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Remote Sports Engineering Jobs (NOW HIRING)

Sleeper

Senior Software Engineer, Backend

San Francisco, CA · On-site +1

$170K - $220K/yr

As one of the fastest-growing platforms in the fantasy sports and real-money gaming space, we're ... Who You Are * 5+ years of backend software engineering experience. * Proven ability to build and ...

SumerSports LLC

AI Application Engineer

Implement prompt-based and retrieval-augmented systems (RAG) that answer complex sports questions ... Strong understanding of prompt engineering, retrieval-augmented generation, and evaluation ...

CEI Services

Electrical Engineering Manager

Rockford, IL · On-site +1

$110K - $141K/yr

This position can be remote with travel or hybrid based in our Rockford, IL office with up to 50 ... Our General Building expertise includes airports, seaport terminals, sports and entertainment, pre ...

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Remote Sports Engineering information

See salary details

$46.5K

$146.9K

$174K

How much do remote sports engineering jobs pay per year?

As of Jun 17, 2026, the average yearly pay for remote sports engineering in the United States is $146,868.00, according to ZipRecruiter salary data. Most workers in this role earn between $116,500.00 and $173,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Remote Sports Engineer, and why are they important?

To thrive as a Remote Sports Engineer, you need a solid background in engineering, sports technology, and data analysis, often supported by a relevant degree in engineering or sports science. Familiarity with broadcast systems, sensor technologies, and specialized sports performance software is typically required. Strong problem-solving abilities, teamwork, and clear communication help engineers collaborate with coaches, athletes, and technical teams. These skills ensure the reliable delivery and optimization of sports technology solutions, directly impacting performance analysis and broadcast quality in remote environments.

What is the difference between Remote Sports Engineering vs Remote Mechanical Engineering?

AspectRemote Sports EngineeringRemote Mechanical Engineering
Required CredentialsBachelor's or higher in sports engineering, mechanical engineering, or related fields; certifications in sports technologyBachelor's or higher in mechanical engineering; professional engineer (PE) license often preferred
Work EnvironmentPrimarily project-based, focused on sports equipment, wearable tech, and athlete performance systems; often collaborativeDesign, analysis, and testing of mechanical systems; can include product development and simulation work
Employer & Industry UsageSports teams, athletic equipment companies, sports tech startupsManufacturing firms, engineering consultancies, industrial companies

Remote Sports Engineering and Remote Mechanical Engineering share a foundation in engineering principles and often require similar educational backgrounds. However, Sports Engineering focuses specifically on sports-related technology and equipment, while Mechanical Engineering covers a broader range of industries. Both roles can be performed remotely, but their applications and industry focus differ significantly.

What is remote sports engineering?

Remote sports engineering involves designing, developing, and supporting technical systems that allow sports events to be broadcast, analyzed, or managed from off-site locations. This field combines sports knowledge with engineering skills to enable real-time data collection, video streaming, and communication technologies. Professionals in remote sports engineering help ensure seamless live coverage and analysis, especially for events that require remote operations due to logistical or safety reasons. The work may involve setting up remote camera systems, data analytics platforms, and cloud-based broadcast solutions.

What are some common challenges faced by professionals in remote sports engineering, and how can they be addressed?

Professionals in remote sports engineering often encounter challenges such as coordinating with on-site teams across different time zones, ensuring reliable data transmission from sporting venues, and troubleshooting technical issues without being physically present. To address these obstacles, clear communication protocols, robust remote monitoring tools, and regular virtual check-ins with the on-site crew are essential. Additionally, building strong relationships with local staff and maintaining detailed documentation of system setups can significantly improve efficiency and problem-solving.
More about Remote Sports Engineering jobs
What cities are hiring for Remote Sports Engineering jobs? Cities with the most Remote Sports Engineering job openings:
What are the most commonly searched types of Sports Engineering jobs? The most popular types of Sports Engineering jobs are:
What states have the most Remote Sports Engineering jobs? States with the most job openings for Remote Sports Engineering jobs include:
What job categories do people searching Remote Sports Engineering jobs look for? The top searched job categories for Remote Sports Engineering jobs are:
Remote Sports Engineering jobs near you
Infographic showing various Remote Sports Engineering job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 100% Remote job distribution, with an average salary of $146,868 per year, or $70.6 per hour.
Machine Learning Engineer

Machine Learning Engineer

Swish Analytics

San Francisco, CA • Remote

$160K/yr

Full-time

Posted 29 days ago

Job description

Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports betting expertise; not intuition. We're looking for team-oriented individuals with an authentic passion for accurate and predictive real-time data who can execute in a fast-paced, creative, and continually-evolving environment without sacrificing technical excellence. Our challenges are unique, so we hope you are comfortable in uncharted territory and passionate about building systems to support products across a variety of industries and enterprise clients.

The Data Science team is hiring an experienced Machine Learning Engineer with a background building machine learning and statistical modeling frameworks from scratch. They can assist with optimizing the different aspects of the modeling process (Data Validation, Data Visualization, Data Stores & Structures, Feature Engineering, Model Training & Evaluation, Deployments) and improving a variety of Swish products. They will know when to “roll your own” and when to outsource a particular step in the modeling process. They will engineer custom solutions to solve complex data-related sports challenges across multiple leagues.

This position is 100% remote

Responsibilities:

  • Design, prototype, implement, evaluate, optimize systems to generate sports datasets and predictions with high accuracy and low latency.

  • Evaluate internal modeling frameworks and tools to optimize data scientist's modeling workflow.

  • Build, test, deploy and maintain production systems.

  • Work closely with DevOps and Data Engineering teams to assist with implementation, optimization and scale workloads on Kubernetes using CI/CD, automation tools and scripting languages.

  • Support maintenance and optimization of cloud-native EDW and ETL solutions.

  • Maintain and promote best practices for software development, including deployment process, documentation, and coding standards.

  • Experience applying large scale data processing techniques to develop scalable and innovative sports betting products.

  • Use extensive experience to build, test, debug, and deploy production-grade components.

  • Experience applying large scale data processing techniques to develop scalable and innovative sports betting products.

  • Participate in development of database structures that fit into the overall architecture of Swish systems

Qualifications:

  • Masters degree in Computer Science, Applied Mathematics, Data Science, Computational Physics/Chemistry or related technical subject area

  • 5+ years of demonstrated experience developing and delivering clean and efficient production code to serve business needs

  • A proven background in quantitative analytics, trading, or engineering is required for this position

  • Demonstrated experience developing data science modeling systems and infrastructure at scale

  • Experience with Python and exposure to modern machine learning frameworks

  • Proficient in SQL; experience with MySQL

  • Background and/or interest in Rust preferred

  • Affinity for teamwork and collaboration with others to solve problems, share knowledge, and provide feedback

  • Strong communication skills when discussing technical concepts with technical and non-technical colleagues

Base salary: starting at $160,000 base plus bonus potential

Swish Analytics is an Equal Opportunity Employer. All candidates who meet the qualifications will be considered without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, pregnancy status, genetic, military, veteran status, marital status, or any other characteristic protected by law. The position responsibilities are not limited to the responsibilities outlined above and are subject to change. At the employer’s discretion, this position may require successful completion of background and reference checks.