MLOps / ML Platform Engineer

Remote (United States), Canada

Live, first seen by us 52 days ago

Pay: Pay not listed
Work setting: Remote — work setting source
Listed location: Remote (United States), Canada
Read the full posting
Employment: Full-time — employment source
Employment type in the employer’s posting: SALARIED_FT
From the employer’s posting

What you’ll work on

Full posting

Run training, serving and inference systems for high-throughput model workflows
Build reproducible training and evaluation pipelines with versioning and scheduling
Operate low-latency model APIs with autoscaling and canary or blue-green rollouts
Tune GPU and CPU workloads and cut compute cost through rightsizing and spot scheduling
Define SLOs and instrument services to track latency, cost, drift and data quality

From the employer’s posting

Design and operate ML infrastructure: Manage data, training, serving, and inference systems for high-throughput model workflows.

Implement reproducible training and evaluation pipelines with versioning, scheduling, and artifact tracking.

Operate APIs for low-latency inference with autoscaling, blue-green or canary rollouts, and rollback safety.

Tune GPU and CPU workloads, manage clusters, and drive efficiency via rightsizing, spot scheduling, and caching.

Define and own SLOs; instrument pipelines and services to track latency, cost, drift, and data quality.

What you’ll bring

All qualifications

Core experience

4+ years of experience in ML platform, DevOps, or infrastructure engineering.
Experience with real-time or low-latency model serving (REST, gRPC).
Deep knowledge of Kubernetes, CI/CD, containers, and cloud infrastructure (AWS, GCP, or Azure).
Familiarity with data quality, lineage, and curation pipelines.
Hands-on experience managing GPU clusters and training/inference pipelines.
Experience integrating LLM workflows or evaluation pipelines.

Qualification wording

4+ years of experience in ML platform, DevOps, or infrastructure engineering.

Experience with real-time or low-latency model serving (REST, gRPC).

Deep knowledge of Kubernetes, CI/CD, containers, and cloud infrastructure (AWS, GCP, or Azure).

Familiarity with data quality, lineage, and curation pipelines.

Hands-on experience managing GPU clusters and training/inference pipelines.

Experience integrating LLM workflows or evaluation pipelines.

Benefits in the posting

Full benefits wording

Competitive Salary and Bonus Plan
Comprehensive health insurance plan
Retirement savings plan (401k) with company match
Remote working environment
Generous paid holiday schedule - 13 in total including Monday after the Super Bowl
The total compensation package for this position may also include annual performance bonus, benefits and/or other applicable incentive compensation plans.

From the employer’s posting.

Apply at SumerSports

Posting record

Report an error

Status in our records: Live
First seen by us: Sep 13, 202652 days ago
Recorded sightings: 29
Last seen by us: Nov 4, 2026
Employer says posted: Sep 10, 2026
Reposted: once since Aug 13, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications. We count a repost when the same title and location comes back as a new listing within 60 days of the last one coming down. Some hiring systems expire listings on a schedule, so repost rates vary by system.

How this job compares

Time since we first saw it

Day 52

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.

SumerSports is hiring

2 open jobs

Open jobs by the week we first saw them, last 12 weeks.

100% of open jobs were reposted

Hiring record

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.

Python
AWS
Azure
GCP
Kubernetes
Spark
Polars

Where the posting names them

- Proven ability to ship and operate production ML systems with SLOs. - Strong Python skills and comfort with infrastructure as code and automation. - Experience with observability and cost optimization at scale.

- 4+ years of experience in ML platform, DevOps, or infrastructure engineering. - Deep knowledge of Kubernetes, CI/CD, containers, and cloud infrastructure (AWS, GCP, or Azure). - Hands-on experience managing GPU clusters and training/inference pipelines.

- Hands-on experience managing GPU clusters and training/inference pipelines. - Familiarity with data orchestration and storage formats (Delta, Parquet, Polars, Spark). - Proven ability to ship and operate production ML systems with SLOs.

Upload resume Add LinkedIn

Your next step

Complete your application on ats.rippling.com. The employer’s form will show what is required.

Applying through Rippling

“The résumé will be parsed to fill in the application details”: check what it filled before you send.

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.

August 17Last week

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

View original posting ↗

SumerSports is a leading football intelligence technology company that specializes in providing an innovative suite of products for football fans and NFL clubs. We are a collection of executives, engineers, data scientists, and visionaries from NFL clubs, technology startups, finance, and academia.

Our data-driven platform empowers teams with insights and tools to make informed decisions within salary cap constraints. The platform also serves the NCAA, offering insights around the transfer portal and more.

What sets us apart is our unique blend of big tech talent, data scientists, and former NFL personnel, who have a combined 600+ years of NFL experience. Our domain knowledge is augmented by AI and machine learning technologies to create a unique view into many aspects of Football.

Responsibilities

Design and operate ML infrastructure: Manage data, training, serving, and inference systems for high-throughput model workflows.
Build scalable pipelines: Implement reproducible training and evaluation pipelines with versioning, scheduling, and artifact tracking.
Optimize compute and cost: Tune GPU and CPU workloads, manage clusters, and drive efficiency via rightsizing, spot scheduling, and caching.
Serve models in production: Operate APIs for low-latency inference with autoscaling, blue-green or canary rollouts, and rollback safety.
Ensure reliability and observability: Define and own SLOs; instrument pipelines and services to track latency, cost, drift, and data quality.
Secure and automate: Manage IAM, secrets, and container security; automate deployment pipelines via CI/CD and infrastructure as code.
Collaborate cross-functionally: Partner with research scientists and AI engineers to deliver models from experiment to production with minimal friction.
Document and enable: Build templates, runbooks, and internal tooling that make ML workflows repeatable, safe, and fast.

Qualifications

4+ years of experience in ML platform, DevOps, or infrastructure engineering.
Deep knowledge of Kubernetes, CI/CD, containers, and cloud infrastructure (AWS, GCP, or Azure).
Hands-on experience managing GPU clusters and training/inference pipelines.
Familiarity with data orchestration and storage formats (Delta, Parquet, Polars, Spark).
Proven ability to ship and operate production ML systems with SLOs.
Strong Python skills and comfort with infrastructure as code and automation.
Experience with observability and cost optimization at scale.

Nice to Have

Experience with real-time or low-latency model serving (REST, gRPC).
Exposure to model registry and promotion workflows.
Familiarity with data quality, lineage, and curation pipelines.
Background in sports analytics or other high-volume data domains.
Experience integrating LLM workflows or evaluation pipelines.

Benefits

Competitive Salary and Bonus Plan
Comprehensive health insurance plan
Retirement savings plan (401k) with company match
Remote working environment
A flexible, unlimited time off policy
Generous paid holiday schedule - 13 in total including Monday after the Super Bowl

SumerSports is committed to fair and equitable compensation practices.

Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor.

The total compensation package for this position may also include annual performance bonus, benefits and/or other applicable incentive compensation plans.

Source details