AI Engineer

• Take end-to-end ownership of key product outcomes, including platform adoption, user engagement, scalability, and business impact
• Serve as the primary liaison between engineering, stakeholders, customers, and internal production teams to ensure strong alignment
• Define and evolve core product experiences, including personalized workspaces, intelligent agents, automated workflows, and contextual artifacts
• Specify requirements that enable users to deploy custom tools and automations using natural language
• Ensure best-in-class integration with Autodesk and third-party products and services used in studio pipelines
• Drive data-informed prioritization
• Clearly communicate complex AI concepts and workflows to both technical and creative audiences

Senior Product Manager, Media & Entertainment AI Platform

Machine Learning Engineer

• Lead the architecture, design, and evolution of production-grade applied AI systems, including LLM, ML, retrieval, agentic, and automation-based capabilities
• Own the most complex technical problems across the stack, from model behavior and system design through infrastructure, reliability, quality, and operational scale
• Set technical direction for how AI solutions are built, integrated, evaluated, secured, monitored, and improved over time
• Drive platform and systems decisions across AWS environments, including performance, resilience, observability, security, and cost optimization
• Shape the design and optimization of data systems, including relational and graph databases, data access patterns, schema strategy, and query performance
• Lead infrastructure thinking for distributed and cross-regional services where availability, latency, failover, or scale require stronger architectural rigor
• Establish stronger engineering quality practices, including validation frameworks, automated testing, release discipline, QA strategy, and incident response maturity
• Translate ambiguous business or product opportunities into technical strategy, execution plans, and measurable outcomes
• Partner across engineering, product, data science, analytics, and business stakeholders to align priorities and drive decisions without requiring formal authority
• Provide technical leadership across a team of roughly 20 people by mentoring engineers, reviewing designs, clarifying tradeoffs, and raising the bar for execution
• Create structure in ambiguous environments by defining milestones, surfacing risks, tracking dependencies, and ensuring follow-through across distributed teams

Principal Applied AI/ML Engineer

• Own the technical architecture of the XR Training Academy platform
• Define and maintain architecture standards across frontend, backend, AI, cloud, and Unity integrations
• Create technical roadmaps aligned with product goals
• Lead technical planning and solution design sessions
• Review technical approaches and ensure alignment with business requirements
• Prevent over-engineering and unnecessary technical complexity
• Design scalable cloud-native architectures
• Define APIs, integration patterns, and service boundaries
• Ensure performance, security, maintainability, and scalability considerations are built into solutions
• Guide AI architecture and integration decisions
• Evaluate and recommend technology choices
• Translate product requirements into technical implementation plans
• Break epics and features into technical workstreams
• Define acceptance criteria and technical success metrics
• Identify technical risks and mitigation strategies
• Support estimation and sprint planning activities
• Guide the implementation of AI-powered experiences
• Evaluate and integrate LLMs, AI services, and agent-based workflows
• Establish best practices for AI architecture, prompting, evaluation, and governance
• Coach team members on the effective use of AI-assisted development
• Contribute production-quality code
• Build backend services, APIs, integrations, and cloud infrastructure
• Support frontend and platform development as required
• Review pull requests and mentor developers
• Troubleshoot technical issues and support delivery teams
• Foster strong communication and accountability

Technical Lead, Full-Stack, AI Platforms

• Drives the overall software development lifecycle including working across functional teams
• Translating user requirements into technical specifications
• Writing code and managing the preparation of design specifications
• Supports system design and advises on security requirements
• Ensures that code/configurations adhere to security and performance standards
• Evaluates new technologies and their impacts on processes

Software Engineer – MLOps Technology

• Contribute to the design, development, and testing of various inference optimization algorithms
• Design, implement, and optimize model compression pipelines
• Develop and maintain speculative decoding frameworks
• Collaborate closely with research scientists to translate experimental ideas into robust, production-ready systems
• Profile and optimize end-to-end LLM performance
• Benchmark, evaluate, and implement strategies for optimal performance on target hardware
• Build tools to streamline model training, evaluation, and deployment
• Participate in technical design discussions and propose innovative solutions
• Contribute to open-source projects, code reviews, and documentation
• Mentor and guide team members, fostering a culture of continuous learning and innovation
• Stay current with LLM architectures, inference optimizations, and hardware advancements.

Senior Machine Learning Engineer

• Pipeline Development: Build and execute outbound sequences targeting senior decision-makers.
• Qualification: Run discovery calls to separate real opportunities from the noise.
• Strategy: Work with the founders to refine our ICP and messaging based on real-world feedback.
• Stack Optimization: Leverage modern tooling (Clay, Sales Nav, etc.) to automate the manual parts of prospecting so you can focus on the strategy.

Business Development, AI Platform

Computer Vision Engineer

• Design AI algorithms for a broad range of applications relating to the site-monitoring industry (facial recognition, OCR, object detection, object tracking)
• Regular maintenance and improvement of existing services
• Ensuring functionality and optimization of services via regular testing and feedback
• Researching new technology and tools
• Communicating in a clear and structured way with colleagues and managers to ensure optimal collaboration and teamwork
• Following the agreed team or project processes and procedures, including reporting to superiors on status of project/release/task efforts and key issues

Senior Computer Vision Engineer

• Lead the migration of a legacy front-end from vanilla JavaScript / jQuery / HTML5 / CSS to a modern React-based stack.
• Help maintain and enhance an existing Angular CLI front-end on a separate product.
• Develop overall concepts for new and existing product features and components across our AI-powered product portfolio.
• Contribute to back-end services in Python (agent workflows) and Node.js as priorities shift.
• Write production-ready, secure, and performant code, and apply secure coding practices throughout design and delivery.
• Own your work end-to-end through our CI/CD pipeline on GitLab — branching, merge requests, code review, build, test, and deploy — and track delivery in Jira.

Senior Full Stack AI Developer

• Define and drive the technical vision for ML solutions across products and platforms
• Own the end-to-end software development lifecycle — from design and code reviews through to deployment and operations
• Architect high-performance, scalable microservices, including synchronous and asynchronous web services
• Build real-time inference pipelines for complex models using Triton, TensorRT, and mixed-precision computing
• Mentor engineers, set technical direction, and foster a strong team culture
• Champion engineering excellence, system resilience, and continuous operational improvement

Principal Machine Learning Engineer, General AI, ML, Big Data

• Drive the operational excellence of our AI/ML Platform
• Design and implement automated deployment pipelines
• Collaborate with teams to maintain scalable infrastructure
• Develop and maintain monitoring and logging systems
• Work closely with data engineers for efficient data pipelines
• Implement version control systems for machine learning models
• Contribute to the governance of machine learning models
• Enforce security best practices in MLOps
• Identify opportunities for process automation
• Contribute to incident response and system recovery

Senior Machine Learning Operations Developer – AI/ML Platform

AI Research Scientist

• Post-training for model development
• Develop novel algorithms that improve model reliability, controllability, and alignment
• Design and run experiments that shape model behavior
• Partner with infrastructure teams to build scalable workflows
• Contribute to publications, patents, and external research visibility

Principal AI Research Scientist – Post-Training Alignment

• Design, construct, and manage robust data pipelines for the training, validation, and continuous retraining of Large Quantitative Models (LQMs) and agentic frameworks.
• Develop, implement, and rigorously test novel ML models and algorithms, defining appropriate metrics to ensure model performance aligns with high-level product objectives.
• Contribute to the efforts in cleaning, transforming, and engineering features from complex and large-scale datasets to optimize LQM performance and predictive accuracy.
• Conduct deep analysis of model behavior, performance, and failure modes, tuning hyper-parameters and optimizing model architecture for efficiency, speed, and accuracy in a production context.
• Collaborate closely with AI researchers, product managers, and SWEs to translate high-level business objectives into actionable ML development and deployment roadmaps.
• Champion and enforce exceptional engineering standards for code quality, system efficiency, and security in a prototyping environment.

Senior Machine Learning Engineer – AI Generation Engine

• Design, construct, and manage robust data pipelines for the training, validation, and continuous retraining of Large Quantitative Models (LQMs) and agentic frameworks
• Develop, implement, and rigorously test novel ML models and algorithms, defining appropriate metrics to ensure model performance aligns with high-level product objectives
• Lead the effort in cleaning, transforming, and engineering features from complex and large-scale datasets to optimize LQM performance and predictive accuracy
• Conduct deep analysis of model behavior, performance, and failure modes, tuning hyper-parameters and optimizing model architecture for efficiency, speed, and accuracy in a production context
• Collaborate closely with AI researchers, product managers, and SWEs to translate high-level business objectives into actionable ML development and deployment roadmaps
• Champion and enforce exceptional engineering standards for code quality, system efficiency, and security in a prototyping environment
• Drive technical execution with high autonomy, making critical design and implementation decisions independently

Staff Machine Learning Engineer, AI Generation Engine

• You'll manage and scale paid campaigns across Meta, TikTok, and Google — and help expand into newer channels like Reddit, Snapchat, and Quora.
• Day to day that means setting up and optimizing campaigns, translating performance data into creative briefs, keeping reporting accurate and up to date, and flagging what's working and what isn't.
• If you study ads while scrolling, save hooks that catch your eye, and naturally think about why certain creatives convert — this role was built for you.

Performance Marketer, Paid Acquisition

• Build and deliver AI solutions: Take ownership of developing high-performance AI features to help users detect, triage, and resolve incidents using observability data and tools.
• Rapid experimentation and iteration: Implement a highly iterative process where you quickly prototype, test, and validate with real users, including shipping and evolving LLM- or agent-powered workflows for incident lifecycle management and automated analysis tasks.
• Collaborate cross-functionally: Work with data analysts, product managers, and designers to shape AI-driven product features, including integration of agentic components with internal tools, alerting systems, runbooks, and developer workflows.
• Utilize AI tools effectively: Use AI and automation tools to enhance both product functionality and your own development workflows.
• Effective communication: You’ll be working in a highly dynamic and collaborative environment, so we need someone who can communicate effectively and contribute across teams.
• Ownership and impact: Take full ownership of the AI solutions you develop, ensuring they are not only innovative but also scalable, maintainable, and aligned with real user workflows.

Senior AI Engineer – Grafana Ops, AI/ML

• Translate business, data, and workflow problems into structured solutions and drive delivery end to end
• Design, build, and deploy AI-powered applications, models, dashboards, and automation tools
• Partner with trading, sales, risk, finance, operations, and technology teams to identify priorities and deliver measurable impact
• Apply strong engineering discipline, including testing, version control, monitoring, and iteration based on feedback and usage
• Communicate technical concepts and analytical findings clearly to both technical and non-technical stakeholders

Forward-Deployed AI Engineering Specialist

• Define end-to-end architecture for AI/ML solutions
• Serve as a strategic advisor to clients
• Architect scalable solutions using cloud-native AI tools
• Lead integration of Generative AI into enterprise applications
• Design retrieval-augmented generation (RAG) systems
• Guide teams on MLOps frameworks
• Evaluate build-vs-buy decisions for AI tools
• Evaluate emerging technologies in AI space
• Mentor technical teams and strengthen organizational expertise
• Collaborate with leadership to align strategies with business objectives
• Represent the organization at industry events
• Drive cross-functional collaboration to identify opportunities

Architect / Principal, AI ML Engineer

• You will develop AI systems that automate dispute and chargeback handling using structured evidence and business logic, creating a better experience for our customers.
• You will build models that automate refunds, getting money back to our customers faster.
• You will build and maintain evidence extraction pipelines that process unstructured data using LLM-powered workflows to produce structured, actionable outputs.
• You will prototype new modeling ideas, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.
• You will collaborate across Engineering, Servicing Operations, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences.

Machine Learning Engineer II

• Design and improve Paxos’s AI workflows. Own the prompt chains, agent patterns, structured-output schemas, evals, and test cases that power customer implementations.
• Turn reimbursement complexity into testable workflow logic. Translate payer rules, document requirements, clinical evidence standards, and operational edge cases into clear AI system behavior.
• Own AI quality and reliability. Analyze failures, improve prompts and schemas, and build evals that catch issues before they reach production.
• Work directly with customers during implementation. Clarify requirements, understand edge cases, and translate what we learn into workflows and reusable Paxos capabilities.
• Document how the system works. Write workflow specs, test plans, prompt/eval documentation, and implementation notes that help the team build and improve faster.

Founding AI Lead

• Embed directly with clients to discover operational pain points, map workflows, and identify high-ROI opportunities for AI and automation.
• Design and build AI agents using frameworks such as LangChain, LangGraph, AutoGen, or similar, including tool-use, memory, and multi-agent orchestration patterns.
• Develop AI-powered workflows that integrate with clients' existing systems (CRMs, ERPs, cloud platforms, communication tools, etc.)
• Engineer automation solutions across areas such as document processing, data extraction, reporting, customer communication, and internal operations.
• Own the full delivery lifecycle from scoping and architecture to build, testing, handoff, and lightweight documentation.
• Train and enable client teams to maintain and iterate on solutions post-deployment.
• Serve as a trusted technical advisor, translating complex AI capabilities into clear business value for non-technical stakeholders.
• Contribute internally by codifying reusable components, playbooks, and delivery templates that scale across the practice.

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