Software Engineer, Machine Learning
Confirmed on the employer's own hiring board on Oct 29, 2026. First seen by Alion on Oct 19, 2026. Meta scores B on the Alion truth index.
Responsibilities
Define and own the technical architecture of critical machine learning systems, including model training pipelines, inference infrastructure, and feature engineering platforms, ensuring reliability and scalability across billions of users Identify and solve the most complex ML systems challenges across multiple product areas, including issues that span model quality, training efficiency, serving latency, and data integrity Develop and establish extensible ML frameworks, modeling standards, and engineering practices that drive consistency and velocity across multiple engineering organizations Lead cross-functional technical strategy for machine learning initiatives, aligning research, infrastructure, and product teams around multi-year roadmaps that balance short-term delivery with long-term architectural health Apply AI-native workflows and tooling as a force multiplier to accelerate model development cycles, automate evaluation pipelines, 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 model performance signals 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 ML systems design, debugging complex model behavior, and building production-grade AI systems, establishing yourself as a sought-after technical coach and technical leader Drive performance improvements across large-scale ML systems by identifying bottlenecks that span training, data loading, model serving, and hardware utilization, and leading cross-org efforts to resolve them Influence the broader ML engineering community through technical publications, design frameworks, and cross-industry engagement that advances the field
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 12+ years of experience designing, building, and deploying large-scale machine learning systems in production environments Experience architecting end-to-end ML platforms spanning data pipelines, distributed training, model evaluation, and low-latency inference serving Experience identifying and resolving complex, cross-system ML failures including issues in model quality, training stability, feature consistency, and serving correctness 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 Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience building AI-native developer tooling or automation that measurably accelerates ML experimentation and production deployment cycles Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Track record of industry-recognized contributions to machine learning systems, such as publications, open-source frameworks, or widely adopted architectural patterns Experience applying ML to multiple product domains such as ranking and recommendation, generative AI, computer vision, or natural language understanding Experience with large-scale foundation model training, fine-tuning, or inference optimization across distributed hardware clusters Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)