The Lead Engineer, Evidence Management is responsible for engineering leadership, application architecture, and delivery governance for the Evidence Management digital ecosystem spanning Scientific Affairs and Regulatory Affairs. This is primarily an Appian-first, AI-enabled, and ecosystem-oriented engineering role.
Locations (2)
Limerick, Munster, Ireland
Raritan, New Jersey, United States
Compensation
The anticipated base pay range for this position is: €71,200.00 - €123,165.00. In addition to base pay, we offer the following benefits*: an annual bonus with set target (% of pay) depending on pay grade / location, where the actual amount is based on the employees’ and companies’ performance of the previous calendar year, or sales commissions.
Responsibilities
Own and maintain the application architecture strategy and engineering roadmap for evidence management capabilities across Regulatory Affairs and Scientific Affairs
Design scalable, interoperable, and compliant solutions across regulatory platforms, Appian applications, data flows, workflows, integrations, content repositories, and analytics capabilities
Translate complex regulatory and scientific evidence processes into practical application designs, engineering patterns, data models, and delivery backlogs
Drive design governance for new capabilities, major enhancements, technical debt remediation, platform modernization, and cross-system interoperability
Ensure solution designs align with enterprise standards for cloud, cybersecurity, privacy, lifecycle management, supportability
Build and maintain full-stack AI systems for hospitality operations, including intent classification, retrieval, tool workflows, evaluation, observability, and user interfaces.
Compensation
Base salary: $130,000 to $220,000 annually, plus 0.20% to 1.20% equity. Benefits include health insurance and a 401(k) match. Visa sponsorship is not available; candidates must be authorized to work in the United States.
Responsibilities
Build response-generation pipelines that classify intent, assemble context, select tools, and determine whether to respond or escalate.
Develop retrieval systems that ground responses in accurate, current customer data, including handling messy inputs.
Build reliable multi-step tool workflows and manage third-party API failures during conversations.
Create evaluation frameworks using real conversations to measure quality and catch regressions.
Implement observability and tracing that make complex agent behavior understandable to support teams.
Requirements
Strong software engineering fundamentals supported by relevant formal education.
Lead offensive security campaigns using AI-enabled tools to identify and remediate vulnerabilities across cloud, infrastructure, identity, and application environments.
Locations (3)
New York
San Francisco
Seattle, Washington, United States
Compensation
$160,000—$190,000 USD
Responsibilities
Plan and execute red team campaigns and adversary simulations
Own scope, timelines, evidence, and reporting for workstreams
Emulate real-world attacker behaviour across infrastructure, identity, endpoints, and applications
Build agentic workflows that orchestrate multiple models and tools
Lead architectural strategy for Microsoft Security’s Perception Platform, defining vision, reference architectures, and technical direction across AI, security, and cloud domains.
Compensation
Software Engineering IC5 - The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year.
Responsibilities
Define architectural vision and long‑term strategy for the Perception Platform
Establish scalable multi‑tenant reference architectures and principles
Drive architectural convergence across distributed systems and engineering teams
Define automation‑first architecture for tenant provisioning and lifecycle management
Establish architectural guardrails for security, privacy, and compliance
Requirements
Bachelor's Degree in Computer Science, Engineering, or related technical field
Nice to have: Master's Degree in Computer Science, Engineering, or related technical field
Nice to have: 8+ years technical engineering experience
Nice to have: 12+ years technical engineering experience
Nice to have: experience with large‑scale distributed systems
Lead AI/ML Engineer focused on designing, building, and deploying production LLM-powered agentic AI systems to transform healthcare operations and claims processing workflows.
Responsibilities
Design and build agentic AI systems — multi-step, tool-using agents that plan, call functions/APIs, retrieve context, and act within guardrails on complex claims and clinical workflows
Build and optimize the orchestration layer: tool/function calling, multi-agent coordination, state management, and integration with external systems via protocols such as MCP
Develop retrieval-augmented (RAG) pipelines over clinical and policy documents, with evaluation harnesses that measure agent accuracy, tool-use correctness, and guard against regressions
Take agents and models from prototype to production — data pipelines, APIs, serving infrastructure, deployment, and monitoring
Define and track evaluation metrics for agentic systems (task success rate, tool-call accuracy, latency, cost-per-run, hallucination rate) and build the test suites that keep quality high as systems evolve
Lead the technology capability that transforms prioritized business-unit AI use cases into advanced solutions, overseeing delivery across multiple programs and leading AI initiatives from concept to real adoption.
Locations (3)
Stamford, Connecticut, United States
New York, United States
Washington, United States
Compensation
The salary range for this position is 200,000.00 - 330,000.00 USD Annual. This position is also eligible for an Annual Incentive Plan target of 25% of salary and an Annual Equity target of 25% of salary.
Responsibilities
Lead AI solutions technology across strategic business-unit and enterprise use cases, managing technical leads and delivery teams across multiple programs to deliver secure and scalable AI solutions.
Serve as a hands-on technical leader for complex AI designs, including solution architecture, model integration, data flows, APIs, workflow automation, agentic patterns, evaluation strategies and observability.
Blackstone Data Science Product Engineering, Vice President
The role focuses on the creation and deployment of agentic and machine learning models that support better investment decision-making across Blackstone's Real Estate and Private Equity businesses.
Compensation
Expected annual salary range: $175,000 - $200,000
Responsibilities
Own the end-to-end delivery of agentic and machine learning models across Real Estate and Private Equity, from prototype through production, user adoption, and ongoing support
Partner with BXDS data scientists to productionize, scale, and continuously improve agentic and statistical models, including feature pipelines, model serving, retraining, and versioning
Work with investment professionals across Real Estate and Private Equity to translate underwriting, diligence, asset management, and portfolio monitoring questions into models and products, incorporating their feedback to improve model quality
Build and own the channels through which models reach users: web applications, MCP servers that expose models, data, and tools to AI assistants and agents, automated reports, and Excel-based tools
Design and build agentic workflows and retrieval and reasoning systems over unstructured investment content, including offering memoranda, leases, CIMs, board materials, and market research
Requirements
Undergraduate degree in a technical discipline (e.g. computer science, data science, applied math, operations research, or engineering)
The Forward Deployed Engineer combines technical expertise with an understanding of business challenges to support clients in their AI trajectory, build necessary platforms, and foster adoption.
Responsibilities
Analyze business needs and AI maturity of clients to define a roadmap, prioritize initiatives, and inform technological choices.
Design and build end-to-end agentic platforms adapted to client constraints using open source technologies or environments like AWS and Databricks.
Integrate these platforms into the client's information system: access to data and models, connection to business tools, security, governance, and supervision.
Support production deployment and scaling, ensuring solution reliability and cost control.
Support teams in the adoption of deployed tools: training, evolving practices, community animation, and continuous improvement.
AI Security, Senior Domain Consultant focused on driving technical wins, PoVs, architecting AI security solutions, and influencing product roadmap through customer feedback.
Responsibilities
Scope, lead, and execute hands-on Proof of Value (PoV) and technical validation projects to demonstrate real-time AI threat prevention, data loss prevention (DLP), agent governance, and supply chain security in customer environments.
Present tailored solutions and interactive demos to audiences ranging from developers and AI practitioners to CISOs and executive leadership.
Serve as the strategic voice of the customer, partnering directly with Product Management and Engineering to channel real-world field feedback into future releases of Prisma AIRS.
Design solution architectures that provide holistic AI security within the constraints of enterprise requirements.
Lead discussions on emerging AI threats and model vulnerabilities, serving as a subject matter expert for RFIs/RFPs.
Translate prioritized AI opportunities into executable roadmaps, delivery plans, and technical milestones aligned to business-unit outcomes, enterprise priorities, and responsible AI guardrails.
Establish and enforce AgenticAI development standards to be used across the enterprise
Partner with architecture, cybersecurity, data, infrastructure, platform, risk, legal, compliance, privacy, and model governance teams to ensure AI solutions meet enterprise control, resiliency, auditability, and responsible AI expectations.
Requirements
Bachelor’s degree in Computer Science
Data Science
Mathematics
or related technical discipline
Nice to have: Advanced degree in Computer Science, Machine Learning, AI, or Applied Mathematics
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