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Were looking for a Hands-on AI & Data Engineering Manager to lead a team building intelligent, large-scale consumer experiences powered by AI. In this role, you will lead engineers developing AI-driven products, work closely with data scientists, product managers, and designers, and ensure that AI capabilities-from LLMs and agents to product data insights-are translated into reliable, scalable production systems. You will be responsible not only for delivering AI features, but also for analyzing product data and user behavior to continuously improve AI performance and product outcomes. This is a technical leadership role combining engineering management, architecture ownership, AI product development, and data-driven decision making.
Responsibilities:
Lead a team of data scientists, data engineers and product analysts
Own the delivery of AI-powered product capabilities, from research and experimentation to production and operation
Drive excellence, code quality, and best development practices
Provide technical direction and hands-on guidance for complex AI systems
Drive the integration of LLMs, AI agents, and intelligent workflows into core consumer experiences
Ensure AI solutions are safe, scalable, observable, and continuously improving
Lead initiatives around product data analysis and experimentation
Analyze user interactions with product features to improve accuracy, UX, and business impact
Partner with product teams to define metrics, dashboards, and experiments that guide product improvements
Design system architectures for AI-enabled applications at scale
Evaluate and select technologies for AI platforms and data pipelines
Guide the development of prompt engineering frameworks and centralized prompt management
Ensure robust monitoring, evaluation, and feedback loops for AI outputs
Translate product and business goals into technical roadmaps and execution plans
Drive alignment between AI capabilities and measurable product outcomes.

As a Data Science Manager, you will lead, mentor, and grow a team of applied data scientists working on machine learning models that power AI-driven cybersecurity solutions. You will own the end-to-end development lifecycle of AI projects, from exploratory research and experimentation through to scalable deployment and optimization. In this role, you will partner with engineering and product leadership to define technical strategies, prioritize initiatives, and ensure successful model delivery. You will also drive innovation across a broad spectrum of ML domains, including supervised/unsupervised learning, anomaly detection, and generative AI, while providing hands-on guidance and championing best practices in MLOps, data governance, and model evaluation.
Key Responsibilities
Lead, mentor, and grow a team of applied data scientists working on machine learning models that power AI-driven cybersecurity solutions.
Own the end-to-end development lifecycle of AI projects - from exploratory research and experimentation through scalable deployment and optimization.
Partner with engineering and product leadership to define technical strategies, prioritize initiatives, and ensure successful model delivery.
Drive innovation across a broad spectrum of ML domains including supervised/unsupervised learning, anomaly detection and generative AI.
Provide hands-on guidance in model development using tools such as scikit-learn, PyTorch, TensorFlow, and Hugging Face.
Ensure high-quality execution through strong code review practices, reproducibility, and model evaluation frameworks.
Champion best practices in MLOps, data governance, explainability, and monitoring.
Keep pace with academic and industry advances and translate cutting-edge research into productized capabilities.

What you will be doing
Lead a team of 4 analysts and partner directly with the CRO, VP Marketing, and other GTM leaders to deliver high-impact analytics that accelerate growth.
Define and lead a data strategy that turns insights into fast, confident decisions across the organization.
Push AI-powered analytics to new heights. AI is already part of our day-to-day, and you'll push it further through research-driven AI initiatives that scale the team's impact well beyond what we could achieve without it.
Own core business metrics, improving visibility, accountability, and decision speed.
Develop and retain top talent, building a high-impact analytics team.

Were looking for a Data & Analytics Team Lead to manage and grow our data team and own the data layer behind our AI. Youll lead a team of data analysts responsible for neural-network model analysis, model performance tracking, customer-facing and internal BI. Youll act as both the people manager and the technical lead for the team, setting technical direction while staying hands-on.

Youll thrive in this role if youre passionate about turning data into better AI products, enjoy solving complex analytical problems, and like working at the intersection of Data, Analytics, and Artificial Intelligence.

Responsibilities:

Lead the data strategy supporting AI model development, evaluation, and continuous improvement
Define KPIs and monitoring frameworks for AI model performance.
Analyze NN model outputs, build and maintain model-tracking and performance-evaluation workflows
Drive close calibration with the Algo, Product, and Data Engineering teams to align data definitions, pipelines, and metrics
Stay hands-on with SQL, Python, BI tooling, and the modern data stack
Build and own agentic data workflows - designing the context, tooling, and data access that let AI agents reliably query, analyze, and act on company data

At UVeye, we're on a mission to redefine vehicle safety and reliability on a global scale. Founded in 2016, we pioneered the world's first fully automated suite of AI-powered vehicle inspection systems, combining computer vision, machine learning, and generative AI. With over $380M in funding and strategic partnerships with Toyota, Amazon, General Motors, JLR, Volvo, and Hertz, our technology is deployed across manufacturing plants, dealerships, wholesale auctions, fleets, and seaports worldwide. Named one of Fast Company's Most Innovative Companies of 2026, our 300+ global employees are solutions-oriented, accountable, and driven by one shared goal: making roads safer for everyone.
We are looking for an experienced Data Engineering Team Leader In this role, you will lead and strengthen our Data Team, drive innovation, and ensure the robustness of our data and analytics platforms.
?A day in the life and how you’ll make an impact:
* Drive the technical strategy and roadmap for the data engineering function, ensuring alignment with overall business objectives.
* Own the design, development, and evolution of scalable, high-performance data pipelines to enable diverse and growing business needs.
* Establish and enforce a strong data governance framework, including comprehensive data quality standards, monitoring, and security protocols, taking full accountability for data integrity and reliability.
* Lead the continuous enhancement and optimization of the data analytics platform and infrastructure, focusing on performance, scalability, and cost efficiency.
* Champion the complete data lifecycle, from robust infrastructure and data ingestion to detailed analysis and automated reporting, to maximize the strategic value of data and drive business growth.

We are looking for a Principal AI Engineering Lead to own and drive our AI engineering maturity journey across a 100-person R&D, QA, and DevOps organization.
This is a hands-on individual contributor role with outsized influence - you will be the internal expert, practitioner, and change agent who takes us from inconsistent AI tool adoption to a fully agentic, multi-phase autonomous engineering capability.
You will not be managing people. You will be changing how 100 engineers work.
This role is equal parts engineering, enablement, and architecture. You will write real code, build real agent workflows, and make the abstract concrete - turning a defined AI maturity framework into daily practice across Java/JVM, Python, C#/.NET, Oracle PL/SQL, and C++ codebases.
Scope of your position:
AI Adoption & Enablement
Audit current AI tool usage across R&D, QA, and DevOps - identify where adoption is genuine vs. nominal
Establish and maintain CLAUDE.md-equivalent constitution files: encoding team conventions, architectural standards, testing patterns, and security policies so AI tools produce consistent, codebase-aware output from day one
Drive daily active usage above 70% across the engineering org, measured by tool telemetry - not seat count
Design and deliver hands-on enablement: prompt engineering, output validation, effective task decomposition, and AI-assisted debugging across our primary stacks (Java/Spring, Python, C#/.NET, C++, Oracle PL/SQL)
Run monthly retrospectives to surface what context AI is still missing and close those gaps systematically
Agentic Infrastructure & Workflow Engineering
Architect and implement the infrastructure that makes autonomous agent execution safe: sandboxed execution environments, audit logging for all agent actions, and state checkpointing for mid-task recovery
Build and enforce the specification discipline: structured spec templates with machine-verifiable acceptance criteria, spec completeness gates before agent assignment, and a lightweight spec-driven development workflow appropriate for our codebases
Stand up self-verifying test loops - agents that write tests, implement, run CI, and iterate to green without human intervention - with coverage gates enforced in CI (80%+ on AI-generated code paths)
Shift CI/CD pipelines to increase build throughput: automated rollback, feature flags for deploy/release decoupling, and tiered review workflows (auto-merge → single reviewer → full review)
Deploy and tune Claude Code as the primary agentic coding platform, alongside evaluation and integration of other tools (GitHub Copilot, Cursor, or equivalent) where they complement the workflow