Distinguished Software Engineer, Recommendation Systems (AI/ML)
Confirmed on the employer's own hiring board on Oct 25, 2026. First seen by Alion on Oct 23, 2026. Meta scores B on the Alion truth index.
Responsibilities
The role: Top applied AI/ML expert in a Recommendation Product Work on the hardest applied AI problem in the product Set a 2-3 year north-star architecture for the product Apply AI-native workflows and tooling as a force multiplier to accelerate AI/ML advancement in the product and expand the scope of what engineering teams can deliver Define new metrics and data-driven decision-making principles for long-term ML projects, connecting operational metrics to organization-level business outcomes Proactively identify systemic reliability, privacy, and integrity risks in ML systems and build robust technical safeguards, partnering with compliance and policy teams to ensure responsible AI deployment Mentor engineers across the organization on applied AI/ML, debugging complex product problems, and building production-grade AI systems, establishing yourself as a sought-after technical coach and technical leader
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience 8+ years of experience in applied AI/ML with a track record of delivering measurable product outcomes Experience defining technical strategy and gaining organizational alignment across multiple engineering teams and cross-functional stakeholders Experience communicating complex ML system designs and trade-offs in writing to both technical and non-technical audiences, including executive leadership Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Experience building AI-native developer tooling or automation that measurably accelerates ML experimentation and production improvement cycles Experience applying ML to multiple product domains such as ranking and recommendation, generative AI, computer vision, or natural language processing Track record of industry-recognized contributions to machine learning applications, such as publications and open-source projects Demonstrated ongoing AI skill development (e.g., prompt/context engineering, harness engineering, agent orchestration) and staying current with emerging AI technologies