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

AI Engineer

• Innovate in a Startup-Like Environment; developing AI-powered systems for accounting workflows
• Collaborate Across Functions; working with AI/ML engineers, frontend developers, product managers, and designers
• Help to build core AI interfaces; such as Conversations, Workspaces, Grid, and Knowledge Base
• Ramp up with a mentor; transitioning into contributing independently with a focus on quality, testing, and user experience

AI Engineer, New Grad

• Design and implement scalable AI architectures, including: LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, agentic / multi-step workflows, vector search and retrieval services, model serving and inference layers.
• Establish reusable platform services, APIs, and design patterns to accelerate delivery across multiple teams.
• Build and operationalize AI delivery pipelines: CI/CD for models, prompts, and workflows, prompt versioning and lifecycle management, evaluation and testing frameworks, model and artifact registries.
• Implement monitoring for response quality and hallucination control, latency, throughput, and system reliability, cost observability and optimization.
• Design AI systems with strong controls for data security and privacy auditability and traceability, entitlements and access controls, data lineage and governance.
• Lead delivery of production-grade AI systems with a focus on scalability and reliability, latency and performance optimization, operational readiness and support.
• Partner closely with data engineering and platform teams to integrate AI capabilities with Snowflake and Databricks environments, structured and unstructured data pipelines, APIs and enterprise data services, semantic and knowledge-layer architectures.

Director, AI Engineering – Agentic Platform

• Design and develop scalable solutions utilizing AI tools and machine learning models
• Conduct research, testing, and analysis to develop machine learning algorithms and predictive models
• Leverage big data processing and storage technologies to create datasets, prototypes, and data-driven solutions
• Perform model training, evaluation, integration, testing, tuning, and ongoing performance monitoring
• Lead and/or develop project requirements from inception through completion for complex and highly complex programs, while providing strategic guidance, technical leadership, and expert consultation to program and project teams
• Demonstrate continuous learning and adaptability within large-scale enterprise IT environments
• Troubleshoot software applications and technical implementations across complex enterprise ecosystems
• Design, develop, and integrate APIs to support business and technical requirements
• Query, manage, and analyze data using both SQL and NoSQL database technologies

Senior AI Engineer/Architect

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

• Lead the strategic development and growth of Circuit Check's most significant customer relationships in the AI/ML data center infrastructure market
• Architect and execute multi-year account strategies that drive large-scale program wins, deepen executive-level partnerships, and position the company as the preferred technology partner for hyperscale and enterprise AI customers
• Serve as the primary executive-level interface with strategic accounts, building trusted advisor relationships with VP and C-suite decision-makers at hyperscale cloud providers, AI chipmakers, and data center operators
• Coordinate engineering, product management and operations around customer requirements
• Partner with Product Line Managers to translate customer insights into product roadmap inputs aligned with AI/ML market direction
• Identify patterns in AI/ML technology adoption, supply chain shifts, and customer investment priorities that create strategic openings for Circuit Check

Director, Strategic Accounts – AI/ML Infrastructure

• join a Data Sciences team responsible for creating personalized recommendations on Target.com and the Target App
• play a crucial role in designing, implementing, and optimizing production machine learning solutions
• conduct training sessions
• present work to technical and non-technical peers/leaders
• build knowledge on business priorities/strategic goals and leverage this knowledge while building requirements and solutions for each business need

Lead Machine Learning Engineer – Item RecSys

• Build & Integrate: Design and implement full-stack features that utilize LLMs to solve user problems.
• Streaming Interfaces: Build low-latency interfaces that stream text to the client.
• Roadmap Ownership: Leading a team of developers to build new features.
• Write robust, high-performance Go code to orchestrate AI services.
• Design and implement RESTful and gRPC APIs.
• Collaborate on integrating Kafka and Redis to build asynchronous message processing pipelines.
• Use AI coding assistants to accelerate code generation, testing, and documentation.
• Critically evaluate and debug AI-generated code.
• Refine system prompts and write automated evaluations for AI performance.

Staff AI Engineer

• Turn quantitative prototypes into reusable tools, services, packaged desktop applications, interfaces, and workflow products that can move from internal demo to client pilot to scaled offer.
• Ship across both cloud-hosted services and locally distributed desktop applications, including Electron-based apps when the workflow or client environment calls for it.
• Build enterprise hardening into the productization layer, including authentication, role-based access control (RBAC), observability, security, release quality, cost controls, and deployment discipline.
• Build evaluation, regression, and release discipline into the productization layer so model logic and agent behavior remain measurable as systems change.
• Work closely with the quant lead so model logic, evaluation intent, and governance requirements survive the move into production.
• Make pragmatic architecture choices across large language models (LLMs), deterministic rules, and hybrid systems based on value, latency, cost, and reliability.
• Help shape repeatable build patterns so strong prototypes become faster, more reliable, and more reusable over time.

QuantAI Engineer – Trading, Investment & Optimization

• Champion Agile principles and practices across AI and data-focused teams, driving adoption and continuous improvement
• Coach product owners, engineers, and business stakeholders on Agile frameworks such as Scrum, Kanban, or SAFe
• Facilitate core Agile ceremonies (sprint planning, stand-ups, reviews, retrospectives) to ensure effective team execution
• Foster a culture of transparency, collaboration, and iterative delivery across cross-functional teams
• Partner with technical teams (data science, ML engineering) and business leaders to translate AI use cases into actionable backlogs and sprint goals
• Guide teams in delivering AI/ML solutions iteratively, accounting for experimentation, model training, and validation cycles
• Align delivery plans with business objectives, ensuring AI initiatives provide measurable value
• Support prioritization of work based on business impact, technical dependencies, and data readiness
• Enable collaboration across diverse teams, including data scientists, engineers, product managers, and domain experts
• Remove impediments and escalate risks impacting delivery timelines or outcomes
• Act as a liaison between technical and non-technical stakeholders to ensure shared understanding
• Define and track Agile and AI-specific performance metrics (e.g., velocity, cycle time, model performance indicators)
• Ensure AI delivery adheres to governance, compliance, and ethical standards (e.g., bias, data privacy)
• Facilitate retrospectives and apply learnings to improve delivery effectiveness and team maturity
• Promote data-driven decision-making and experimentation
• Support enterprise Agile transformation efforts, particularly within AI and digital initiatives
• Influence leaders and stakeholders to adopt Agile ways of working and AI-enabled delivery models
• Drive mindset shifts toward iterative development, experimentation, and continuous learning

AI Platform Agilist

• 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

• Design, implement, and optimize production machine learning solutions
• Join a Data Sciences team responsible for creating and optimizing the data and models used to build a world-class merchandising product
• Conduct training sessions
• Present work to technical and non-technical peers/leaders
• Build knowledge on business priorities/strategic goals
• Leverage knowledge while building requirements and solutions for each business need

Lead Machine Learning Engineer – Merchandising AI, ML Ops

• Lead the design and development of ML pipelines for advanced AI algorithms and ML models to solve complex problems across diverse business domains.
• Collaborate on development of ML architecture and implement robust, efficient, and scalable AI systems that integrate seamlessly with existing infrastructure and platforms.
• Collaborate with research scientists, data scientists, and software engineers to translate research findings into practical, scalable AI solutions.
• Evaluate and experiment with emerging AI technologies, frameworks, and methodologies to stay at the forefront of innovation.
• Provide technical guidance and mentorship to junior team members, fostering a culture of continuous learning and growth.
• Collaborate with stakeholders to understand requirements, gather feedback, and iterate on AI solutions to ensure alignment with business objectives.
• Deploy, test, and optimize ML models and data pipelines in production environments.
• Perform model tuning, prompt tuning, and other ML optimization processes alongside other technical experts to maximize the mission impact of the AI product.
• Participate in the delivery, evaluation, and maintenance of enterprise products, ensuring they meet high-quality standards.

Machine Learning Engineering Consultant

• You will own a product capability end to end, from architectural decisions through production outcomes, cutting through every layer: entry points, orchestration, intelligence, tools, data, and observability.
• Your work directly shapes how Augeo's clients engage tens of millions of users.
• You will build and ship mission-critical AI agents that drive revenue growth across finance, healthcare, telecom, and commerce.
• Define how AI agents are built at Augeo.
• Drive the Agent Development Life Cycle (ADLC) from initial pilot through production iteration, setting the standard for how agents are developed, tested, evaluated, and improved.
• Build the shared platform: agent orchestration, agentic workflows, ML models, and data infrastructure that every product team at Augeo builds on.
• Work across Augeo with engineers, product leads, and end users to understand needs, close feedback loops, and ensure the platform compounds with every interaction.
• Shape the team. Participate in hiring and define the culture of an engineering org that is AI-native from day one.

AI Architect

• Support development of computer vision and machine learning (ML) algorithms capable of detection, classifying, localizing, and tracking objects of interest from a variety of stationary and mobile sensor platforms
• Implement ML algorithms and validate designs through a series of purpose-designed experiments
• Analyze ML algorithms to solve a given problem and rank them by their success probability on new data
• Set objectives and develop models that help achieve them, along with metrics to track their progress
• Select appropriate datasets and data representations
• Analyze errors of the model and design strategies to overcome them
• Write and test software to support the integration of machine learning algorithms into aircraft or other systems

Machine Learning Engineer II

• 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 iterate on AI Assistants integrated with enterprise tools including Jira, Slack, and internal data sources
• Develop and maintain prompt engineering frameworks, evaluation pipelines, and feedback loops to continuously improve assistant quality and reliability
• Build and maintain APIs, backend services, and integrations that connect AI capabilities to the tools teams already use
• Collaborate with AI Ops, security, and platform engineering teams to ensure systems are production-grade, secure, and observable
• Contribute to a repeatable framework for building new AI Assistants as the program expands
• Evaluate new models, tools, and libraries and make informed recommendations on when and how to adopt them
• Identify and mitigate risks related to AI outputs, data handling, and responsible use
• Contribute to technical documentation, runbooks, and onboarding materials that help the team scale what gets built

• Translate Data Science Algorithms and Methods in R&D stage to stable, durable, and extensible software
• Provide thought leadership on best algorithm development practices and hosting technologies
• Engineer fully featured and real-time modeling pipelines that leverage the best of containerization, data storage, streaming, and MPP technologies
• Engineer and maintain ecosystem of modeling micro-services utilizing best of on-prem and cloud-based elastic compute
• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure in collaboration with stakeholders
• Design and engineer high performance computing platforms and algorithms for a wide variety of data
• Work with real time data sources and have a knack for identifying scalable data access patterns
• Work closely with R&D data science teams to productionize successful models in an on-demand environment

Principal Machine Learning Engineer

• Design scalable, secure enterprise AI and GenAI architectures aligned to business strategy
• Lead solutioning and hands‑on prototyping of AI systems, including LLM‑based workflows
• Translate business needs into architecture diagrams, solution designs, and integration patterns
• Partner with data, platform, security, and application teams to embed AI into core enterprise systems
• Support delivery teams with implementation guidance, architectural tradeoffs, and best practices
• Contribute to enterprise AI platform development, reusable patterns, and standards
• Apply AI governance, risk, and security guardrails across all solutions
• Engage with external AI partners and assist in evaluating proof‑of‑value initiatives
• Present architectures and solution designs for enterprise review and alignment

Senior AI Architect

• Designs, develops, and deploys AI/ML solutions that support planning, forecasting, construction optimization, and risk reduction for renewable energy projects, owning delivery for defined workstreams or products.
• Leads implementation of MLOps pipelines, model lifecycle processes, and production monitoring for assigned solutions, ensuring adherence to security, integration, and performance requirements.
• Partners with IT and data teams to design and implement secure, scalable, and integrated AI solutions within established enterprise architectures and standards.
• Works with business stakeholders to define requirements and translate them into actionable technical designs; recommends solution approaches and design decisions for review and alignment with AI leadership team.

See all jobs on JobTailor

AI Research Scientist, Applied AI

AI Engineer, New Grad

Director, AI Engineering – Agentic Platform

Senior AI Engineer/Architect

Machine Learning Engineer, Conversion ML

Director, Strategic Accounts – AI/ML Infrastructure

Lead Machine Learning Engineer – Item RecSys

Staff AI Engineer

QuantAI Engineer – Trading, Investment & Optimization

AI Platform Agilist

Senior Manager, AI Lead – Semantic Layer

Lead Machine Learning Engineer – Merchandising AI, ML Ops

Machine Learning Engineering Consultant

AI Architect

Machine Learning Engineer II

Principal Artificial Intelligence Engineer

Staff AI Engineer

Principal Machine Learning Engineer

Senior AI Architect

Senior AI Engineer