Statistical Engineering Jobs in Georgia (NOW HIRING)

PlayOn Sports processes over 250,000 live high school sports games a year across NFHS Network, GoFan, and MaxPreps. We’re using computer vision and AI to turn those streams into real-time scores, player-level statistics, automated highlights, and coaching tools. This is one of PlayOn’s highest-priority investment areas, and we’re looking for a Director of Engineering to lead the team building it.

As Director of Engineering for Streaming Intelligence, you’ll own the engineering organization responsible for our computer vision stats pipeline, AI-powered fan experiences, and the broader application of AI across PlayOn’s product portfolio. You’ll partner directly with our Principal Product Manager to form the leadership duo for this product line. This is a people management role. You’ll build and lead a team of senior and staff engineers, set technical direction, and be accountable for delivery against the program’s milestones.

The right candidate has led engineering teams through ambiguous, high-stakes programs before. You’ve shipped AI-powered products in production, you know how to build and retain strong senior talent, and you’re comfortable operating at the intersection of engineering, product, and business strategy. You don’t just manage engineers. You set the technical bar, make architecture calls when it matters, and create the conditions for your team to do their best work.

• Streaming intelligence in production: Drive the computer vision stats program from its current POC phase into production-scale operation across five major sports. You own the delivery timeline, the technical quality bar, and the team’s ability to execute. Target: production-scale within six months.

• Team build-out: Recruit, hire, and onboard the engineering talent needed to execute the streaming intelligence roadmap and expand AI capabilities across PlayOn. You’ll inherit a strong foundation of senior engineers and are expected to grow and shape the team to match the program’s ambition.

• AI enablement across PlayOn: Extend AI capabilities beyond streaming intelligence into adjacent product areas. Work with engineering leaders across the company to establish reusable AI development patterns (model integration, evaluation frameworks, agentic workflows) that other teams can adopt. Target: at least one cross-cutting AI initiative shipped within twelve months.

• Product-engineering partnership: Build a high-functioning working relationship with the Principal Product Manager. Establish the operating rhythm, planning cadence, and decision-making framework that keeps the product line healthy as it scales.

• Vendor and partner management: Co-own the technical relationship with AI and computer vision vendors. Make build-vs-buy-vs-partner recommendations grounded in engineering reality, and hold partners accountable for delivery.

• Lead and grow a team of senior and staff engineers working on computer vision, AI-powered fan experiences, human-in-the-loop systems, and data infrastructure. Set expectations, remove blockers, and create a culture of ownership and shipping.

• Own the technical direction for PlayOn’s streaming intelligence program. Make architecture decisions, set engineering standards, and ensure the team is building systems that scale to 250K+ concurrent live streams.

• Partner with the Principal Product Manager to translate product strategy into engineering execution. Co-own the roadmap, drive sprint and quarterly planning, and jointly manage scope, sequencing, and tradeoffs.

• Drive AI adoption and enablement across PlayOn’s broader engineering organization. Identify opportunities to apply AI in search, recommendations, content generation, internal tooling, and other product areas.

• Manage external vendor and academic partner relationships on the engineering side. Evaluate capabilities, run technical assessments, negotiate integration approaches, and hold partners to their commitments.

• Represent engineering in cross-functional leadership forums. Communicate program status, risks, and resource needs to the CTO and broader leadership team with clarity and appropriate context.

• Recruit and retain top engineering talent. Build a hiring pipeline, run a rigorous interview process, and create a team environment where strong engineers want to stay and grow.

• Establish and maintain engineering processes that match the team’s maturity: design reviews, RFC processes, on-call rotations, incident management, and production readiness standards.

• 8+ years of software engineering experience with at least 3 years in engineering management, leading teams of senior and staff-level engineers.

• Experience shipping AI/ML-powered products in production. You understand the full lifecycle: model integration, evaluation, deployment, monitoring, and the human workflows that surround them.

• A track record of building and retaining high-performing engineering teams. You’ve hired senior talent, managed performance, and created team cultures that attract strong engineers.

• Strong technical foundation in Python, cloud-native architectures (AWS), and modern data infrastructure (Snowflake, Kafka, or similar). You’re not writing code daily in this role, but you can go deep when it matters and your team respects your technical judgment.

• Experience partnering closely with product management to co-own a product line. You know how to run the planning, execution, and delivery cadence for a complex, multi-workstream program.

• Comfort with ambiguity and early-stage programs. You’ve built structure and momentum where none existed before, and you know how to make progress without waiting for everything to be defined.

• Strong communication skills. You can represent engineering to the CTO and executive team, translate technical complexity for business stakeholders, and create transparency around status, risks, and tradeoffs.

• Bonus: experience with computer vision or video processing at scale, sports domain knowledge, vendor/partner management, or a background in media and streaming.

Ownership over Participation – You take responsibility for program-level outcomes. Streaming intelligence in production within six months, AI capabilities extending across PlayOn within twelve. You own the result, not just the effort.

Team over Stars – You build teams that are greater than the sum of their parts. You hire well, develop your people, and create the conditions for senior engineers to do career-defining work.

Growth over Comfort – You embrace the ambiguity of building AI capabilities in a domain where no established playbook exists. You drive the organization forward through experimentation, honest assessment, and a willingness to change direction when the evidence calls for it.

Fairness over Popularity – You evaluate tools, vendors, and approaches objectively. You make build-vs-buy-vs-partner recommendations grounded in evidence, and you give your team honest feedback even when it’s hard.