ZipRecruiter

Log In

1

Sports Ai Software Jobs (NOW HIRING)

Blitzy

Software Developer Growth Engineer

Cambridge, MA · On-site

$140K - $180K/yr

About Blitzy Blitzy is a Cambridge, MA based AI software development platform on a mission to ... We operate like a professional sports team. We win as a team by holding ourselves and each other to ...

NY 411

Senior Director, AI Platforms & Ops

Manhattan, NY · On-site

$140 - $180/hr

Overview This is a Full Time position in the field of Live Broadcast / Sports / TV News / Technical ... You will partner closely with Forward Deployed AI teams, AI Software Engineering, Security, Legal ...

New Balance

Principal Software Engineer - AI

Boston, MA · On-site

$120K - $150K/yr

Since 1906, New Balance has empowered people through sport and craftsmanship to create positive ... New Balance is seeking an experienced Principal Software Engineer to join our Enterprise AI ...

New Balance

Principal Software Engineer - AI

Boston, MA · On-site

$120K - $150K/yr

Since 1906, New Balance has empowered people through sport and craftsmanship to create positive ... New Balance is seeking an experienced Principal Software Engineer to join our Enterprise AI ...

previous pagenext page

Showing results 21-40

All JobsSports Ai Software Jobs

Sports Ai Software information

See salary details

$48K

$111.8K

$166K

How much do sports ai software jobs pay per year?

As of Aug 12, 2026, the average yearly pay for sports ai software in the United States is $111,845.00, according to ZipRecruiter salary data. Most workers in this role earn between $90,000.00 and $130,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a sports AI software engineer, and why are they important?

To excel as a Sports AI Software Engineer, you need a strong background in computer science or a related field, proficiency in programming languages like Python or C++, and a solid understanding of machine learning algorithms. Experience with AI frameworks (such as TensorFlow or PyTorch), data analytics, and familiarity with sports data systems are typically required. Strong problem-solving, teamwork, and communication skills help you collaborate with cross-functional teams and translate complex data insights into actionable sports strategies. These abilities are vital for developing innovative AI-driven solutions that enhance performance analysis and decision-making in the sports industry.

What are some common challenges faced by professionals working in sports AI software development?

Professionals in Sports AI software often encounter challenges such as managing large and complex datasets, ensuring real-time data processing for live events, and balancing accuracy with computational efficiency. Additionally, integrating AI solutions with existing sports analytics platforms and collaborating with coaches or analysts who may not have technical backgrounds can require strong communication skills. Staying updated with the latest advancements in machine learning and sports technology is also crucial for continued success in this dynamic field.

What is the difference between Sports Ai Software vs Sports Data Analyst?

AspectSports Ai SoftwareSports Data Analyst
Required CredentialsTypically no formal degree, familiarity with AI toolsBachelor's degree in sports science, statistics, or related field
Work EnvironmentSoftware platforms, AI development environmentsOffice, sports teams, data analysis labs
Employer & Industry UsageSports tech companies, AI startups, sports analytics firmsSports teams, media outlets, analytics agencies
Common Search & ComparisonYesYes

Sports Ai Software focuses on developing and utilizing AI tools for sports analytics, often requiring technical skills in AI and software development. In contrast, Sports Data Analysts interpret data, generate reports, and provide insights using statistical tools. Both roles are integral to sports analytics but differ in technical complexity and daily tasks.

What is sports AI software?

Sports AI software refers to applications that use artificial intelligence to analyze sports data, improve athlete performance, and enhance coaching decisions. These tools can process large amounts of data from games, training sessions, and biometric sensors to provide insights on player statistics, injury risks, and tactical strategies. Teams, coaches, and analysts use Sports AI software to gain a competitive edge, make informed decisions, and optimize both individual and team performance. The software is widely used across various sports, from football and basketball to tennis and athletics, and continues to evolve with advancements in AI and machine learning.
More about Sports Ai Software jobs
What cities are hiring for Sports Ai Software jobs? Cities with the most Sports Ai Software job openings:
What states have the most Sports Ai Software jobs? States with the most job openings for Sports Ai Software jobs include:
What job categories do people searching Sports Ai Software jobs look for? The top searched job categories for Sports Ai Software jobs are:
Sports Ai Software jobs near you
Infographic showing various Sports Ai Software job openings in the United States as of August 2026, with employment types broken down into 1% Internship, 87% Full Time, 8% Part Time, and 4% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $111,845 per year, or $53.8 per hour.

Junior Software Engineer (AI-Forward)

Texas Sports Academy Main

Austin, TX

Full-time

Re-posted 3 days ago

Job description

As a Junior Software Engineer with Texas Sports Academy, you'll help build the software that runs our school, student records, academic mastery tracking, training data, parent portals, admissions, and the AI-powered tools our guides and coaches use every day. This is an early-career, AI-forward seat. You'll work directly with the founders and senior engineers, ship code every week with AI in your loop, and grow into the LLM-powered features that make our school feel nothing like a traditional school.

What You Will Be Doing
  • Building and shipping product features across the full stack every week, with AI coding tools running alongside you.
  • Contributing to real LLM-powered product features: tutoring agents, parent-facing copilots, coach-facing dashboards, retrieval over student data, and the evals behind them.
  • Working directly with the founders and senior engineers on scope and trade-offs, no PM layer in between.
  • Picking up ownership of smaller systems end-to-end and growing into bigger ones.
  • Running your own AI coding workflow, prompts, subagents, custom tools, MCP servers, and getting sharper at it every week.
  • Writing evals and regression tests for AI features the same way you'd write unit tests for classical code.
What You Will NOT Be Doing
  • Pretending AI is optional. If you're not already coding with Claude Code, Cursor, Codex, or an equivalent agent loop, this role is not for you.
  • Sitting in status meetings all day. Few meetings, more shipping.
  • Working on a narrow slice of a giant codebase.
  • Waiting around for tickets. You'll be in the room when we decide what to build.
Key Responsibilities
  • Ship production code and AI features that real students, parents, and staff rely on every day.
  • Own smaller systems and features end-to-end as you ramp up.
  • Move fast. Features go from idea to production in days, not quarters, without breaking things.
  • Level up your AI-engineering chops alongside a senior team.

Requirements

  • Bachelor's or master's degree in Computer Science, Engineering, Math, or Physics.
  • 0 to 2 years of full-time engineering experience. Strong internships, side projects, and shipped personal work count.
  • Daily, fluent use of AI coding tools (Claude Code, Cursor, Codex, Windsurf, Aider, or equivalent) as your default way of writing software.
  • Comfortable in a modern web stack (TypeScript / React / Node or Python / Postgres / AWS or GCP).
  • Excellent written English.
  • Based in Austin, TX.
Nice to Have
  • At least one shipped LLM-powered project, school project, hackathon, or side project, with some kind of eval story.
  • Agent frameworks (LangGraph, CrewAI, Mastra, custom), vector search / RAG, evals (Braintrust, LangSmith, custom), prompt caching, MCP servers, structured output / tool-use, or voice agents.
  • Public GitHub or a personal AI project we can actually try.
  • A personal project you built because you wanted to.
  • Background in education, edtech, or sports.

Apply