, understand frameworks and database design, and grasp basic design patterns, all while honing their debugging skills in Jupyter Notebooks.
Intermediate Machine Learning
This course focuses on implementing machine learning models with large datasets. The course touches on neural networks and implementing an AI that can solve a set of computer games. Learners must build, test, refine, and deploy multiple models for various industries and scenarios, using best practices and managing the data lifecycle from start to finish.
Advanced Machine Learning
Learners select an area of specialty for machine learning: computer vision, natural language processing, LLMs, ML for GPU programming, etc. They then specialize in this area by completing two projects, one of which is a capstone project of choice.
This is definitely not an introductory track. Minimum requirements are strictly enforced!
YOUR LEARNING JOURNEY
Learning Top Skills is Tough
Learning skills is like learning a sport, a musical instrument, or cooking: it takes time, learning by doing, trial and error, and lots of practice. NBA players don’t get good by watching lectures or videos. Skills-based learning at Qwasar is TOUGH.
DO NOT GIVE UP
The key to our pathways is to not give up. No matter how difficult the problem is in front of you, do not give up. Great engineers do not give up but instead apply structured problem solving.
EARN AN ELITE CERTIFICATE
After your hard work and upon completing the pathway, learners will earn a certificate from Qwasar Silicon Valley for Elite Machine Learning Engineering.
How Learning Works
What you will be doing throughout the program.
PROJECTS
Each season has a series of projects to complete that last 1 day to up to 3 months. These are problems and challenges to build software based on certain requirements and restrictions.
One example of a project would be to build a task-management software with tags, permissions, and a basic user interface.
EXERCISES
Each week, participants will have 1-5 coding exercises to complete. These are accessed through our software and your code is auto-graded to ensure it’s is up to speed and functioning. This is part of the learning process. We have over 800 exercises in our library with thousands of test cases!
ROLE PLAY
We use role play to develop soft skills such as job negotiations or conflict resolution. We also use role play in technical interview practice where participants will both be the interviewee and the interviewer. This dual-sided perspective is unique to our program & helps build better interviewees.
GAMIFICATION
Our system is gamified, meaning that you will earn and spend “Qpoints.” As you complete peer code reviews, you earn points and as you submit your projects for review, you will spend Qpoints.
What Sets Us Apart
Silicon Valley Standards
We train to standards set by Silicon Valley for machine learning engineers. This means the level is much higher than that of bootcamps, and higher than that of CS degrees. Your specialty is being an elite engineer at a world-renown level.
Technical Skills & Knowledge
Thanks to the depth and breadth of our program curriculum, you acquire a level of technical skills and knowledge that learners in other programs or bootcamps simply never acquire.
Strong Back-end Skills
The vast majority of bootcamps don’t cover data structures or algorithms. CS degrees don’t cover hands-on application of theory or actually developing software architecture. We cover both and your strong back-end skills and experience with databases, data structures, and algorithms will set you apart from other candidates.
Depth of Technical Portfolio
Learners develop a technical portfolio that has depth and shows the extent of their technical skills and ability to handle databases, deployments, and development. Neither bootcamps nor CS degrees offer this.
Why Qwasar
Why pay more, study less, and then spend 6-12 months trying to find a job (at a bootcamp or CS degree), when you could pay less, study more, and give yourself a much higher chance of gaining employment at (or before graduation)?
Unlike others, our learning model is backed by significant learning science and the learning experience is rooted in learning by doing and in a learning community.
Our employment rates are within 2 months of graduating, not 6 or 12, and most learners gain employment before they finish the curriculum.
We’re way cheaper than other options, and offer flexible learning opportunities.
We list some of the reasons why you should consider Qwasar:
- Train to Silicon Valley standards
- MUCH greater depth of fundamentals, data structures, algorithms
- Freedom and flexibility in learning – i.e. time to learn what you don’t understand
- Hugely diverse and welcoming learning community
- Depth and breadth of languages, technical skills, and frameworks covered
- Extensive technical interview, resume, and profile training/resources
- Industry-leading coding and learning management platform
- Access to alumni network for referrals to jobs
Real Employer Partnerships and High Employment Rates
Qwasar works directly with employers and hiring partners who recruit from our courses and pathways. Partners include LinkedIn, Capgemini, Workday, Claris, Accenture, Crowdstrike, and more.
Many employers have reached out to us to recruit directly from our programs. Qwasar is recognized in industry for its superior programs, curriculum, and graduates, and as a result, our learners often have significantly better opportunities for recruitment into jobs, internships, and apprenticeships compared to bootcamp grads.
As of June 2022, our programs have a 95% employment rate for learners who finish the program, with most learners gaining employment before they finish the program. The employment rate is calculated within 6 weeks of graduation, NOT 6-12 months after graduation.
What to Expect
At Qwasar, you are responsible for your learning, just as you would be responsible for your work in a job. Problem-based learning involves finding, trying, and building solutions. With no single source of truth and no answers provided, it’s up to you to figure out how to make your code work, when and how to ask for help, and how to successfully build software in a team.
Attend an Information Session
Join us for a session on what learning is like at Qwasar. Sign up to learn more about our program options and how each cohort works:
Join an Outstanding Learning Community!
Joining Qwasar is about joining a learning community. Learning on your own is hard, watching online videos can be boring, and sharing your learning journey (and certainly lots of jokes) with others is important.
Our platform and Discord chat, as well as our program meetings, are all about building and participating in the community. When you join our programs, you have access to:
- Group debugging sessions
- Small group sessions
- Virtual coworking rooms
- Sophisticated peer code review system
- Coding collaboration workshops and pair programming
- Our lively Discord online chat (with a tech news section and a meme section)
Transitioning Careers? Join the Club!
Qwasar is a hive of career switchers. We have people who are or were: baristas, retail associates, medical assistants, accountants, professional musicians, biomedical researchers, teachers, finance directors, IT support professionals, network engineers, mechanical engineers, college students in non-technical fields, business analysts, writers, translators, and more.
We also have career returners and veterans in the community.
Chances are high that you’ve got transferable skills. Here’s an example we put together for people transitioning from accounting:
A 6-Step Guide on How to Transition From Accounting to Tech
Check Job Descriptions: Our Curriculum Aligns to Employer Demands
Our curriculum is based on what employers are looking for in machine learning engineers.
Other programs don’t cover the minimal level required by Tier 1 tech companies.
Here’s how we stack up:
Qwasar | Bootcamps | CS Degree | |
Hands-on experience designing and implementing data architecture, able to contribute to architecture discussions | ✓ | X | Limited |
Proven experience with programming languages/tools and working with large and complex data sets (Python or C++, PyTorch, Jupyter, Tensorflow, etc.) | ✓ | X | Depends by student |
Proven experience with deep learning, algorithms and optimization | ✓ | X | X |
Experience with the data management lifecycle, i.e. collection, cleaning, storage, analysis, etc. | ✓ | Limited | X |
Proven using data to solve business challenges (choosing data, training models, analyzing algorithm options, proposing solutions) | ✓ | X | Limited |
Good at structured problem solving to efficiently debug code and to anticipate potential problems | ✓ | X (debugging not required) | X (debugging not required) |
Habituated to collaborating to solve problems, pair programming, and communicating in-person and remotely | ✓ | X | X |
Information Sessions
Join us for a session on what learning is like at Qwasar. Sign up to learn more about our program options and how each cohort works.
Qwasar vs. Others
How do Qwasar programs compare to other tech training options out there? Find out how we stack up.
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