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

Machine Learning Engineer

• 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

AI Engineer

• Design and implement agentic AI systems capable of multi‑step reasoning, tool use, and autonomous task execution.
• Architect enterprise‑grade GenAI solutions, including RAG pipelines, embedding strategies, orchestration layers, and evaluation frameworks.
• Lead PoCs and pilots and scale successful solutions into production‑ready capabilities.
• Establish reusable architectural patterns, reference implementations, and technical standards for GenAI and agentic systems.
• Integrate AI solutions with existing platforms, data sources, and workflows to support adoption.
• Mentor team developers and contribute to raising overall GenAI and agentic AI maturity.
• Embed governance, security, evaluation, and monitoring considerations into solution design and deployment.

PharmSci Agentic and Generative AI Architect

• Lead and scale the Forward Deployed AI Engineering team across its two functions: customer-embedded engineering and AI & Data Science
• Own the team's mandate: embed in strategic customer accounts to discover problems, build high-grade solutions, and produce cross-customer intelligence that elevates the entire platform
• Define the engagement model for FDE work — how engineers are assigned to accounts, how engagements are scoped, and how solutions are documented and productized
• Oversee the AI & Data Science function — ensuring cross-customer telemetry, threat intelligence, and detection optimization work is translated into tools that the SE&A team can operate
• Partner with Sales to identify strategic accounts that are candidates for FDE engagement
• Partner with the SE&A and Rapid Deployment teams to ensure FDE-built solutions flow back into the broader delivery model
• Serve as an executive-level presence with strategic customers — participating in account reviews and helping define the roadmap for embedded engagements
• Evaluate new AI technologies, data science tooling, and engineering approaches that can improve team output and customer outcomes
• Hire, develop, and retain engineers across both the FDE and AI & DS functions

Director of Forward Deployed AI Engineering

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Lead AI Engineer

• Represent Informatics as the subject matter expert in cross-functional initiatives, collaborating closely with scientists, data teams and technical stakeholders.
• Identify and deliver solutions where computational and AI-driven approaches can improve efficiency, decision making, and knowledge utilization across complex workflows.
• Design, develop and deploy production-ready AI-enabled applications that integrate structured and unstructured data for scalable use.
• Build and implement retrieval-augmented systems (RAG), semantic search frameworks, and embedding-based data workflows to enable intelligent information retrieval.
• Develop conversational AI interfaces and agentic architectures that automate knowledge-intensive tasks and support user interaction with complex datasets.
• Research, design and develop machine learning algorithms, tools and systems;
• Write structured, tested, readable and maintainable code, as well as participate in code reviews to ensure code quality and distribute knowledge for existing and new tools that allow us to extract, analyze, visualize and produce valued added insights of data for our customers;
• Independently pursue research projects, implement novel computational workflows and support a dynamic multi-disciplinary team focused on moving projects from the hit identification stage to the discovery of candidates;
• Perform statistical analysis and fin-tuning using test results;
• Be actively involved in digital transformation projects and data governance objectives working closely with IT and scientific teams;
• Ensure proper documentation of workflows, reports, tools, and analyses of the Research Informatics’ team;
• Provide transparency and regular communication on project status, potential roadblocks for execution, and new strategies with Technology Sciences leadership;
• Ensure project activities align with Eurofins policies, procedures and methodologies

Machine Learning Engineer II

• 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

• Implement Sophisticated AI Agents: Design, build, and deploy complex AI agents using LangChain and LangGraph
• Master Prompt & Context Engineering: Design, test, and refine complex prompts and contextual data frameworks to ensure our AI agents perform with maximum accuracy, efficiency, and reliability
• Lead AI Research & Innovation: Stay at the bleeding edge of AI
• Build for Production Scale on GCP: Engineer and operate our AI systems in a scalable, reliable production environment on Google Cloud Platform
• Champion MLOps for Agentic Systems: Establish and lead best practices for the reliability, versioning, monitoring, and observability of our AI agents
• Collaborate to Deliver Impact: Partner closely with product leaders, data scientists, and other engineers to translate business needs into technical reality
• Champion modern software development practices by actively using AI code-assist tools to accelerate development cycles
• Build, manage, and mentor a cross-functional team of software, quality, and reliability engineers, fostering a culture of technical excellence and continuous improvement
• Define and report on key engineering metrics (SLA, SLO, SLI) and ensure compliance with security, quality, and financial operations best practices
• Collaborate with product managers, architects, SREs and business partners to define technical strategy and create software roadmaps
• Lead troubleshooting efforts to resolve production and customer issues

• Design and implement data-driven systems that enhance decision-making across dealer workflows
• Develop predictive or optimization logic using structured dealership data
• Translate business objectives into scalable intelligent services
• Improve consistency, accuracy, and efficiency in operational processes
• Ensure solutions are explainable, reliable, and aligned with enterprise and regulatory expectations
• Build systems that extract insights from structured dealership data (CRM, DMS, transactional records)
• Implement knowledge retrieval or search capabilities across policies and documentation where appropriate
• Collaborate with product teams to surface actionable intelligence within dashboards and workflows
• Design and deploy containerized AI services within AWS (ECS/Fargate)
• Integrate intelligent services into existing APIs and application workflows
• Monitor system performance, reliability, and integrity
• Ensure data security, auditability, and compliance alignment

AI/ML Engineer – Automotive CRM

• Architect and lead the development of agentic AI systems that automate and augment finance workflows.
• Design and implement multi-agent systems leveraging LLMs, tool-use frameworks, and orchestration patterns.
• Translate cutting-edge research in LLMs and agentic AI into scalable, production-ready solutions.
• Establish guardrails, evaluation frameworks, and responsible AI practices to ensure safe, compliant, and reliable outputs.
• Lead the design and implementation of advanced predictive models, including time series forecasting and attrition prediction across customer segments.
• Develop interpretable, production-grade models that drive retention strategies and financial planning.
• Define and standardize evaluation metrics, validation frameworks, and monitoring systems for model performance and drift detection.
• Design and build scalable AI/ML systems with a strong emphasis on software engineering best practices.
• Develop and integrate AI services into internal applications and workflows.

Principal Applied AI Engineer, Finance

• 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 comprehensive systems and frameworks for AI applications and products.
• Construct prototypes and minimum viable products to validate AI/ML solutions before committing substantial resources.
• Develop, refine, and productionize AI systems using cloud resources such as Azure OpenAI, Azure ML, and Cognitive Services.
• Contribute to the creation of robust data pipelines for model evolution and production systems.
• Develop specifications for low latency APIs and services necessary to deploy AI models and incorporate them into applications.
• Supervise and sustain AI systems in production to guarantee consistent model accuracy and reliability.
• Actively participate in a team that exercises principled, agile-like development practices.
• Create and maintain thorough documentation that is consistent with team procedures, corporate policies, and expectations.
• Ensure peer review on all assigned work, as well as conduct peer reviews over the work of others as requested.
• Guide junior AI engineers on AI/ML and industry best practices and methodologies.
• Keep abreast of new tools and concepts through reading documentation or literature and actively practicing skills development.
• Support and participate in meetings with external stakeholders.

AI Engineer – III

• Design, develop, and deploy scalable AI solutions
• Build LLM-based solutions for enterprise applications
• Integrate LLMs with business systems
• Architect AI agents for intelligent automation
• Implement CI/CD pipelines for machine learning models
• Monitor model performance and governance
• Collaborate with cross-functional teams for solution delivery
• Create technical documentation and mentoring

• As a Senior AI Engineer focused on Agentic Evaluation and Verification and Validation (V&V), you will join the AI and Data Science team within Slingshot’s Research and Development organization.
• You will contribute to advancing how intelligent systems are evaluated and validated for mission-critical space operations.
• This role focuses on building and scaling evaluation frameworks, benchmarks, and simulation-backed validation systems for agentic AI systems, including multi-step, tool-using, and autonomous decision-making workflows powered by LLMs and reinforcement learning.
• Your work will directly support the development of reliable and trustworthy autonomous mission planning systems.
• You will partner closely with AI researchers and domain experts to translate real-world mission concepts into structured, testable evaluation systems.

Senior AI Engineer, Agentic Evaluation – V&V

• Build and deliver scalable systems that incorporate machine learning and Generative AI capabilities.
• Create, evaluate, and deploy machine learning solutions, including applications powered by large language models.
• Establish and maintain datasets, testing strategies, and validation approaches to ensure model accuracy and stability.
• Partner with product and engineering teams to translate business needs into practical, deployable AI solutions.
• Integrate machine learning components into existing platforms and services.
• Develop and manage automated pipelines that support continuous integration and delivery of machine learning solutions.
• Monitor performance in production environments and refine systems based on real-world usage.

AI/ML Engineer, AWS

• Manages the design and development of scalable software solutions utilizing statistical, artificial intelligence (AI), and machine-learning (ML) modeling approaches
• Performs and oversees research and testing to develop machine learning algorithms and predictive models
• Conducts and reviews model training and evaluation
• Integrates, tests, tunes, and monitors solutions
• Leads the design, development, and maintenance of predictive analytics, machine learning, and artificial intelligence solutions, making specific determinations about overall performance, reliability, and scalability
• Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to develop use cases and to deploy solutions into production
• Communicates advanced ML/AI concepts to senior leadership to facilitate decision making
• Works cross functionally with product managers, data scientists, and engineers to understand, implement, refine, and design machine learning and other algorithms
• Maintains knowledge of new learning approaches, technology, and tools through continuous research to ensure currency of solutions
• Drives innovative ideas and act as one of the thought leaders in identifying and selecting use cases for applicability of machine learning, appropriate modeling techniques and technologies, and determines success criteria and implementation methodologies
• Manages the hiring and training of new and existing staff, conducts performance reviews, and provides leadership, technical guidance, and coaching
• Shares knowledge and develops staff capabilities to strengthen understanding of business issues and best practices
• Develops and communicates organizational objectives; inspires and motivates team members to achieve results
• Responsible for the establishment and maintenance of Agile Scrum ceremonies and methodologies on the team
• Performs other duties as assigned
• Complies with all policies and standards

Senior Manager, Machine Learning – Fraud, Waste and Abuse

• Oversee the design and architecture of scalable, reliable, and secure systems that power autonomous detection, RAG-backed investigation, and auto-remediation workflows
• Define the technical strategy and architecture for our core platform, ensuring it scales to petabytes of security data and billions of daily events
• Power autonomous detection, RAG-backed investigation, and auto-remediation workflows
• Develop and Productionize large-scale LLMs, graph-based reasoning engines, and streaming feature pipelines that operate on billions of security events
• Own Evaluation & Reliability of AI systems—from prompt libraries and fine tuning to red-team testing, latency budgets, and fallback strategies
• Oversee cross-cutting concerns like observability, reliability, performance, security, and operational excellence in production environments
• Partner tightly with Product, Detection Engineering, and Customer Success to translate real-world attacker behavior into robust ML and rule-based detections, and define the technical roadmap
• Experiment with retrieval-augmented generation, tool-calling agents, and multi-modal models (text + logs + graphs) to maintain a competitive advantage
• Mentor and influence engineering teams on best practices in cloud architecture, reliability, and security-first development.

AI Architect

• Architect and lead the development of agentic AI systems that automate and augment finance workflows (e.g., forecasting, reporting, and decision support).
• Design and implement multi-agent systems leveraging LLMs, tool-use frameworks, and orchestration patterns (e.g., RAG, model chaining, dynamic prompting).
• Translate cutting-edge research in LLMs and agentic AI into scalable, production-ready solutions.
• Establish guardrails, evaluation frameworks, and responsible AI practices to ensure safe, compliant, and reliable outputs.
• Design fault-tolerant, observable agent systems with clear failure modes and recovery strategies.
• Lead the design and implementation of advanced predictive models, including time series forecasting and attrition prediction across customer segments.
• Develop interpretable, production-grade models that drive retention strategies and financial planning.
• Define and standardize evaluation metrics, validation frameworks, and monitoring systems for model performance and drift detection.
• Translate complex predictive insights into actionable recommendations for finance and business leaders.
• Design and build scalable AI/ML systems with a strong emphasis on software engineering best practices (modular design, APIs, CI/CD, testing).
• Lead end-to-end development from concept to production, ensuring robustness, scalability, and maintainability.
• Develop and integrate AI services into internal applications and workflows, including light front-end/back-end components where needed.
• Drive adoption of modern tooling (e.g., containerization, orchestration, cloud-native architectures).
• Establish and enforce MLOps best practices for deployment, monitoring, retraining, and governance of AI systems.
• Ensure systems meet enterprise standards for security, compliance (e.g., SOX), and auditability.
• Develop advanced feature engineering strategies capturing behavioral, financial, and temporal signals.
• Set technical direction for AI/ML initiatives across the finance organization.
• Lead complex, cross-functional projects and mentor other data specialists.
• Work alongside stakeholders across finance, IT, and product to adopt AI-driven solutions.
• Contribute to long-term AI strategy, identifying opportunities to drive efficiency and innovation.

See all jobs on JobTailor

AI Research Scientist, Applied AI

Machine Learning Engineer, Conversion ML

PharmSci Agentic and Generative AI Architect

Director of Forward Deployed AI Engineering

Lead AI Engineer

Machine Learning Engineer II

Senior Manager, AI Lead – Semantic Layer

AI Engineer

AI/ML Engineer – Automotive CRM

Principal Applied AI Engineer, Finance

Senior Software AI Engineer

AI Engineer – III

AI Engineer

Senior AI Engineer, Agentic Evaluation – V&V

AI/ML Engineer, AWS

Senior Manager, Machine Learning – Fraud, Waste and Abuse

AI Architect

Principal Applied AI Engineer, Finance