Is the MLOps / ML Platform Engineer position at SumerSports fully remote?

Yes, this MLOps / ML Platform Engineer position at SumerSports is a remote position. The role is available for candidates in United States.

Is this a full-time or part-time MLOps / ML Platform Engineer role?

This is a full-time MLOps / ML Platform Engineer position at SumerSports.

What benefits does SumerSports offer for this position?

SumerSports offers the following benefits: Health Insurance, Remote Work.

What is the salary range for the MLOps / ML Platform Engineer position?

The salary range for this MLOps / ML Platform Engineer role is $170k - $200k.

How do I apply for the MLOps / ML Platform Engineer position at SumerSports?

You can apply for this MLOps / ML Platform Engineer position by clicking the "Apply Now" button on this page, which will take you to SumerSports's application portal.

MLOps / ML Platform Engineer at SumerSports

United States

$170k - $200k

<div><body>Responsibilities<ul style='font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;'><li style="margin:0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;text-align:start;">Design and operate ML infrastructure: Manage data, training, serving, and inference systems for high-throughput model workflows.</li><li style="margin:0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;text-align:start;">Build scalable pipelines: Implement reproducible training and evaluation pipelines with versioning, scheduling, and artifact tracking.</li><li style="margin:0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;text-align:start;">Optimize compute and cost: Tune GPU and CPU workloads, manage clusters, and drive efficiency via rightsizing, spot scheduling, and caching.</li><li style="margin:0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;text-align:start;">Serve models in production: Operate APIs for low-latency inference with autoscaling, blue-green or canary rollouts, and rollback safety.</li><li style="margin:0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;text-align:start;">Ensure reliability and observability: Define and own SLOs; instrument pipelines and services to track latency, cost, drift, and data quality.</li><li style="margin:0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;text-align:start;">Secure and automate: Manage IAM, secrets, and container security; automate deployment pipelines via CI/CD and infrastructure as code.</li><li style="margin:0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;text-align:start;">Collaborate cross-functionally: Partner with research scientists and AI engineers to deliver models from experiment to production with minimal friction.</li><li style="margin:0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;text-align:start;">Document and enable: Build templates, runbooks, and internal tooling that make ML workflows repeatable, safe, and fast.</li></ul>Qualifications<ul style='font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;'><li style="margin:0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;text-align:start;">4+ years of experience in ML platform, DevOps, or infrastructure engineering.</li><li style="margin:0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;text-align:start;">Deep knowledge of Kubernetes, CI/CD, containers, and cloud infrastructure (AWS, GCP, or Azure).</li><li style="margin:0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;text-align:start;">Hands-on experience managing GPU clusters and training/inference pipelines.</li><li style="margin:0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;text-align:start;">Familiarity with data orchestration and storage formats (Delta, Parquet, Polars, Spark).</li><li style="margin:0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;text-align:start;">Proven ability to ship and operate production ML systems with SLOs.</li><li style="margin:0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;text-align:start;">Strong Python skills and comfort with infrastructure as code and automation.</li><li style="margin:0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;text-align:start;">Experience with observability and cost optimization at scale.</li></ul>Nice to Have<ul style='font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;'><li style="margin:0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;text-align:start;">Experience with real-time or low-latency model serving (REST, gRPC).</li><li style="margin:0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;text-align:start;">Exposure to model registry and promotion workflows.</li><li style="margin:0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;text-align:start;">Familiarity with data quality, lineage, and curation pipelines.</li><li style="margin:0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;text-align:start;">Background in sports analytics or other high-volume data domains.</li><li style="margin:0px;font-size:11pt;line-height:1.38;letter-spacing:0.25px;text-align:start;">Experience integrating LLM workflows or evaluation pipelines.</li></ul>Benefits<ul style='font-family:"Basel Grotesk",Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;'><li style="margin:0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;">Competitive Salary and Bonus Plan</li><li style="margin:0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;">Comprehensive health insurance plan</li><li style="margin:0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;">Retirement savings plan (401k) with company match</li><li style="margin:0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;">Remote working environment</li><li style="margin:0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;">A flexible, unlimited time off policy</li><li style="margin:0px;font-size:12pt;line-height:1.38;letter-spacing:0.25px;">Generous paid holiday schedule - 13 in total including Monday after the Super Bowl</li></ul> 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.</body></div>

Apply Now

Remote Opportunity

MLOps / ML Platform Engineer

Join SumerSports as a mid professional working remotely from United States. Explore the role, benefits, and apply in one place.

MLOps / ML Platform Engineer

SumerSports

Full Time

$170k - $200k

2 weeks ago

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.

United States

Canada

Mid

MLOps

Kubernetes

CI/CD

+5 more

Job Description

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.

Requirements

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.

Benefits

Health Insurance
Remote Work

Skills

MLOps

Kubernetes

CI/CD

Cloud infrastructure

AWS

GCP

Azure

Python

Ready to Apply?

Join SumerSports today

Salary Range

$170k - $200k

Posted 2 weeks ago

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