Senior Software Engineer (Machine Learning) - Economy ML
Roblox is hiring a Senior Software Engineer (Machine Learning) - Economy ML in San Mateo, United States. It pays $243k-$295k a year and Level rates it ; you can apply on Roblox.
Level's read on this job
- Level rates this software engineer role Builds AI ●●●●: the job is building AI systems.
- The listed pay starts 46% above the typical software engineer posting on Level.
- Roblox mentions visa sponsorship in this posting.
- Tools and skills the posting cites include PyTorch.
- Level lists 267 other open roles at Roblox.
AI in this role
Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.
At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there.
A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone.
The Economy ML team is building the machine learning backbone for Roblox’s virtual economy - from intelligent pricing and personalized storefronts to dynamic layout optimization and avatar content understanding. We partner closely with Marketplace, Payments, and Developer Monetization to deliver ML systems that drive long‑term value across engagement, retention, and creator earnings.
As a Senior ML Software Engineer on the team, you will build the infrastructure and backend services that train, serve, and integrate the models powering ranking, personalization, and pricing across Roblox’s economic surfaces, including Marketplace, Developer Monetization, and Payments. Your work determines how quickly we can take new models from idea to production, and how well they perform once they are serving millions of users.
You Will
- Build and operate the training infrastructure behind Economy ML’s ranking, recommendation, and pricing models: distributed training jobs, efficient data loading, and reproducible pipelines from raw data to trained model.
- Co-design models and systems with ML engineers: profile and optimize training and inference for our ranking and recommendation models, improving GPU utilization, memory, throughput, and latency (e.g., moving models to GPU serving, request batching, mixed precision, faster request-time feature processing).
- Design and build the backend services and APIs that bring model predictions into Marketplace, Payments, and Developer Monetization, with clear latency budgets and graceful fallbacks.
- Build and scale the batch and real-time data and feature pipelines that feed training and serving, with a focus on freshness, correctness, and training/serving consistency.
- Own the path from trained model to production serving: packaging, deployment, versioning, shadow and canary rollouts, and integration with A/B experiments on Roblox’s ML platform.
- Make the systems you own reliable and observable, and set the team’s best practices for logging, monitoring, alerting, and on-call across ML services.
- Partner with Roblox’s central ML platform and data infrastructure teams, and with Marketplace / Economy backend teams, to adopt shared infrastructure and contribute improvements back to it.
- Contribute to the technical direction of the Economy ML team: help shape our training and serving stack, infrastructure roadmap, and the tradeoffs between model quality, latency, and cost.
- Mentor other engineers through design reviews, code reviews, and hands-on guidance, helping raise the technical bar of the broader Economy organization.
You Have
- 5+ years of software engineering experience, including 2+ years building and operating ML infrastructure or ML-backed production services at high scale. Experience with recommendation, search, or ads systems is a strong plus.
- Solid engineering fundamentals in Python and at least one other backend language, with a track record of designing and operating distributed, low-latency services.
- Hands-on experience training and serving deep learning models in production with PyTorch, including scaling training across GPUs and nodes and deploying models on GPU inference servers. Comfortable profiling and optimizing for utilization, memory, latency, and cost.
- Experience building batch and streaming data pipelines and feature stores (e.g., Spark, Flink, Kafka), with attention to data quality and training/serving skew.
- You build systems that are easy to debug and operate, and you take ownership of production health, from monitoring and alerting to incident response.
- You enjoy working closely with ML engineers, platform teams, product managers, and data scientists across Marketplace, Payments, and Developer Monetization, and you can drive alignment in complex, multi‑stakeholder environments.
- You understand how infrastructure choices (latency, freshness, cost) affect model quality and product metrics, and you prioritize work by its impact on users, creators, and the business.
- BS, MS, or Ph.D. in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on this page.
Annual Salary Range$243,290—$295,250 USDRoles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).
Roblox provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Roblox also provides reasonable accommodations to candidates with qualifying disabilities or religious beliefs during the recruiting process.
For US based roles only, please note the Company may not be able to employ candidates for this role who have United States work authorization related to certain U.S. visa categories, or support future H-1B sponsorship at this time.
How we rate this
Senior Software Engineer (Machine Learning) - Economy ML at Roblox rates 97 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
Pay
- This job starts at
- $243k
- Typical software engineer job
- $167k
This posting starts 46% above the typical software engineer job. See software engineer jobs
Questions you could be asked
- What's a project where you used PyTorch hands-on?
- 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. 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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