Senior Principal Al Engineer

Namely; Mountain View (CA); On-site; USD 130,000 - 170,000; Full time

Job summary

Namely is looking for a Distinguished Software Development Engineer to drive AI and machine learning solutions in our Galaxy ecosystem. This role involves leading the development of sophisticated AI features and delivering enterprise-grade reliability and security.

The ideal candidate will possess a PhD or Master's, 6+ years in software engineering, and deep expertise in AI/ML model development across cloud platforms.

Qualifications

• 6+ years in software engineering with minimum of 2+ years in AI/ML applications.
• Deep expertise in NLP, Deep Learning, LLMs, and Generative AI.
• Strong foundation in system architecture and scalable data engineering.

Responsibilities

• Lead development of AI-driven features using Agile practices.
• Architect and scale distributed AI systems.
• Design and tune production AI/ML models.

Skills

AI/ML model development

Cloud platforms (AWS, Azure, GCP)

Modern programming languages (Python, Java)

Distributed systems (Kubernetes, Docker)

Education

Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Machine Learning

Tools

TensorFlow

PyTorch

Scikit-Learn

Keras

Job description

Cornerstone powers the future‑ready workforce with AI‑driven employment solutions. Our platform enables companies to develop, manage, and engage their talent—unlocking growth and innovation across organizations of all sizes.

Who We’re Looking For

We are seeking a visionary and highly accomplished Distinguished Software Development Engineer to spearhead the creation of groundbreaking AI and machine learning solutions across our industry‑leading workforce agility - Galaxy ecosystem.

Responsibilities

• Full‑Stack AI Engineering: Lead the hands‑on development, deployment, and continuous improvement of sophisticated AI‑driven features, leveraging Agile practices and top‑tier coding standards.
• Advanced Architecture System Design: Architect, implement, and scale modern, distributed AI systems—including training pipelines, streaming data processing, serverless microservices, and MLOps infrastructure—to deliver enterprise‑grade reliability and security.
• ML Model Innovation: Expertly design, build, and tune production AI/ML models (NLP, Deep Learning, Recommender Systems, LLMs, Generative AI) using cutting‑edge frameworks (TensorFlow, PyTorch, Hugging Face, Keras, Scikit‑Learn, Ray).
• Cloud Data Engineering Mastery: Develop and optimize cloud‑native (AWS, GCP, Azure) AI workloads—utilizing Kubernetes, Docker, Spark, and high‑performance data lakes for advanced data wrangling, batch and real‑time inference, and model monitoring.
• Agentic Generative AI Technologies: Design and deploy intelligent, autonomous AI agents (LLMs, multi‑agent systems) capable of planning, reasoning, and decision‑making—solving complex HR and talent management challenges with next‑gen AI.
• Orchestration Tooling: Build frameworks for multi‑agent orchestration, message passing, prompt engineering, vector databases (FAISS, Pinecone), and scalable knowledge graphs to enable robust agent collaboration and negotiation.
• Task Automation Workflow AI: Develop specialized AI agents for process automation—streamlining content generation, personalized recommendations, and end‑to‑end workflow optimization using RPA and conversational AI.
• Safety, Reliability, Explainability: Set gold standards for AI safety, fairness, and explainability—implementing evaluation protocols, guardrails, and bias detection to ensure ethical agent behavior in real‑world deployments.
• Seamless Systems Integration: Fuse agentic and generative AI systems with modern APIs, REST/gRPC, user interfaces (React, Angular), microservices, and enterprise data sources for resilient, scalable solutions.
• Performance Tuning MLOps: Apply best‑in‑class techniques for model performance, hyperparameter optimization, scalable retraining, monitoring, and CI/CD for AI pipelines.
• Research, Innovation Thought Leadership: Stay at the cutting edge with constant exploration of new AI technologies—transforming foundational research into impactful product features.
• Standards Advocacy: Champion software engineering excellence—driving best practices in secure coding, peer review, and responsible AI design throughout the full SDLC.

Qualifications

• Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Machine Learning, or related field.
• 6+ years in software engineering with a minimum of 2+ years hands‑on building, deploying, and optimizing AI/ML applications at enterprise scale.
• Deep expertise in AI/ML model development (NLP, Deep Learning, LLMs, Recommender Systems, Generative AI) and their deployment in cloud production environments.
• Advanced hands‑on proficiency with cloud platforms (AWS, Azure, GCP), ML frameworks (TensorFlow, PyTorch, Hugging Face, Scikit‑Learn), modern programming languages (Python, Java, Scala, C++), and distributed systems (Kubernetes, Docker, Spark).
• Strong foundation in system architecture, algorithm design, scalable data engineering (ETL, batch stream processing), and model serving.
• Experience with modern MLOps, CI/CD, GitOps, and DevSecOps methodologies.
• Commitment to ethical, responsible AI—deep understanding of privacy, explainability, bias, and regulatory considerations.
• Prior experience in HR tech, SaaS, or enterprise software highly advantageous.

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