Lead the design, development, and maintenance of compute infrastructure and scheduling systems that run ServiceNow’s platform across private and public cloud, driving performance, capacity, and reliability.
Compensation
For positions in this location, we offer a base pay of $166,500 - $291,400, plus equity (when applicable), variable/incentive compensation and benefits.
Responsibilities
Write and maintain software-defined, declarative infrastructure for compute at scale, with build systems, services, and automation that are specified, documented, and testable.
Build and improve components of the compute scheduler and resource-management systems, including placement, prioritization, and preemption.
Build tools for capacity data collection, forecasting, and reporting across private and public cloud.
Write performance and load tests, and help detect regressions in scheduling latency and throughput.
Research and adopt open-source and commercial tools and methods that improve compute efficiency and reliability.
Requirements
Bachelor's degree in Computer Science or related field
Master's degree in Computer Science or related field
Design and evolve the compute scheduler and resource-management systems, including placement, prioritization, preemption, and fairness across workloads.
Compensation
For positions in this location, we offer a base pay of $190,900 - $334,100, plus equity (when applicable), variable/incentive compensation and benefits.
Responsibilities
Design and evolve the compute scheduler and resource-management systems, including placement, prioritization, preemption, and fairness across workloads.
Build capacity models and forecasts across private and public cloud, and turn them into reservation and placement decisions.
Establish performance and scale testing: benchmarks, load tests, regression detection, and SLOs for scheduling latency and throughput.
Apply AI and ML to forecasting, anomaly detection, and placement optimization where they outperform simpler approaches.
Write and maintain declarative, software-defined infrastructure at scale, with build systems, web services, and automation that are specified, documented, and testable.
Design and evolve the compute scheduler and resource‑management systems, including placement, prioritization, preemption, and fairness across workloads.
Compensation
For positions in this location, we offer a base pay of $221,200 - $387,100, plus equity (when applicable), variable/incentive compensation and benefits.
Responsibilities
Design and evolve the compute scheduler and resource‑management systems, including placement, prioritization, preemption, and fairness across workloads.
Build capacity models and forecasts across private and public cloud, and turn them into reservation and placement decisions.
Establish performance and scale testing: benchmarks, load tests, regression detection, and SLOs for scheduling latency and throughput.
Apply AI and ML to forecasting, anomaly detection, and placement optimization where they outperform simpler approaches.
Write and maintain declarative, software‑defined infrastructure at scale, with build systems, web services, and automation that are specified, documented, and testable.
Building the runtime infrastructure that powers Moveworks' AI agents—the systems that orchestrate, execute, and deliver agent responses to millions of enterprise users in real time, sitting where large language models meet distributed systems.
Compensation
For positions in this location, we offer a base pay of $161,300 - $274,200, plus equity (when applicable), variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies and work location. Compensation is based on the geographic location in which the role is located and is subject to change based on work location.
Responsibilities
Agent orchestration engine — A state machine that manages long-running agent sessions, coordinating planning, execution, and user interaction across multiple LLM calls and tool invocations
Model routing and adaptation — Choose, route between, and fine-tune or distill LLMs for specific agent tasks.
Distributed session management — Lease-based ownership using conditional writes, heartbeat protocols, and crash recovery via checkpointing
Event-driven message pipeline — FIFO queues for ordered delivery, consumers for event processing, and real-time streaming via gRPC and Socket.IO
Structured concurrency — Running multiple concurrent tasks per session with fail-fast semantics and graceful cancellation
IN: Hiring Range in INR from: 1,832,007 to 3,140,585 per annum.
Responsibilities
Implements and contributes to the development for components of distributed systems that support horizontal and vertical scaling including leveraging distributed state management tools
Optimizes code and/or systems for large-scale data processing in large-scale systems
Implements scalability requirements for assigned components and reviews implementation of team members
Leverages components of data plane platforms to handle large-scale data retrieval, storage, and processing
Contribute to computational biology research in a new lab, designing and conducting studies, developing machine learning models, and disseminating findings.
Compensation
The starting salary range (or hiring range) for this position has been established as: Entry Level: $53,631 to $68,219 Intermediate Level: $58,363 to $74,238
Responsibilities
Design, implement, tune, and evaluate machine learning models
Develop scripts to wrangle complex biomedical data
Integrate data from multiple sources to accelerate discoveries
Assist with the design and development of machine learning projects
Present research-in-progress at lab meetings, project meetings, and 1-on-1 meetings
Requirements
Bachelor’s degree in Bioinformatics, Computational Biology, Data Science, Computer Science, Statistics, or a related field
Master’s degree in Bioinformatics, Computational Biology, Data Science, Computer Science, Statistics, or a related field
The AI Developer Engineer is responsible for developing new AI applications and software including agentic AI applications, analytical solutions and pilot new ideas for deployment.
Compensation
Projected Salary Range: $121,000 to $151,000
20% Bonus opportunity + other incentives
Responsibilities
Design, build, and deploy production AI applications on Google Cloud Platform, applying machine learning and generative AI techniques to deliver solutions across merchandising, supply chain, store operations, finance, and customer experience.
Develop agentic AI applications using modern agent development kits and frameworks (e.g., Google Agent Development Kit, Gemini Agent Platform, LangGraph), including multi-agent workflows, tool integration, and human-in-the-loop checkpoints.
Build and operate retrieval-augmented generation (RAG) solutions that ground LLMs in Family Dollar's enterprise data, including ingestion, embeddings, vector and hybrid search, reranking, and evaluation.
Establish and maintain MLOps pipelines for AI workloads, including CI/CD, model and prompt versioning, evaluation harnesses, monitoring, cost controls, and rollback procedures.
Implement enterprise-grade guardrails for AI applications, including prompt injection defense, PII handling, content safety, role-based access controls, and audit logging - partnering closely with platform, security, infrastructure, and business stakeholders to ensure deployments meet Family Dollar's standards for identity, networking, data residency, and compliance.
Lead strategy, architecture, delivery, and operation of LPL's core data platforms, modernize books‑and‑records capabilities, and build an AI‑native organization focused on trusted, real‑time, agent‑ready data.
Compensation
Pay Range: $207,648.00 - $346,080.00
Responsibilities
Own the strategy, architecture, and delivery of real‑time, event‑driven books‑and‑records platforms
Lead AI‑driven ingestion, normalization, entity resolution, anomaly detection, and quality controls
Modernize security master and pricing capabilities
Build and govern semantic data products
Expose core data through secure, governed APIs and Model Context Protocol services
Strong hands-on expertise in Python and end-to-end AI/ML model development, including data preparation, feature engineering, training, evaluation, and production integration.
Compensation
Rate: $60/hr
Responsibilities
Translate business requirements into scalable AI/ML solutions
Collaborate with architects, data engineers, application teams, QA, and business stakeholder
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