Heath Black

Heath Black is vice president of applied AI at Arccos Golf, where he develops products that turn golfers’ performance data into personalized guidance. His career encompasses conversational commerce, Reddit’s trust-and-safety systems, Meta’s consumer assistants and smart glasses, and SignalFire’s Beacon AI platform for venture investing and technical recruiting.

After graduate study at DePaul University concentrating on modern Irish literature, Black began working with startups around 2009. At Chirpify, he helped develop conversational products and built a configurable campaign system that allowed brands to launch landing pages without custom engineering. He subsequently joined Imzy, a community-focused Reddit alternative, then Reddit itself, working on experimental business lines, trust-and-safety tools, and Upvoted Weekly, a curated digest attentive to attribution, privacy, and security.

At Meta, Black worked on M, the assistant inside Messenger, and became the first product manager for the AI assistant on Ray-Ban smart glasses. He joined SignalFire in 2021 and became managing director of product, helping develop Beacon’s machine-learning and language-model capabilities. His work improved its data quality, technical infrastructure, proprietary models, and tools supporting investment research and startup recruiting. He left after approximately four and a half years before joining Arccos.

At Arccos, Black helped deliver a substantial mobile-app redesign incorporating AI-generated round summaries, personalized practice recommendations, course-specific strategy, club comparisons, and warm-up plans. These performance-driven coaching features translate accumulated shot data into specific advice about what golfers should practice or change.

• Skills-based AI hiring: Black evaluates candidates through production engineering, open-source contributions, applied machine learning, and MLOps experience. He argues that employers should distinguish roles requiring specialized research expertise from product-oriented positions where demonstrated engineering ability matters more than prestigious degrees.

• Historical team composition: Recruiting should account for when someone joined a successful company, which problems they solved, and whether that experience matches another startup’s stage. Employee movement, retention, geography, and funding patterns further sharpen candidate selection and outreach timing.

• Cost-aware language-model deployment: Beacon’s applications included normalizing inconsistent job titles, classifying companies, summarizing research, and improving recruiting searches. Black emphasizes selective processing, dependable underlying data, and privacy tradeoffs between external models and proprietary systems.

• Recruiting as a career narrative: Compensation alone rarely explains why a talented engineer should join. Black connects mission, founder access, difficult technical problems, collaboration, and professional growth to the trajectory candidates want for their careers, an approach developed in his AI Engineer Summit session on building technical teams.

Golf also gives his current work personal significance: in an essay about his relationship with the game, Black describes its role in family connection, friendship, and community.