Machine Learning Engineer

• Design, build, and operate end‑to‑end AI/ML solutions (data pipelines, models, services, and tools) for diagnostics, prognostics, and test analytics.
• Implement production‑grade ML pipelines on platforms such as Azure and Databricks, covering data ingestion, feature engineering, training, evaluation, and inference for batch and streaming workloads.
• Develop and maintain robust, observable ML services and internal tools that make complex vehicle and field data easy to use for engineers and technical stakeholders.
• Apply practical ML and statistical methods (e.g., tree‑based models, time‑series and anomaly detection, deep learning where appropriate) with a focus on reliability, explainability, and impact.
• Own model and data observability in production, including metrics, dashboards, alerts, and remediation workflows for drift, data quality, and performance regressions.
• Partner with data engineering to define and use industrialized and vectorized data products that support search, RAG, and analytics at scale.
• Review designs and code, mentor AI/ML practitioners, and help set high standards for testing, logging, deployment, and documentation.
• Collaborate with diagnostics/prognostics SMEs, validation, safety, and program teams to prioritize work, define success metrics, and embed solutions in day‑to‑day engineering workflows.

Staff Artificial Intelligence Machine Learning Engineer

AI Research Scientist

• Engage in relevant research and development (R&D) of AI systems, models, and advanced machine learning algorithms that augment physics-driven modeling and simulation systems
• Explore and implement AI-powered simulation tooling in support of AI workflows through reinforcement learning, multi-agent systems, and hybrid modeling approaches
• Collaborate with research, engineering, and product teams to build AI-powered solutions that meet mission-critical modeling and decision-support needs
• Engage in and support the drafting and review of conference and journal articles and presentations, sharing advances with both internal stakeholders and the wider research community.
• Contribute content to technical invention disclosures, including associated narrative, graphics, and engagements in support of patent development
• Perform additional responsibilities (no more than 10% of duties) in support of the company’s technology and product development initiatives.

AI Research Scientist, Applied AI

• Acquire deep understanding of the business problems and translate them into appropriate business solutions
• Drive development and delivery of analytic and statistical models using skills such as data acquisition and management, algorithm design, and model development and refinement
• Ensure overall quality of the data and solutions throughout the analytic development process
• Interpret results and communicate insights to technical and non-technical audiences including executive leadership.

AI Engineer

• Responsible for architecting solutions achieving customer business outcomes
• Develop and articulate compelling, accurate, and relevant proposals
• Provide technical expertise to sales teams and customers
• Develop customer proposals and critically review them
• Quantify the impact of business problems and position business value
• Communicate how the solution value propositions address customer business needs
• Drive the Account Business Planning process leveraging knowledge of industry trends and customer’s technical environment

AI Architect – Pre-Sales

• Lead the design, development, and deployment of production AI systems end-to-end
• Contribute hands-on to the codebase and set a high bar for engineering quality
• Establish scalable patterns for evaluation, deployment, and system reliability
• Conduct technical design reviews and guide architectural decisions
• Mentor and develop engineers, providing regular coaching and technical guidance
• Support hiring efforts, including candidate evaluation and onboarding
• Partner with cross-functional stakeholders to align priorities and deliver impactful solutions
• Help translate experimentation into stable, production-ready systems

Full Stack AI Engineer

• Lead the architecture and end-to-end execution of the ML lifecycle - data strategy, model development, deployment, and continuous operation - primarily for computer vision and LLM/agentic systems
• Own the MLOps foundation: training and deployment pipelines, model serving, CI/CD for models, and reproducible experimentation
• Set and enforce standards for model accuracy and quality (evaluation frameworks, offline and online metrics, regression testing of models, and A/B testing) and hold the team to defined targets
• Ensure production AI systems are scalable and highly available: define service-level objectives for latency, throughput, and uptime, and establish monitoring, drift detection, alerting, and rollback practices
• Plan and manage team workload: delegate tasks, set daily, weekly, and monthly goals, and track progress against them
• Partner with product, data engineering, DevOps/infrastructure, and clinical stakeholders to align priorities and drive projects forward

Machine Learning Manager

• Develop and maintain machine learning models that are integral to our production decision-making system and directly influencing business outcomes.
• Adopt or build new technologies for training and serving ML models (e.g. support large models, increase developer velocity, etc)
• Monitor the latest ML research for functional ideas that the team could try.
• Optimize ML Pipelines – Build and scale efficient pipelines for real-time and batch processing.
• Model Monitoring & Improvement – Track performance, detect drift, and automate retraining.
• Use strong communication skills (verbal and written) to explain statistical and machine learning concepts to both technical and non-technical audiences
• Collaborate with a team of world-class engineers with diverse backgrounds.
• Be part of an “engineering excellence” culture through state-of-the-art tools, risk-driven testing, explainable systems, and code review.

Machine Learning Engineer, Conversion ML

• Develop spatio-temporal machine learning models for multi-object tracking and uncertainty estimation.
• Develop machine learning models to estimate road features, lane markings, road shape and road topology
• Read literature, analyze raw data, and design state-of-the-art solutions
• Transition solutions from the lab to the test track and public roads to ensure successful production-level implementation
• Collaborate with perception experts and experienced roboticist on algorithm design, prototyping, testing, deployment, and productization
• Build and maintain industry-leading software practices and principles
• Develop clean and efficient software for perception modules interfacing with other key modules
• Show initiative and be a valued team member in a fast-paced, innovative, and entrepreneurial environment

Senior Software Engineer – Machine Learning, Multi-Object Tracking

• Implement the latest research advances in Neural Rendering and generative models
• Translate cutting edge solution in the domain of autonomous driving for high-quality Camera, LiDAR and Radar sensor simulations
• Support implementing a neural rendering framework that allows to scale perception simulation and AV 3.0 training
• Integrate the framework in a cloud environment and automate the pipeline to allow scaling for the target verification and validation of our autonomous trucks
• Own development projects in the team – From research, design, to implementation, testing and deployment
• Design, implement, test and deploy shippable production quality software starting from early prototypes using disciplined software development processes.
• Work in the cloud machine learning ecosystem alongside other machine learning services existing in the company.
• Proactively assess current capabilities to identify areas for improvement proposing solutions that align with core strategy and operation.
• Demonstrate project management skills, serving as project lead guiding less experienced team members in multiple facets of project execution, coaching and mentoring as needed.

Senior ML Engineer – Neural Rendering

• Define and drive the vision, strategy, and roadmap for enterprise semantic data and data connectivity across business domains
• Partner with business and IT leaders to identify high-value opportunities to connect and standardize data across systems
• Position the semantic data product as a product, not just a technical asset, with clear customers, outcomes, adoption goals, and success metrics
• Define and evolve the product’s value proposition, ensuring alignment to business priorities and measurable outcomes
• Act as the general manager of the product, balancing user needs, technical feasibility, strategic priorities, operational constraints, and business impact
• Define and manage data products aligned to key business domains, including clear ownership, users, and success metrics
• Define target users and use cases for data products, ensuring alignment to specific decisions, workflows, and business outcomes
• Partner closely with business customers to ensure data products support critical decisions, workflows, and operational outcomes
• Drive adoption and usage of shared data assets across business units
• Identify and prioritize use cases where connected data unlocks measurable business value (e.g., improved decisioning, automation, personalization)
• Ensure solutions are aligned to real-world workflows and deliver tangible outcomes
• Lead the development and evolution of enterprise ontologies, taxonomies, and data models that represent key business concepts and relationships
• Establish a scalable semantic layer that enables reuse across analytics, AI, and operational use cases
• Ensure alignment of definitions across domains to reduce fragmentation and duplication
• Support both structured and unstructured data integration, enabling downstream AI and analytics applications
• Ensure data is discoverable, understandable, and usable across teams through consistent definitions and relationships
• Partner deeply with business domains to understand workflows, decision points, and data dependencies, acting as a product owner for how data supports those functions
• Own the end-to-end product lifecycle from discovery and definition to delivery and iteration
• Define product requirements, success metrics, and release plans
• Continuously refine the semantic data product based on user feedback and evolving business needs
• Define critical metrics to measure adoption, usability, and impact of the semantic data product
• Supervise usage and continuously improve accessibility and value delivery
• Evangelize the value of connected semantic data across the organization
• Partner with data governance teams to establish standards, definitions, and data quality expectations
• Promote consistency, reusability, and scalability of data assets across the enterprise

Senior Manager, AI Lead – Semantic Layer

• Own the product roadmap covering agentic execution environments, skills and MCP ecosystem, agent orchestration, and AI-powered code review
• Drive developer adoption through training, newsletters, cross-team projects, and feedback sessions
• Define and track success metrics
• Coordinate across teams as the product voice spanning various areas of the Developer Experience data platform
• Shape vendor strategy: evaluate external AI tooling
• Own ADAS ecosystem discovery by mapping tools, workflows, build systems, and dependencies
• Build the business case for convergence by quantifying dual-ecosystem costs
• Identify AI acceleration opportunities
• Communicate to leadership: translate technical concepts into briefings

Staff Product Manager, AI Developer Productivity

• Design, build, and deploy generative and agentic AI solutions that automate, optimize, and transform tools supporting Medtronic’s global operations
• Collaborate closely with cross-functional partners to implement intelligent automation, architect AI-powered workflows, and develop scalable, robust APIs and platforms that drive operational efficiency and enhance staff experience across global facilities
• Support Medtronic’s strategic objectives by leveraging advanced AI / Machine Learning (ML) frameworks and modern development practices to deliver measurable business impact
• Ensure contributions align with the company's mission to alleviate pain, restore health, and extend life

Principal Artificial Intelligence Engineer

• Design, build, and operate end-to-end machine learning and artificial intelligence pipelines supporting batch, streaming, and real-time inference use cases
• Automate the full machine learning lifecycle including ingestion, feature engineering, training, validation, deployment, monitoring, and retraining
• Implement CI/CD pipelines for machine learning systems with automated testing, validation gates, and controlled model promotion
• Develop orchestration workflows using tools such as Airflow, Kubeflow, and MLflow for experiment tracking and governance
• Optimize artificial intelligence workloads for performance, scalability, and cost efficiency using distributed compute and cloud-native services
• Establish monitoring and observability frameworks including performance metrics, data quality checks, drift detection, and bias monitoring
• Design automated retraining strategies including trigger-based, schedule-based, and performance-based refresh cycles
• Create repeatable prompting frameworks and artificial intelligence guardrails to support safe and effective AI-assisted development
• Implement access controls, secrets management, compliance standards, and security best practices across machine learning and artificial intelligence platforms
• Evaluate and operationalize emerging artificial intelligence technologies and vendor tools, identifying measurable business value
• Mentor engineers and data scientists on Machine Learning Operations best practices and influence enterprise-wide architectural standards

AI Commercial, ML Ops Engineer

• Identify opportunities to apply AI, machine learning, and automation to improve manufacturing throughput, quality, and cost efficiency
• Lead end-to-end project management for AI initiatives, from concept through deployment and continuous improvement
• Develop, test, and deploy AI models and tools (e.g., predictive maintenance, demand forecasting, quality inspection, workflow automation)
• Collaborate with production, engineering, and IT teams to integrate AI solutions into existing systems and processes
• Analyze operational data to identify trends, inefficiencies, and opportunities for optimization
• Translate business needs into technical requirements and actionable solutions
• Manage project timelines, budgets, vendor relationships, and stakeholder communication
• Build dashboards, reporting tools, and data pipelines to support real-time decision-making
• Ensure solutions are scalable, secure, and aligned with organizational goals
• Provide training and change management support to drive adoption across the workforce
• Stay current on emerging AI technologies and recommend practical applications for the business

AI Developer – Project Manager

• Design and implement scalable APIs for agent orchestration and learner interaction
• Build data pipelines that feed agent evaluation and continuous improvement cycles
• Create event streaming infrastructure for real-time learner interaction analysis
• Ensure data quality and accessibility for training and evaluating learning agents
• Improve data infrastructure for cost and performance in close coordination with Data and ML teams
• Design and implement rigorous evaluation frameworks to measure agent performance and improve cycles
• Develop LLM evaluation processes and perform error analysis to identify systemic improvements for agentic learning systems
• Support instrumentation that makes evaluation relevant (metrics, traces/logs, and analysis workflows)
• Participate in on-call rotation and incident response for learning platform reliability
• Contribute to runbooks, postmortems, and reliability improvements as we evolve our operating model

Senior Software AI Engineer

• Develop actionable insights and recommendations in support of business objectives using in-house and external data sources
• Collaborate with business and data engineering professionals for timely and reliable delivery of data solutions
• Leverage deep expertise in data engineering, data science, advanced analytics, and insurance industry data to aid business partners in risk management
• Drive efficiencies and improve customer experience
• Partner with business stakeholders to scope, structure, and drive analytical projects from requirements through delivery
• Present findings and recommendations clearly to both technical and non-technical audiences
• Source, validate, and integrate structured and unstructured data across platforms
• Conduct exploratory data analysis to reveal data quality issues and identify opportunities
• Analyze business trends, time series patterns, model performance, and causal relationships using advanced statistical and machine learning / deep learning methods
• Design, build, and deploy machine learning / deep learning models — including NLP, computer vision, and LLM-based solutions
• Create visualizations, dashboards, and reports that communicate analytical and model results to support decision-making across the organization
• Own and maintain recurring analytical, reporting, and model-monitoring processes
• Document data pipelines, model architecture, analytical frameworks, and deliverables for knowledge sharing across technical and non-technical teams

Machine Learning Scientist

• Own and articulate the AI vision for Global Finance, aligned with Dematic’s enterprise digital, data, and automation strategy
• Define, maintain, and evolve the roadmap for financial artificial intelligence, balancing near-term impact with long-term capability development
• Translate executive leadership priorities into a prioritized portfolio of AI-enabled initiatives
• Continuously assess emerging AI capabilities and their applicability to Finance use cases
• Partner with Finance teams (FP&A, Function Controlling, Project Management, Accounting, etc.) to identify AI-ready opportunities
• Translate Finance workflows, decision processes, and challenges into clear, actionable AI use cases
• Develop case studies and benefit analysis models, including efficiency gains, cost savings, risk reduction, and decision-quality improvements
• Act as the primary liaison between Finance process owners and AI technical teams, ensuring shared understanding of requirements and constraints
• Ensure Finance domain knowledge, controls, and business logic are embedded into AI solution development
• Advocate for practical, usable solutions that resolve real problems and fit Finance ways of working
• Own AI governance for Finance, including data privacy, model risk management, audit readiness, and regulatory compliance
• Ensure AI solutions align with financial controls, internal governance standards, and external regulations
• Establish Finance AI standards, policies, and operating models for responsible and ethical AI use
• Partner with Risk, Compliance, IT, and Internal Audit to ensure transparency and control
• Coordinate pilot programs and User Acceptance Testing (UAT) with Finance end users
• Validate outputs, capture feedback, and ensure solutions meet business and control requirements
• Lead change management, training, and AI literacy across the Finance organization
• Develop user materials, job aids, and communication plans to support adoption and behavioral change
• Define and analyze key performance indicators and value realization metrics for Finance AI initiatives
• Assess live AI solutions to ensure continued performance, accuracy, and adoption
• Surface issues, risks, and improvement opportunities to technical and Finance leadership
• Provide regular executive updates to leadership on progress, benefits, and roadmap
• Partner closely with IT, Digital, and Data teams on AI architecture, platforms, security, and scalability
• Ensure Finance AI solutions leverage enterprise-aligned, secure, and reusable technology foundations
• Balance innovation speed with sustainability, control, and integration

Finance AI Lead

• Establish engineering standards, best practices, and evaluation frameworks for AI systems
• Lead technical decision-making for model selection, system design, and deployment strategies
• Act as the subject matter expert for agentic AI and modern LLM-based systems within the organization
• Architect and deliver production-grade, multi-step AI agents capable of autonomous reasoning, tool orchestration, task decomposition, memory management, and human-in-the-loop escalation
• Design and deliver AI systems on enterprise cloud platforms (e.g., AWS, Azure), including LLM services (AWS Bedrock, Azure OpenAI)
• Own the agent evaluation and observability stack, including benchmarking, tracing, regression testing, and performance monitoring
• Optimize LLM inference costs and resource utilization for production workloads
• Partner with business leaders to identify, prioritize, and shape AI-driven initiatives aligned with organizational goals
• Translate complex business problems into scalable AI solutions with measurable impact
• Drive roadmap planning and investment decisions related to AI and automation
• Collaborate with IT, data engineering, and operations teams to integrate AI solutions into enterprise systems
• Mentor and develop machine learning engineers and data scientists
• Provide technical guidance and elevate team capabilities in modern AI practices
• Ensure responsible and compliant use of AI systems, including managing risks related to model behavior, data usage, and regulatory considerations in a highly regulated industry
• Lead evaluation and integration of external AI platforms and vendors, including assessment of cost, intellectual property, scalability, security, and long-term architectural impact

Principal Machine Learning

• Design, develop, and deploy machine learning models and AI solutions tailored to Finance use cases (e.g., forecasting, planning, variance analysis, anomaly and risk detection).
• Build and maintain ML models for financial planning, forecasting, trend analysis, and anomaly detection across large, structured datasets.
• Develop LLM‑powered tools to support financial analysis, commentary generation, summarization, and scripted insights for Finance users.
• Translate Finance requirements into data pipelines, feature engineering, model architecture, and deployment approaches.
• Conduct model validation, back‑testing, and performance evaluation to ensure accuracy, robustness, and business relevance.
• Evaluate model performance over time and diagnose issues related to data quality, concept drift, and changing business conditions.
• Implement appropriate controls, explain-ability, and documentation to support Finance governance, audit, and compliance requirements.
• Document model assumptions, methodologies, limitations, and change history for audit and risk review.
• Implement MLOps standard methodologies, including model versioning and lifecycle management, drift detection and performance monitoring, retraining schedules and automated pipelines, and reproducibility and rollback procedures.
• Partner with IT to deploy models into enterprise environments (cloud, Salesforce, SAP, Snowflake, proprietary tools, etc.).
• Ensure AI solutions are secure, scalable, and maintainable within enterprise standards.
• Collaborate with Finance, IT, data teams, and other AI workstreams to promote consistent standards, tooling, and patterns across the organization.
• Serve as a technical thought partner to Finance leaders, helping shape the AI roadmap and identify high‑value use cases.
• Help educate Finance partners on AI capabilities, limitations, and responsible usage.
• Contribute to establishing foundational AI practices for a growing Finance AI team.

See all jobs on JobTailor

Staff Artificial Intelligence Machine Learning Engineer

AI Research Scientist, Applied AI

Machine Learning Engineer

AI Architect – Pre-Sales

Full Stack AI Engineer

Machine Learning Manager

Full Stack AI Engineer

Machine Learning Engineer, Conversion ML

Senior Software Engineer – Machine Learning, Multi-Object Tracking

Senior ML Engineer – Neural Rendering

Senior Manager, AI Lead – Semantic Layer

Staff Product Manager, AI Developer Productivity

Principal Artificial Intelligence Engineer

AI Commercial, ML Ops Engineer

AI Developer – Project Manager

Senior Software AI Engineer

Machine Learning Scientist

Finance AI Lead

Principal Machine Learning

Senior Applied AI Engineer – Finance