Senior Technical Solutions Engineer providing frontline support for the Databricks Data Intelligence platform, troubleshooting complex issues, managing incidents, and driving platform improvements.
Responsibilities
Serve as primary technical point of contact for escalated issues
Engage with customers to understand technical issues
Lead resolution of high-priority incidents
Collaborate with engineering, product and DevOps teams
Create and maintain detailed documentation
Requirements
Bachelor’s degree in Computer Science, Engineering, or a related field
Lead a team of software engineers focused on AI/ML infrastructure and performance engineering, driving technical leadership, project delivery, and cross‑team collaboration across Google’s global ML stack.
Responsibilities
Learn and build an intuitive understanding of existing data collection, analysis, and visualization workflows with deep introspection across Frameworks, Accelerated Linear Algebra (XLA) and runtime stack.
Support new and exciting ML paradigms (such as horizontal scaling for upcoming TPU chips) by making contributions across the end to end stack and analysis tools.
Partner with ML Stack leads to understand model optimization use cases and bring debugging to feel native in 3P environments (VSCode, Cursor, Grafana, etc.).
Work with OSS ML inference frameworks such as vLLM, SGLang to provide insights in Xprof about performance improvement opportunities.
Partner with other teams that own various parts of the ML stack to understand performance optimization use cases.
Requirements
Bachelor's degree or equivalent practical experience
This role focuses on scaling the efficiency of our information and protection integrations, ensuring our customers and partners are equipped to build the future of innovative defense.
Compensation
The expected base salary ranges for this position are: 152,000 USD – 241,500 USD for Level 3; and 184,000 USD – 287,500 USD for Level 4.
Responsibilities
Design and implement GPU-accelerated pipelines capable of processing very large (multi-TB and PB sizes) cyber, network, and industry datasets.
Develop proof-of-concept discussions that assist and illustrate how to address accelerated and AI compute workloads to tackle real customer issues.
Function as a technical lead and liaison between NVIDIA engineering, product teams, and OEM partners to drive innovative technical and business strategies.
Guide the development of Agentic AI workflows, including semantic search, retrieval-augmented generation (RAG), and Graph Neural Network (GNN) based applications for threat detection.
Translate complex AI and cybersecurity research into actionable, deployable architectures for enterprise and government customers.
Designs and builds data platforms, tools, and solutions that enable the Bank to manage, secure, and extract value from its data, providing technical leadership across multiple teams.
Compensation
The base salary for this position is reflective of the range of salary levels for all roles within this pay grade across the U.S. Individual salaries within this range will vary based on factors such as role, relevant skillset, relevant experience, education and geographic location. In addition to the base salary, this role is eligible to participate in an incentive compensation plan, with any such payment based upon company, line of business and/or individual performance.
Responsibilities
Design, develop, and support scalable data solutions, APIs, tools, and processes
Work closely with IT application teams, Enterprise Architecture, infrastructure, information security, and LOB stakeholders
Act as a principal technical expert and trusted advisor
Conduct research and development on current trends and technologies
Build and maintain CI/CD pipelines and self-service deployment tools
Requirements
Bachelor's degree in Computer Science, Information Systems, or related field
Nice to have: modern data platforms
Nice to have: cloud-native architectures
Nice to have: data transformation technologies (Snowflake, dbt)
Nice to have: traditional data engineering tools (IBM DB2, IBM Information Server/DataStage)
Software Engineer II on the HMS Factory Team building and maintaining automated testing, log-analysis, and scenario-generation tools for the Hivemind Platform.
Compensation
Pay within range listed + Bonus + Benefits + Equity
Responsibilities
Develop, test, and maintain Python scripts and libraries used to analyze simulation data
Support the development of analysis tools that process simulation outputs
Help build and maintain automated test workflows and wrappers around HMS Forge
Assist with developing program-specific test adapters, configuration files, and automation utilities in Python and C++
Contribute to programmatic scenario-generation tools
Requirements
Master’s degree in Computer Science, Computer Engineering, Aerospace Engineering, Systems Engineering, or a related technical discipline
Nice to have: Python-based data-analysis workflows
Nice to have: continuous-integration systems
Nice to have: test frameworks such as pytest, unittest, GoogleTest, Robot Framework
Lead design, development, and deployment of advanced computer vision and machine learning solutions for industrial and manufacturing applications.
Responsibilities
Design, develop, and deploy computer vision algorithms for detection, classification, segmentation, anomaly detection, and predictive analytics.
Build scalable machine learning and deep learning models using image, video, and multimodal sensor data.
Lead the complete data science lifecycle, including data collection, labeling, feature engineering, model training, validation, deployment, and monitoring.
Develop robust image-processing pipelines utilizing modern computer vision frameworks and cloud technologies.
Define data quality standards and establish model performance metrics to ensure production readiness.
Requirements
Master's degree in Data Science, Computer Science, Physics, or related field
The AI Data Engineer will design, build, and maintain data foundations for foundational applications, advanced analytics, machine learning, Generative AI, and LLM applications. The role sits at the intersection of data engineering, AI platform development, and data governance.
Responsibilities
Design, develop, and maintain scalable data ingestion, transformation, and enrichment pipelines.
Assess, modernize, and migrate legacy data platforms and tools to AI-ready data infrastructures.
Lead initiatives to replace manual or siloed data workflows with automated, cloud-enabled solutions.
Design and execute migration strategies for data assets in legacy applications and on-premise environments.
Refactor and optimize existing ETL/ELT pipelines and data models.
Hands-on, backend-heavy engineering role focused on building a unified, large-scale machine learning platform that drives one-to-one offer, content, and channel decisions. Includes technical leadership, architecture design, and mentoring engineers.
Compensation
The typical pay range for this role is: $144,200.00 - $288,400.00
Responsibilities
Architect and build scalable, production-grade backend services (APIs, microservices, batch and streaming pipelines)
Design data contracts and interfaces between data engineering, model training, and production serving layers
Own the design of versioned, queryable feature stores and point-in-time lookup APIs
Design and optimize large-scale distributed computing workflows
Set and enforce engineering standards across the ML codebase (modular design, dependency management, code review, automated testing, CI/CD)
health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
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