Develop phased digital engineering and capability transformation roadmaps aligned with PNT/NAVWAR future planning requirements.
Support MBSE pilot architectures, digital-thread traceability, and data interface planning across requirements, architecture, software, and sustainment artifacts.
Evaluate tool integration and configuration management frameworks to inform future-state capability planning.
Provide technical insights and digital engineering recommendations to Government leadership.
Deliver informal technical mentoring to build organic Government staff competency in digital engineering/MBSE practices.
Requirements
Bachelor's degree in Systems Engineering
Bachelor's degree in Computer Science
Bachelor's degree in Engineering
Nice to have: Master's degree
Nice to have: experience transitioning programs from document-centric to model-based/digital engineering environments
Lead technical vision and architecture for AI/ML solutions across AstraZeneca’s commercial landscape, guiding advanced AI from traditional ML to generative and agentic AI, while influencing multi-functional teams and ensuring production readiness.
Responsibilities
Define and drive technical vision, roadmap and architecture for AI capabilities
Provide hands‑on technical leadership to project teams
Lead design of multi‑agent systems and LLM applications
Establish evaluation standards and quality gates
Drive model optimization and cost/latency improvements
Requirements
Bachelor's degree in Computer Science or related quantitative field
Master's or PhD in machine learning, computer science or related field
Lead technical vision and architecture for AI systems in a senior individual-contributor role, guiding advanced AI across commercial, medical, and technology domains.
Responsibilities
Define and drive the technical vision, roadmap and architecture for traditional ML, Generative AI and agentic AI capabilities
Provide hands‑on technical leadership to project teams; set design direction, review architectures and code, and remove blockers on sophisticated problems
Lead the design of multi‑agent systems and LLM applications, including orchestration, tool use, memory, guardrails and human‑in‑the‑loop patterns
Establish standards for evaluation, including automated and human‑in‑the‑loop methods, benchmarking, and quality gates pre‑ and post‑release
Drive approaches to fine‑tuning, distillation and reinforcement learning to improve quality, cost and latency over off‑the‑shelf models
Requirements
Bachelor's degree in computer science, mathematics, statistics, engineering, physics, computational sciences or a related quantitative field
Nice to have: Master’s or PhD in machine learning, computer science or related field
Nice to have: reinforcement learning (RLHF, RLAIF)
Lead Forward Deployed Engineer to accelerate Target's enterprise AI platform adoption, combining solution architecture, hands‑on engineering, and technical guidance.
Compensation
The pay range is $132,000.00 - $238,000.00
Responsibilities
Lead complex technical engagements that accelerate enterprise AI platform adoption
Design, build, and productionize AI solutions, reference implementations, technical tooling, and platform‑support capabilities
Guide technical design and implementation across engineering efforts
Apply software engineering and distributed systems expertise to improve reliability, security, performance, observability, and ops readiness
Compensation Range:
Pay Range - Start:
$118,960.00
Pay Range - End:
$178,440.00
Geographic Specific Pay Structure:
Structure 110:
$130,880.00 USD - $196,320.00 USD
Structure 115:
$136,800.00 USD - $205,200.00 USD
We believe in fairness and transparency. It’s why we share the salary range for most of our roles. However, final salaries are based on a number of factors, including the skills and experience of the candidate; the current market; location of the candidate; and other factors uncovered in the hiring process. The standard pay structure is listed but if you’re living in California, New York City or other eligible location, geographic specific pay structures, compensation and benefits could be applicable, click here to learn more.
Responsibilities
Applies and integrates advanced statistical, mathematical, data mining, predictive modeling and business analysis skills and techniques to discover and identify new insights, predict and prescribe outcomes, advise on turning new insights into actionable opportunities and outcomes, and provide the tracking and measurement of results.
Develop LLM-based, mathematical models, algorithms, machine learning techniques, and robust analytics to support analytic insights and visualization of complex data sets for productization and monetization.
Create evaluation frameworks and monitor deployed models and AI systems for performance, drift and unintended outcomes.
Applies creative problem solving and inter-departmental networking to address data and information needs across the enterprise.
Shares best practices with peers through coaching and mentoring core methodologies, patterns, standards and processes.
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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