Lead ML Engineer
Versant
Company Description VERSANT (Nasdaq: VSNT) is an industry-changing media and entertainment business and home to trusted brands that shape culture, inform audiences, and build lasting connections. It operates across four core markets: political news and opinion, business news and personal finance, golf, and sports and genre entertainment. These markets are served through a powerful portfolio of iconic and innovative brands, including CNBC, MS NOW, USA Network, Golf Channel, Oxygen, E!, SYFY, and Versant's sports division USA Sports, along with complementary digital assets including Fandango, Rotten Tomatoes, GolfNow and GolfPass. Job Description We are seeking a Lead ML / AI Engineer to design and lead end-to-end ML/AI systems spanning computer vision, large language models, and retrieval-based architectures. You will build production-grade intelligence that powers products, operations, and newsroom workflows-while creating foundational AI capabilities that scale across the VERSANT portfolio.
This role combines hands-on architecture with platform vision. You will guide model selection, experimentation, and integration into applications; establish rigorous standards for deployment and monitoring; and partner with Product, Engineering, Data, Executive Leadership, and Editorial teams to deliver high-impact systems. You will also mentor engineers and maintain a high bar for technical excellence across the ML lifecycle.
Responsibilities
This role combines hands-on architecture with platform vision. You will guide model selection, experimentation, and integration into applications; establish rigorous standards for deployment and monitoring; and partner with Product, Engineering, Data, Executive Leadership, and Editorial teams to deliver high-impact systems. You will also mentor engineers and maintain a high bar for technical excellence across the ML lifecycle.
Responsibilities
- Set the technical vision for ML/AI at VERSANT, translating business and newsroom priorities into a focused, high-impact execution roadmap.
- Architect and implement end-to-end ML systems (data → training → evaluation → deployment → monitoring) across vision, language, and retrieval domains, making high-leverage model and systems decisions that balance performance, latency, cost, and long-term maintainability at scale.
- Lead model evaluation and monitoring strategy: define rigorous offline and online evaluation frameworks, establish performance benchmarks, and implement feedback loops that continuously improve model quality, safety, and real-world impact.
- Develop deep expertise in the AI vendor landscape (foundation models, tooling, infrastructure providers), rigorously assess strengths, weaknesses, cost structures, and risk profiles, and guide teams in selecting the right solutions for their specific use cases.
- Identify cross-brand leverage points and develop reusable AI primitives, APIs, and shared services that reduce duplication and scale efficiently across the VERSANT portfolio.
- Partner directly with Editorial and product leaders to embed AI into newsroom workflows, content intelligence, and audience experiences.
- Partner closely with software engineering teams to ensure AI-powered applications are delivered with reliability, resiliency, and scalability as first-order requirements.
- 8-12+ years of experience in machine learning, applied AI, and distributed systems, including significant experience operating models in production.
- Proven track record of architecting and scaling end-to-end ML systems in high-traffic, production environments.
- Deep expertise in Python and modern ML frameworks with strong software engineering fundamentals.
- Demonstrated experience designing evaluation frameworks, monitoring strategies, and feedback loops for continuously improving model quality and safety.
- Strong experience with large language models, retrieval systems, and generative AI, including real-world integration into applications.
- Experience building or contributing to shared ML platforms, internal AI tooling, or reusable services used across multiple teams or business units.
- Strong understanding of distributed systems, data engineering, cloud infrastructure (AWS, GCP, or Azure), and cost/performance trade-offs at scale.
- Experience partnering cross-functionally with product, engineering, and non-technical stakeholders; ability to influence senior leadership and translate ambiguity into execution.
- Experience in media, content, recommendation, search, or newsroom-adjacent systems is a strong plus.
Vacancy posted 7 hours ago
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