Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Machine Learning Platform Engineer
Atlanta, GA preferred, Remote
Open 3 months, first seen by us 92 days ago- Pay
- Pay not listedSimilar roles state $166K–$240K
- Work setting
Remote — work setting source
Listed location: Atlanta, GA preferred, Remote
Read the full posting- Employment
- Not stated
Before you apply
- Work authorization
- Must already have the right to work in the US
You must be authorized to work for any employer in the US We are unable to sponsor or take over sponsorship of an employment Visa at this time
What you’ll work on
Full postingBuild end-to-end ML infrastructure that moves models from experiments into production
Deploy low-latency services that serve model inferences in milliseconds
Lead creation of a centralized feature store for training models
Implement automated retraining pipelines and ML observability for drift
From the employer’s posting
Design and build the end-to-end machine learning infrastructure
Build automation to deploy low-latency services that serve model inferences in milliseconds
You will lead the creation and optimization of a centralized feature store
You will implement automated retraining pipelines and observability for ML systems
What you’ll bring
All qualificationsCore experience
- 3+ years in platform engineering running a scalable ML platform in production
- 1+ years owning ML systems end-to-end in production, including on-call
- Streaming architecture experience (Kafka, Flink, or PubSub)
- Expert in Python; proficiency in Go
- Proficient with Docker and Kubernetes
Qualification wording
3+ years of experience in Platform Engineering
1+ years of experience owning ML systems end-to-end in production, including on-call and incident response.
proficient in streaming architectures (Kafka/Flink/PubSub)
Expert in Python, proficiency in Go.
Proficient with Containerization, Docker, Kubernetes, and cluster-level management.
Posting record
Report an errorOpen 3 monthsWe first saw this posting 92 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Aug 4, 202692 days ago
- Recorded sightings
- 50
- Last seen by us
- Nov 5, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 92
Up longer than most. Similar jobs usually come down between day 6 and day 70.
From 4,444 similar postings that came down. Coming down is not always a hire.
PrizePicks is hiring
10 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Hiring at a steady pace
- Its jobs tend to stay up a long time
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- C++
- Python
- Docker
- Kafka
- Kubernetes
- Redis
- SageMaker
- Go
- Elasticsearch
Where the posting names them
- Proficient with Containerization, Docker, Kubernetes, and cluster-level management. - Expert in Python, proficiency in Go. C++, or Rust is a strong plus for building high-performance inference layers. What makes you stand out:
- MLOps Expertise, deep experience building a platform for managing the full ML lifecycle (training, deploying, monitoring) using tools like SageMaker, VertexAI, Vector DBs, Graph Databases. Managing and scaling caches like Redis or Elasticsearch. - Proficient with Containerization, Docker, Kubernetes, and cluster-level management. - Expert in Python, proficiency in Go. C++, or Rust is a strong plus for building high-performance inference layers.
- 1+ years of experience owning ML systems end-to-end in production, including on-call and incident response. - Experience with Real-Time Data, proficient in streaming architectures (Kafka/Flink/PubSub) and building low-latency services to serve model inference in <100ms. - MLOps Expertise, deep experience building a platform for managing the full ML lifecycle (training, deploying, monitoring) using tools like SageMaker, VertexAI, Vector DBs, Graph Databases. Managing and scaling caches like Redis or Elasticsearch.
- Experience with Real-Time Data, proficient in streaming architectures (Kafka/Flink/PubSub) and building low-latency services to serve model inference in <100ms. - MLOps Expertise, deep experience building a platform for managing the full ML lifecycle (training, deploying, monitoring) using tools like SageMaker, VertexAI, Vector DBs, Graph Databases. Managing and scaling caches like Redis or Elasticsearch. - Proficient with Containerization, Docker, Kubernetes, and cluster-level management.
Your next step
Complete your application on prizepicks.com. The employer’s form will show what is required.
Applying through Greenhouse
- No account needed: the form is one page, with an “Autofill my application” option that reads your resume.
- Greenhouse doesn’t read resumes over 2.5 MB, or details placed in a header, footer, text box, table or second column. The file still attaches, but a recruiter has to type you in by hand.
Already applied? Track this application
Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
At PrizePicks, we are the fastest-growing sports company in North America, as recognized by Inc. 5000. As the leading platform for Daily Fantasy Sports, we cover a diverse range of sports leagues, including the NFL, NBA, and Esports titles like League of Legends and Counter-Strike. Our team of over 550 employees thrives in an inclusive culture that values individuals from diverse backgrounds, regardless of their level of sports fandom. Ready to reimagine the DFS industry together?
As a ML Platform Engineer, you will contribute to building the ML platform at Prizepicks to scale and productionize our core machine learning capabilities. Your work will directly impact key metrics like Time-to-Bet, Deposit Velocity, and Platform Integrity by integrating robust, low-latency ML models across our sports betting and daily fantasy ecosystems.
What you’ll do:- Build Scalable ML Systems: Design and build the end-to-end machine learning infrastructure, setup platform for transitioning experimental Data Science models into robust, high-availability production services.
- Real-Time Inference at Scale: Build automation to deploy low-latency services that serve model inferences in milliseconds. You will power real-time decisions across the platform, from dynamic oddsmaking and risk analysis to smart deposit defaults.
- Feature Engineering & Data Strategy: You will lead the creation and optimization of a centralized feature store required to train complex models across diverse business domains.
- End-to-End MLOps: You will work with the Infrastructure team to build and operate core ML platform components for training and experimentation, with a focus on developer experience. You will champion best practices for model deployment, monitoring, and CI/CD for ML. You will implement automated retraining pipelines and observability for ML systems to ensure data drift and model degradation are caught and addressed instantly.
- 3+ years of experience in Platform Engineering, with a proven track record of deploying and maintaining a scalable ML platform in high-traffic production environments.
- 1+ years of experience owning ML systems end-to-end in production, including on-call and incident response.
- Experience with Real-Time Data, proficient in streaming architectures (Kafka/Flink/PubSub) and building low-latency services to serve model inference in <100ms.
- MLOps Expertise, deep experience building a platform for managing the full ML lifecycle (training, deploying, monitoring) using tools like SageMaker, VertexAI, Vector DBs, Graph Databases. Managing and scaling caches like Redis or Elasticsearch.
- Proficient with Containerization, Docker, Kubernetes, and cluster-level management.
- Expert in Python, proficiency in Go. C++, or Rust is a strong plus for building high-performance inference layers.
- Experience implementing infrastructure while enforcing best practices for the deployment of ML Platform.
- Background in Daily Fantasy Sports (DFS), oddsmaking, or high-frequency trading.
- Experience building and scaling "Feature Stores" that successfully bridge batch historical data with real-time event streams.
- Enabling self-service for ML and Data Science teams for model development and deployment.
- Enabling AI agents and AI coding for faster and iterative software development.
- While we prefer candidates based in Atlanta, we are open to qualified applicants from anywhere in the U.S. and are willing to consider remote candidates. #LI-Remote
Benefits you’ll receive:
In addition to your great compensation package, full-time employees will be eligible for the following perks:
- Company-subsidized medical, dental, & vision plans
- 401(k) plan with company match
- Annual bonus
- Flexible PTO to encourage a healthy work/life balance (2 weeks STRONGLY encouraged!)
- Generous paid leave programs, including 16-week paid parental leave and disability benefits
- Workplace flexibility and modern work schedules focused on getting the job done, not hours clocked
- Company-wide in-person events and team outings
- Lifestyle enhancement program
- Company equipment provided (Windows & Mac options)
- Annual performance reviews with opportunities for growth and career development
You must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.
PrizePicks is an Equal Opportunity Employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Atlanta, GA preferred, Remote
Where you’ll live: - While we prefer candidates based in Atlanta, we are open to qualified applicants from anywhere in the U.S. and are willing to consider remote candidates. #LI-Remote Benefits you’ll receive:
- Work authorization
- Annual performance reviews with opportunities for growth and career development You must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time. PrizePicks is an Equal Opportunity Employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.