Machine Learning Engineer - Recommendation Systems
xAI is hiring a Machine Learning Engineer - Recommendation Systems in Palo Alto, United States. It pays $180k-$440k a year and Level rates it ; you can apply on xAI.
Level's read on this job
- On Level's scale, this machine learning engineer role is Builds AI ●●●●: the job is building AI systems.
- The posting mentions PyTorch and JAX among its tools and skills.
- xAI has 296 other open roles on Level.
AI in this role
SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars.
SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.
ABOUT THE ROLE:
We’re seeking exceptional Applied engineers to join a high-priority project that approximately 600 million monthly users use. This is an exciting opportunity for individuals with a engineer or scientist background to apply their skills to recommendation systems, ranking algorithms, search technologies, and many other systems. You’ll work at the intersection of advanced AI development and real-world impact, enhancing the ability to connect users with relevant content, accounts, and experiences.
RESPONSIBILITIES:
- Designing and architecting recommendation algorithms across various product surfaces
- Leverage all of SpaceXSI's infra and AI stacks to dramatically enhance the user experience
- Write data pipelines and training jobs that continuously learn from product data.
- Iterate and improve the algorithm by gathering user feedback in real time through experimentation
- Ensuring scalability and efficiency of machine learning systems
BASIC QUALIFICATIONS:
- Knowledge of data infrastructure like Kafka, Clickhouse, and Spark
- Experienced in implementing recommender systems and/or deep learning applications at industrial scale
- Skilled in one or more DL software frameworks such as JAX or PyTorch
- Exceptional candidates may be experienced in writing CUDA kernels
COMPENSATION AND BENEFITS:
$180,000 - $440,000 USD
Base salary is just one part of our total rewards package at SpaceXSI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks.
SpaceXSI is an equal opportunity employer. For details on data processing, view our Recruitment Privacy Notice.
How we rate this
Machine Learning Engineer - Recommendation Systems at xAI rates 92 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in machine learning engineer jobs at this level
Pay
- This job starts at
- $180k
- Typical machine learning engineer job
- $199k
This posting starts 10% below the typical machine learning engineer job. See machine learning engineer jobs
Questions you could be asked
- What's a project where you used PyTorch hands-on?
- Walk me through how you've used JAX in your day-to-day work.
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: PyTorch and JAX. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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