Lead Engineer Inference Platform 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

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

• 9+ years experience working in a Customer Success, Account Management, Client Services or other similarly customer-centric role
• A background and passion for advocating on behalf of your customers - this role should act as an extension of our customers team within MongoDB
• A mind for technology - we’ll teach you about MongoDB and databases, but our customers and product are inherently technical and you should have an aptitude and curiosity to learn about those concepts
• The ability to act with a high level of autonomy, you will be expected to take full ownership of your customer portfolio and make key decisions to drive effective customer outcomes; including deciding when to include our executive team or c-suite in customer escalations, how to de-risk or de-escalate customer issues and conflict and advocating to internal teams including sales, professional services, etc
• An entrepreneurial mindset - this is a new team at MongoDB where you will be required to both deviate from established procedure and often build entirely new process or practices to ensure we best serve this customer segment
• Team player and passion for collaboration - this role will work with some of our most strategic growth customers so must align closely to Sales, Professional Services, Tech Services, and the broader MDB ecosystem
• Prior exposure to database, cloud, and infrastructure technology is a plus
• German speaking

• Work as a strategic advisor to your customer providing them with guidance on MongoDB best practices and their overall technology strategy; this could include running enablement sessions alone or with another internal MongoDB team members, advising the customer on strategies to optimize their technical environment or current spend with MongoDB, positioning and recommending product features and best practices to accelerate customers time to value and growth
• Collect feedback and identify roadblocks from customers to inform internal teams including Product, Professional Services, and Leadership on how MongoDB can build a stronger product and go to market organization
• Act as the link between our customers and product engineering to develop new innovative solutions. You will be key in building the future roadmap of our product by acting as the Product team’s eyes and ears in this field
• De-escalate and resolve critical customer issues and complaints by finding the best possible solution for both the customer and MongoDB; this could include anything from navigating a customer outage that has a financial impact on their business, to helping an application team devise a custom MongoDB solution or implementation for their critical application, no day is the same
• Build and execute account plans to mitigate risk and drive growth 3+ quarters out across your portfolio
• Lead in-person executive business reviews for strategic customers in your portfolio, including interfacing with C-suite executives and other technical leaders to align to business objectives and agree to a mutual success plan
• Work on strategic internal projects to help build the Customer Success program; our expectation is that anyone in this role has strong business acumen and the ability to create and teach best practices, new process, and enablement to the broader organization
• Document all customer interactions in internal systems, including Gainsight and Salesforce.com
• Provide feedback and guidance to leadership on key signals within MongoDB Atlas that indicate healthy or unhealthy customer accounts; as our product evolves, we’ll need to be constantly adjusting our engagement strategies based on these signals, which you’ll be in the best position to identify and share back with internal teams
• Manage the relationship with Sales Leadership and Account Executives in your territory, including reporting on business performance, training on best practices, and rolling out program updates to ensure sales people and leaders are abreast of best practices for interacting with Customer Success
• Forecast expected churn and growth to your senior leadership team
• Help interview, onboard and ramp new team members - as a more senior team member you will have an immediate role in who joins the team
• Act as a leader amongst your peers, running enablement sessions, product certifications and being vocal in team meetings to ensure those around you grow

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