Learning and Experience Designer

Build and scale an integrated learning ecosystem for DataOps and Physical AI, serving internal production and specialist

teams as well as clients. Translate complex workflows, tooling, quality requirements, analytics skills, and partnership processes

into engaging, measurable learning experiences. Own instructional architecture, reusable content standards, and LMS learning

strategy in close collaboration with subject matter experts (SMEs).

Mission: Standardize onboarding and trainer delivery; improve engagement, proficiency, and production quality; enable clients

to use our tooling and partner effectively; and build data and technical capabilities across DataOps.

Key responsibilities

1. Standardized onboarding, e-learning, and trainer enablement

• Design role-based onboarding and e-learning for annotators, reviewers, auditors, Trainers, Solutions Architects, Quality

Leads, and other DataOps roles.

• Create modular, reusable curricula for common workflows that can be adapted quickly to client-specific SOPs, tools, and

quality standards.

• Build standardized, ready-to-deliver trainer packets: facilitator guides, slide decks, learner workbooks, exercises, sample

datasets or cases, assessment rubrics, answer keys, calibration examples, and certification criteria.

• Develop train-the-trainer guidance and version-controlled rollout kits to ensure consistent delivery across locations, shifts,

and projects.

• Create visual, interactive, scenario-based and accessible learning for a global workforce, including plain-language content

for varying English proficiency.

2. Data analysis training and cross-functional upskilling

• Partner with the Manager of DataOps to identify onboarding requirements, capability gaps, and prioritized upskilling needs

across the organization.

• Work closely with Data Analysts, Solutions Architects, Quality Leads, Trainers, and other SMEs to develop and validate

technical and analytical curricula.

• Develop practical data literacy and analysis learning: metric definitions, data quality, interpreting dashboards, trend analysis,

root-cause analysis, reporting, and evidence-based operational decisions.

• Build role-specific development pathways for Trainers, Solutions Architects, Quality Leads, and other teams, with hands-on

practice and measurable proficiency checks.

• Refresh learning priorities as tooling, customer requirements, and DataOps strategy evolve; measure whether new skills

transfer into day-to-day work.

3. Client onboarding and client-facing enablement

• Create client-facing tool onboarding: guided walkthroughs, feature tutorials, quick-start guides, release education, and

self-service modules.

• Develop materials explaining how clients partner with us, including scoping, access and data requirements, pilots, feedback

loops, escalation, quality governance, and operating cadence.

• Collaborate with Solutions Architects, Product/Tooling, Sales/Solutions, and account teams; obtain SME and commercial

approval before external publication.

4. LMS learning architecture and content governance

DataOps & Physical AI | Proposed job description 1• Own LMS learning pathways, course organization, role-based assignments, assessments, certification/recertification design,

completion criteria, and reporting requirements.

• Partner with IT/LMS administrators on platform configuration, access, integration, and technical maintenance rather than

independently owning all platform administration.

• Establish reusable templates, content taxonomy, metadata, version control, content ownership, review cycles, and

retirement rules.

• Connect LMS reporting with the DataOps training tracker and operational dashboards where feasible.

5. Engagement, learning effectiveness, and continuous improvement

• Increase training engagement through short, visual, interactive, accessible, and job-relevant experiences rather than

presentation-heavy content.

• Analyze engagement, completion, learner feedback, assessment performance, retention, and proficiency to improve

courses.

• Partner with Quality Leads and Data Analysts to distinguish knowledge gaps from tooling, guideline, and process failures.

• Evaluate impact on time to proficiency, first-pass quality, recurring errors, client onboarding friction, and retraining.

6. Physical AI technical workflow enablement and innovation

• Translate SME knowledge into learning for 2D/3D annotation, LiDAR/point clouds, AV perception and simulation, robotics

video, temporal segmentation, multimodal data, telemetry, and human-in-the-loop workflows.

• Maintain rapid learning update processes for new workflows, guideline changes, tool releases, and quality calibrations.

• Explore AI-assisted authoring, multilingual content, adaptive learning, and simulated practice, subject to human technical

validation.

Cross-functional working model

Learning Experience Designer: Owns learning design, trainer packets, LMS learning strategy, engagement, and content

governance.

Manager of DataOps: Prioritizes onboarding and upskilling needs, business outcomes, and capability roadmap.

Trainers: Deliver training, coach learners, and test the usability of trainer packets.

Solutions Architects / Data Analysts / Product / Tooling: Provide and validate domain-specific technical and analytical

content.

Quality Leads / Delivery / Account Teams: Identify performance gaps, validate readiness, and support client enablement.

IT / LMS administrators: Support platform administration and integrations.

Required qualifications

• 3-5+ years in instructional design, learning experience design, educational technology, or technical/operational enablement,

or equivalent experience.

• Demonstrated ability to design scalable curricula, standardized facilitator resources, interactive e-learning, scenario-based

assessments, and role-based onboarding.

• Experience with tools such as Articulate 360 (Rise/Storyline), Captivate, or comparable platforms, plus LMS course

management and learning analytics.

• Ability to partner with technical SMEs to teach data literacy and analytical concepts accurately, even without being the

primary data analyst.

• Strong visual communication, accessible technical writing, stakeholder management, and project execution skills.

Preferred qualifications

• Experience in AI data operations, robotics, autonomous vehicles, computer vision, LiDAR, or adjacent technical workflows.

DataOps & Physical AI | Proposed job description 2• Experience with client-facing product education, global production teams, multilingual learning, and competency-based

certification.

• Familiarity with operational dashboards, data analysis workflows, knowledge management, and AI-assisted content

development.

Success measures (baseline first; jointly agreed)

Measure Illustrative outcome

Time to proficiency 20-30% reduction where feasible

Trainer standardization Approved trainer packets for priority workflows

Workflow coverage Reusable learning paths for prioritized common workflows

DataOps upskilling Role-based pathways and measured proficiency gains

Client onboarding Higher self-service completion; fewer repeat questions

Learning engagement Improved participation, completion, and feedback

Quality and readiness Reduced repeat knowledge-related errors

LMS and governance Role-based reporting; owned and versioned content

Targets are planning examples, not commitments; measure against an agreed baseline and distinguish outcomes shared with operations from

deliverables directly controlled by this role.

First 90 days

Days 1-30 | Discover and prioritize: Audit internal and client learning, LMS capabilities, existing trainer materials, common

workflows, and engagement data. Work with the Manager of DataOps and SMEs to identify priority onboarding, data analysis,

and cross-functional upskilling needs.

Days 31-60 | Standardize and pilot: Deliver approved standardized instructional templates and complete, ready-to-use trainer

packets for selected priority workflows. Pilot one reusable internal e-learning pathway and one client-facing onboarding

experience. Draft the DataOps data literacy/analysis curriculum and role-specific upskilling roadmap with SMEs; define LMS

pathways, assessments, and reporting requirements.

Days 61-90 | Validate and scale: Pilot trainer packets with Trainers, refine based on feedback and learner results, and publish

a prioritized rollout plan for common workflows. Launch an initial data analysis or role-based upskilling module; evaluate

engagement, proficiency, and client onboarding results. Establish content governance, certification standards, and the LMS

scaling roadmap.

Proposed career pathway

Individual contributor track: Junior Instructional Designer → Instructional Designer → Learning Experience Designer →

Senior Learning Experience Designer → Principal Learning Experience Designer.

People leadership track: Learning Experience Designer / Senior Learning Experience Designer → Manager, Learning

Experience Design → Director, Learning & Enablement (future, based on organizational scale).

Career architecture note: Principal and Manager are parallel progression options, not sequential requirements. The Principal

track emphasizes learning architecture, complex programs, standards, and strategic influence; the Manager track adds people

leadership, capacity planning, prioritization, and team accountability. Exact level criteria should be defined internally.