Sr ML Software Development Engineer, Admin Central

Description

The company is a Fortune 500 AI platform that helps organizations manage people, finances, and AI agents, with a focus on helping teams spend less time on routine work.

Admin Central is creating one unified experience for administrators, replacing a scattered set of tools and disconnected AI features. Implementation work is shifting from manual effort to intelligence built into the platform.

The team is building from scratch and moving quickly. Its systems serve thousands of administrators daily and over 700 customer accounts, and the work includes mission-critical distributed systems, high-availability usage pipelines, agentic features, and event-driven data flows across global product regions to keep enterprise billing accurate at scale.

This is a Senior Software Development Engineer (ML/AI) role on the Admin Central team. You will lead the technical design, development, and scaling of ML pipelines, predictive analytics engines, and AI recommendation features that sit at the center of the company's enterprise AI and automation strategy.

You will connect production distributed software engineering with modern ML and LLM architectures. The work includes low-latency inference services, feature stores and retraining pipelines built on big data infrastructure (Kafka, Spark/Flink, ClickHouse, S3), and observability across agent execution loops using LangSmith and LangChain.

Responsibilities

• Build, deploy, and maintain complete ML pipelines, from distributed data ingestion and feature engineering through training, evaluation, and production rollout.
• Optimize real-time model inference and scoring microservices so they respond in under a second under high concurrency in multi-tenant environments.
• Design algorithms and LLM-driven recommendation features, such as IDD insights and next-best-action models, that improve agentic workflows and tool use.
• Add telemetry and tracing with OpenTelemetry, LangChain, and LangSmith to track model performance, prompt and response drift, and execution efficiency.
• Work with core backend engineers on ClickHouse, Apache Iceberg, and Spark to support high-dimensional feature retrieval and analytics.
• Partner with Product Managers, Data Engineering, SRE, and Legal/Compliance to turn business and governance requirements into scalable ML solutions.

Requirements

• Basic qualifications: at least 8 years in ML research, development, and implementation of advanced models and techniques, including at least 4 years building, deploying, and running production ML systems at scale.
• Basic qualifications: at least 5 years in deep learning, NLP, and GenAI/LLM orchestration with PyTorch, TensorFlow, Scikit-learn, LangChain, or LlamaIndex, plus proficiency in Java, Go, Scala, or C++ for high-performance backend systems.
• Other qualifications: experience with distributed processing (Spark, Flink, Kafka) and high-throughput columnar or vector storage (ClickHouse, Pinecone, Milvus) to build reliable ML data pipelines.
• Other qualifications: working knowledge of RAG architectures, LLM fine-tuning, intent classification, and recommendation engines, and of applying ML algorithms and Model-Based Design, including MCP tooling and structured agentic execution paths, to multi-agent orchestration, contextual reasoning, and workflow optimization.
• Other qualifications: experience leading exploratory data analysis on multi-tenant execution logs to check prompt and response accuracy and improve tool use, along with feature engineering for vector embeddings, feature stores, and multi-tenant data isolation.
• Other qualifications: experience leading model work such as foundation model adaptation, prompt engineering, fine-tuning, and low-latency inference, and a solid grasp of the model lifecycle, including MLOps tools (Kubeflow, MLflow, Airflow, SageMaker) for monitoring, latency tracking, and retraining at scale.
• Other qualifications: strong Python skills with ML libraries (PyTorch, TensorFlow, NumPy, Pandas, Scikit-learn, LangChain, LangSmith, DSPy), and experience shipping scalable cloud-native systems with Docker, Kubernetes, gRPC or REST APIs, and CI/CD, meeting 99.9% uptime and low-latency SLAs.
• Other qualifications: ability to mentor teammates, align cross-functional technical decisions, write system design specs and model cards, and explain technical and governance choices clearly.
• Other qualifications: bachelor's degree in Computer Science, Data Science, Machine Learning, Applied Mathematics, Statistics, or Engineering. A master's or PhD is strongly preferred.

Conditions

Primary location is Pleasanton, California, USA. Base pay is $192,000 USD to $288,000 USD. Other US locations have a base pay range of $163,000 USD to $288,000 USD.

Pay varies by work location. Your offer depends on factors such as geography, experience, skills, job duties, and business need. The recruiter can share more details during hiring.

Flex work: you spend at least half (50%) of your time each quarter in the office or in the field with customers, prospects, and partners, depending on the role. There is no fixed number of office days, so you can shape your schedule around work and personal needs. Remote home office roles can also join in-office gatherings for key moments.

The company considers qualified applicants with arrest and conviction records, in line with applicable Fair Chance law. It is an Equal Opportunity Employer, including individuals with disabilities and protected veterans.

Benefits

• Eligibility for the company Bonus Plan or a role-specific commission or bonus, and annual refresh stock grants, as part of total compensation.
• Access to the company's comprehensive benefits program.