Staff Netsuite Engineer jobs at Mongodb in United States, Palo Alto

System Architecture & Development

• Lead the design, configuration, and customization of NetSuite to support R2R and accounting processes (GL, journals, allocations, consolidations, fixed assets, intercompany eliminations)
• Architect scalable NetSuite solutions that integrate seamlessly with upstream and downstream systems (e.g., procurement, billing, expense management, data lake)
• Develop and maintain SuiteScript, SuiteFlow, SuiteTalk (Web Services), and RESTlets to support automation and integrations

Process & Accounting Alignment

• Partner with Finance, Accounting, and Audit teams to translate accounting policies into system-enforced controls within NetSuite
• Act as the primary point of contact for Finance teams including Accounting, Tax, Treasury and RevOps
• Ensure compliance with SOX, GAAP, and audit requirements through configuration of role-based security, approvals, reconciliations, and audit trails
• Optimize financial close processes (journals, reconciliations, accruals, consolidations) to reduce close cycle time
• Build and enhance reporting frameworks (saved searches, financial reports, dashboards) to support accurate and timely financial reporting
• Support compliance and audit requirements through appropriate controls and documentation

Technical Leadership

• Serve as the subject matter expert (SME) for NetSuite R2R and OTC processes, guiding engineers, administrators, and analysts
• Serve as the primary technical advisor for business stakeholders
• Translate business requirements into scalable technical solutions with clear documentation
• Establish engineering best practices in NetSuite development, including coding standards, testing, version control, and deployment
• Drive system performance, scalability, and reliability improvements
• Mentor junior engineers and provide architectural oversight for NetSuite-related initiatives

Integration & Data Management

• Oversee integrations with external systems (e.g., payroll, banking, consolidation tools, CRM platforms like Salesforce, Coupa, Concur)
• Partner with Data Engineering teams to ensure accurate data flow into finance data lake/warehouse for analytics and compliance reporting
• Support data governance, master data management (chart of accounts, dimensions, entity hierarchy) in alignment with accounting standards

Required

• 10+ years of NetSuite development/engineering experience with a focus on General Ledger and accounting modules
• Strong knowledge of OTC and R2R processes including journal entries, close cycle, reconciliations, consolidations, and intercompany accounting
• Proficiency in SuiteScript 1.0/2.0, SuiteFlow, SuiteTalk, REST APIs, and NetSuite integration patterns
• Demonstrated experience implementing system-enforced financial controls and supporting SOX compliance
• Solid understanding of US GAAP and accounting workflows
• Experience with role-based security design, segregation of duties, and audit requirements

Preferred

• Experience with global accounting operations (multi-entity, multi-currency, consolidations)
• Bachelor’s degree in Computer Science, Information Systems, Finance, or related field (or equivalent experience)
• Familiarity with modern finance data stacks and BI/reporting tools
• Prior experience in a high-growth, SaaS or technology company
• Exposure to AI/automation in Finance (e.g., reconciliations, anomaly detection)

Soft Skills

• Strong problem-solving and cross-functional collaboration abilities
• Excellent communication skills, able to translate technical details into business terms
• Leadership mindset with the ability to mentor and influence stakeholders
• Detail-oriented with a commitment to accuracy and compliance

• Impact at Scale: Play a strategic role in modernizing Finance Technology for a growing global organization
• Innovation: Work at the intersection of Finance, Technology, and Compliance to deliver scalable impact
• Cross-Functional Collaboration: Partner with senior leaders in Finance, Procurement, and Technology to deliver systems that enable smarter decisions
• Opportunity to lead technical strategy and influence how core accounting processes are automated and optimized

Req: 3263255986

As a Lead Engineer, Inference Platform, you’ll be hands-on with design and implementation, while working with engineers across experience levels to build a robust, scalable system. The focus is on latency, availability, observability, and scalability in a multi-tenant, cloud-native environment. You will also be responsible for guiding the technical direction of the team, mentoring junior engineers, and ensuring the delivery of high-quality, impactful features.

• Partner with Search Platform and Voyage.ai AI engineers and researchers to productionize state-of-the-art embedding models and rerankers, supporting both batch and real-time inference
• Lead key projects around performance optimization, GPU utilization, autoscaling, and observability for the inference platform
• Design and build components of a multi-tenant inference service that integrates with Atlas Vector Search, driving capabilities for semantic search and hybrid retrieval
• Contribute to platform features like model versioning, safe deployment pipelines, latency-aware routing, and model health monitoring
• Collaborate with peers across ML, infra, and product teams to define architectural patterns and operational practices that support high availability and low latency at scale
• Guide decisions on model serving architecture using tools like vLLM, ONNX Runtime, and container orchestration in Kubernetes
• Provide technical leadership and mentorship to junior engineers, fostering a culture of technical excellence and continuous improvement within the team

• 8+ years of engineering experience in backend systems, ML infrastructure, or scalable platform development, and the ability to provide technical leadership and guidance to a team of engineers
• Expertise in serving embedding models in production environments
• Strong systems skills in languages like Go, Rust, C++, or Python, and experience profiling and optimizing performance
• Comfortable working on cloud-native distributed systems, with a focus on latency, availability, and observability
• Familiarity with inference runtimes and vector search systems (e.g., Faiss, HNSW, ScaNN)
• Proven ability to collaborate across disciplines and experience levels, from ML researchers to junior engineers
• Experience with high-scale SaaS infrastructure, particularly in multi-tenant environments
• 1+ years of experience serving as TL for a large-scale ML inference or training platform SW project

• Prior experience working with model teams on inference-optimized architectures
• Background in hybrid retrieval, prompt-based pipelines, or retrieval-augmented generation (RAG)
• Contributions to relevant open-source ML serving infrastructure
• 1+ years of experience in managing a technical team focused on ML inference or training infrastructure

• Be part of shaping the future of AI-native developer experiences on the world’s most popular developer data platform
• Collaborate with ML experts from Voyage.ai to bring cutting-edge research into production at scale
• Solve hard problems in real-time inference, model serving, and semantic retrieval — in a system used by thousands of customers worldwide
• Work in a culture that values mentorship, autonomy, and strong technical craft
• Competitive compensation, equity, and career growth in a hands-on technical leadership role

You’ll join the broader Search and AI Platform organization and collaborate with ML researchers and engineers from our Voyage.ai acquisition. Together, we’re building infrastructure for real-time, low-latency, and high-scale inference — fully integrated with Atlas and designed for developer-first experiences.

As a Senior Engineer, you'll focus on building core systems and services that power model inference at scale. You'll own key components of the infrastructure, work across teams to ensure tight integration with Atlas, and contribute to a platform designed for reliability, performance, and ease of use.

• Design and build components of a multi-tenant inference platform integrated directly with MongoDB Atlas, supporting semantic search and hybrid retrieval
• Collaborate with AI engineers and researchers to productionize inference for embedding models and rerankers — enabling both batch and real-time use cases
• Contribute to platform capabilities such as latency-aware routing, model versioning, health monitoring, and observability
• Improve performance, autoscaling, GPU utilization, and resource efficiency in a cloud-native environment
• Work across product, infrastructure, and ML teams to ensure the inference platform meets the scale, reliability, and latency demands of Atlas users
• Gain hands-on experience with tools like vLLM and container orchestration with Kubernetes

• 5+ years of experience building backend or infrastructure systems at scale
• Strong software engineering skills in languages such as Go, Rust, Python, or C++, with an emphasis on performance and reliability
• Experienced in cloud-native architectures, distributed systems, and multi-tenant service design
• Familiar with concepts in ML model serving and inference runtimes, even if not directly deploying models
• Knowledge of vector search systems (e.g., Faiss, HNSW, ScaNN) is a plus
• Comfortable working across functional teams, including ML researchers, backend engineers, and platform teams
• Motivated to work on systems integrated into MongoDB Atlas and used by thousands of developers

• Experience integrating infrastructure with production ML workloads
• Understanding of hybrid retrieval, prompt-driven systems, or retrieval-augmented generation (RAG)
• Contributions to open-source infrastructure for ML serving or search

• Be part of building the AI foundation of the world’s most popular developer data platform
• Collaborate with ML researchers from Voyage.ai to bring novel ideas into scalable systems
• Tackle challenging problems in inference, observability, and distributed infrastructure
• Work in a culture that emphasizes mentorship, ownership, and technical excellence

This role can be based out of our Palo Alto office, or remotely in the United States.

• Conduct cutting-edge research in artificial intelligence, from frontier LLMs to embedding models and rerankers
• Innovate in next-generation information retrieval and LLM agent paradigm
• Collaborate closely with other research scientists and research engineers as well as peers across the organization

• PhD degree in Computer Science or related field
• A track record of coming up with new ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications in top venues
• Strong background in machine learning, deep learning, and natural language processing
• Experience building complex neural networks for language and visual understanding
• Capable of conducting rigorous empirical studies to validate theoretical results
• Excellent leadership, problem-solving, and communication skills

• Opportunity to work on real-world problems at the cutting edge of AI research
• Opportunity to utilize research vision to innovate the entire company and make real-world impact
• Exposure to the full lifecycle of AI model development, from research to production
• Our compensation (base + equity) for this position is competitive with frontier AI labs

In this role, you’ll have the opportunity to investigate some of the most challenging defects, collaborating with software engineers across the organization as well as senior technical services engineers. In addition, you’ll continually improve our diagnostic processes by contributing to applications and tools that support this mission.

This role can be based out of our San Francisco office or remotely in the western United States.

Please note: Candidates must be a US Person (i.e. U.S. citizen, U.S. national, lawful permanent resident, asylee, or refugee) to fulfill all work responsibilities.

• Work with the Engineering and Technical Services teams to debug and reproduce bug reports from MongoDB users and escalate problems as needed
• Maintain a user-oriented perspective, advise on possible solutions and help MongoDB users understand complex technical issues and options to mitigate or resolve
• Write code to expand our diagnostic toolset, including contributing to electron apps that were developed by our team to investigate diagnostic data

• 3+ years of experience in software development, with a focus on data management systems
• Experience supporting production environments, and/or working directly with end-users to investigate and diagnose technical issues
• Excellent communication skills (both written and verbal) are critical as you will be working with users from all over the world with very diverse backgrounds, as well as with a highly technical engineering team
• Exposure and interest in thorny technical issues central to databases: distributed systems, consensus algorithms, data replication, query optimization, data storage, OS internals, concurrency and scheduling, networking, etc
• Ability to:

• • Quickly grok and clearly synthesize implications of system behavior
  • Contribute to fullstack projects, preferably in Python, Typescript, or Go
  • Read and understand the intent of code and stack traces in many languages, especially C++

• You are comfortable handling tickets with identifiable diagnostic signatures
• You can identify user reported tickets that don’t describe bugs
• You have made contributions to our diagnostic tools

• You are identifying issue patterns and escalating when appropriate
• You can take any issue and work it to completion, either by working it yourself or knowing who and when to pull in additional help
• You have made significant contributions to our diagnostic tools

• You are running a project to improve our diagnostic tools
• You can answer some escalations from our experienced Technical Services Team
• You have a deep understanding in one or more Server components

As a Staff Engineer, you’ll be hands-on with design and implementation, while working with engineers across experience levels to build a robust, scalable system. The focus is on latency, availability, observability, and scalability in a multi-tenant, cloud-native environment.

• Partner with Search Platform and Voyage.ai AI engineers and researchers to productionize state-of-the-art embedding models and rerankers, supporting both batch and real-time inference.
• Lead key projects around performance optimization, GPU utilization, autoscaling, and observability for the inference platform
• Design and build components of a multi-tenant inference service that integrates with Atlas Vector Search, driving capabilities for semantic search and hybrid retrieval
• Contribute to platform features like model versioning, safe deployment pipelines, latency-aware routing, and model health monitoring
• Collaborate with peers across ML, infra, and product teams to define architectural patterns and operational practices that support high availability and low latency at scale
• Guide decisions on model serving architecture using tools like vLLM, ONNX Runtime, and container orchestration in Kubernetes

• 8+ years of engineering experience in backend systems, ML infrastructure, or scalable platform development
• Expertise in serving embedding models in production environments
• Strong systems skills in languages like Go, Rust, C++, or Python, and experience profiling and optimizing performance
• Comfortable working on cloud-native distributed systems, with a focus on latency, availability, and observability
• Familiarity with inference runtimes and vector search systems (e.g., Faiss, HNSW, ScaNN)
• Proven ability to collaborate across disciplines and experience levels, from ML researchers to junior engineers
• Experience with high-scale SaaS infrastructure, particularly in multi-tenant environments

• Prior experience working with model teams on inference-optimized architectures
• Background in hybrid retrieval, prompt-based pipelines, or retrieval-augmented generation (RAG)
• Contributions to relevant open-source ML serving or vector search infrastructure

• Be part of shaping the future of AI-native developer experiences on the world’s most popular developer data platform
• Collaborate with ML experts from Voyage.ai to bring cutting-edge research into production at scale
• Solve hard problems in real-time inference, model serving, and semantic retrieval — in a system used by thousands of customers worldwide
• Work in a culture that values mentorship, autonomy, and strong technical craft
• Competitive compensation, equity, and career growth in a hands-on technical leadership role

You’ll join the broader Search and AI Platform organization and collaborate with ML researchers and engineers from our Voyage.ai acquisition. Together, we’re building infrastructure for real-time, low-latency, and high-scale inference — fully integrated with Atlas and designed for developer-first experiences.

As a Senior Engineer, you'll focus on building core systems and services that power model inference at scale. You'll own key components of the infrastructure, work across teams to ensure tight integration with Atlas, and contribute to a platform designed for reliability, performance, and ease of use.

• Design and build components of a multi-tenant inference platform integrated directly with MongoDB Atlas, supporting semantic search and hybrid retrieval
• Collaborate with AI engineers and researchers to productionize inference for embedding models and rerankers — enabling both batch and real-time use cases
• Contribute to platform capabilities such as latency-aware routing, model versioning, health monitoring, and observability
• Improve performance, autoscaling, GPU utilization, and resource efficiency in a cloud-native environment.Work across product, infrastructure, and ML teams to ensure the inference platform meets the scale, reliability, and latency demands of Atlas users
• Gain hands-on experience with tools like vLLM and container orchestration with Kubernetes

• 5+ years of experience building backend or infrastructure systems at scale
• Strong software engineering skills in languages such as Go, Rust, Python, or C++, with an emphasis on performance and reliability
• Experienced in cloud-native architectures, distributed systems, and multi-tenant service design
• Familiar with concepts in ML model serving and inference runtimes, even if not directly deploying models
• Knowledge of vector search systems (e.g., Faiss, HNSW, ScaNN) is a plus
• Comfortable working across functional teams, including ML researchers, backend engineers, and platform teams
• Motivated to work on systems integrated into MongoDB Atlas and used by thousands of developers

• Experience integrating infrastructure with production ML workloads
  Understanding of hybrid retrieval, prompt-driven systems, or retrieval-augmented generation (RAG)
  Contributions to open-source infrastructure for ML serving or search

• Be part of building the AI foundation of the world’s most popular developer data platform
• Collaborate with ML researchers from Voyage.ai to bring novel ideas into scalable systems
• Tackle challenging problems in inference, observability, and distributed infrastructure
• Work in a culture that emphasizes mentorship, ownership, and technical excellence