Pay
Higher than most similar jobs
$220K–$280K against $166K–$240K, the middle half of what similar US postings state.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Staff Machine Learning Engineer
Atlanta, GA preferred, Remote
Open 6 months, first seen by us 188 days ago- Pay
$220,000–280,000/yearAnnual period assumed — pay source
Working at PrizePicks: The typical salary range for this position is $220,000 to $280,000. At PrizePicks, we consider your role, level, and where you'll be working when determining our salary ranges. The compensation info you see on our job postings gives you an idea of the starting pay range for the position. Your actual pay within that range will depend on your specific work location, as well as your skills, experience, and education. Your recruiter will be happy to chat more about the specific pay range for your location and how we arrived at it during the hiring process. This application period will remain open for 30 days. We’re committed to finding the best candidate, so this date may be adjusted, and any changes will be reflected in this posting.
Read the full posting- 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 postingDesign end-to-end ML infrastructure that moves experimental models into production
Build low-latency services that serve model inferences in milliseconds
Lead creation of a centralized feature store and data pipelines with Data Science
Implement automated retraining pipelines and drift and degradation monitoring
From the employer’s posting
Design and build the end-to-end machine learning infrastructure, transitioning experimental Data Science models into robust, high-availability production services.
Steer the design and deployment of low-latency services to 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 tools to ensure data drift and model degradation are caught and addressed instantly.
Posting record
Report an errorOpen 6 monthsWe first saw this posting 188 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
- May 1, 2026188 days ago
- Recorded sightings
- 154
- 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 188
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
- SQL
- AWS
- BigQuery
- Databricks
- GCP
- Kafka
- MLflow
- SageMaker
Where the posting names them
- MLOps Expertise: Deep experience managing the full ML lifecycle (training, deploying, monitoring) using tools like MLFlow, Kubeflow, Databricks, or SageMaker. - Strong Coding Skills: Expert in Python and SQL; proficiency in Go, C++, or Rust is a strong plus for building high-performance inference layers. - Cloud Native: Deep experience with GCP services (BigQuery, Cloud Functions, GKE, Vertex AI) or AWS equivalents.
- Strong Coding Skills: Expert in Python and SQL; proficiency in Go, C++, or Rust is a strong plus for building high-performance inference layers. - Cloud Native: Deep experience with GCP services (BigQuery, Cloud Functions, GKE, Vertex AI) or AWS equivalents. What makes you stand out:
- 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 managing the full ML lifecycle (training, deploying, monitoring) using tools like MLFlow, Kubeflow, Databricks, or SageMaker. - Strong Coding Skills: Expert in Python and SQL; proficiency in Go, C++, or Rust is a strong plus for building high-performance inference layers.
- 3+ years of technical leadership, acting as a lead and driving architecture decisions for consumer applications or scalable backend platforms. - 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 managing the full ML lifecycle (training, deploying, monitoring) using tools like MLFlow, Kubeflow, Databricks, or SageMaker.
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 Staff Machine Learning Engineer, you will lead the technical charge 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:- Architect Scalable ML Systems: Design and build the end-to-end machine learning infrastructure, transitioning experimental Data Science models into robust, high-availability production services.
- Real-Time Inference at Scale: Steer the design and deployment of low-latency services to 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: Partner with Data Science to build scalable logging and data pipelines. 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 Leadership: Champion best practices for model deployment, monitoring, and CI/CD for ML. You will implement automated retraining pipelines and observability tools to ensure data drift and model degradation are caught and addressed instantly.
- 7+ years of experience in Machine Learning Engineering or Backend Engineering, with a proven track record of deploying and maintaining complex ML models in high-traffic production environments.
- 3+ years of technical leadership, acting as a lead and driving architecture decisions for consumer applications or scalable backend platforms.
- 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 managing the full ML lifecycle (training, deploying, monitoring) using tools like MLFlow, Kubeflow, Databricks, or SageMaker.
- Strong Coding Skills: Expert in Python and SQL; proficiency in Go, C++, or Rust is a strong plus for building high-performance inference layers.
- Cloud Native: Deep experience with GCP services (BigQuery, Cloud Functions, GKE, Vertex AI) or AWS equivalents.
- Experience implementing reinforcement learning or complex probabilistic models for dynamic pricing, risk management, or fraud detection.
- 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.
- 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
The typical salary range for this position is $220,000 to $280,000. At PrizePicks, we consider your role, level, and where you'll be working when determining our salary ranges. The compensation info you see on our job postings gives you an idea of the starting pay range for the position. Your actual pay within that range will depend on your specific work location, as well as your skills, experience, and education. Your recruiter will be happy to chat more about the specific pay range for your location and how we arrived at it during the hiring process.
This application period will remain open for 30 days. We’re committed to finding the best candidate, so this date may be adjusted, and any changes will be reflected in this posting.
Date Posted: 4/16/2026
1st Extension: 5/16/2026
2nd Extension: 6/16/2026
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
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Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
Working at PrizePicks: The typical salary range for this position is $220,000 to $280,000. At PrizePicks, we consider your role, level, and where you'll be working when determining our salary ranges. The compensation info you see on our job postings gives you an idea of the starting pay range for the position. Your actual pay within that range will depend on your specific work location, as well as your skills, experience, and education. Your recruiter will be happy to chat more about the specific pay range for your location and how we arrived at it during the hiring process. This application period will remain open for 30 days. We’re committed to finding the best candidate, so this date may be adjusted, and any changes will be reflected in this posting.
- 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 Working at PrizePicks:
- 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.