AI / Machine Learning Engineering — P7
DATASC.AIMACHIN97C0.P7
AI / Machine Learning Engineering — builds, trains, deploys, and operates machine learning models and the production pipelines that serve them. Distinct from Data Science (hypothesis-driven statistical analysis and experimentation) and Data Engineering (data platform/pipeline plumbing) in that the core deliverable is production-grade ML systems: model architecture, training/evaluation, MLOps, deployment to cloud, and scaling of inference. Spans data preprocessing and feature engineering through deep learning (transformers, generative AI), distributed systems for large-scale ML, and the technical leadership that aligns ML capability with business objectives.
AI / Machine Learning Engineering — builds, trains, deploys, and operates machine learning models and the production pipelines that serve them. Distinct from Data Science (hypothesis-driven statistical analysis and experimentation) and Data Engineering (data platform/pipeline plumbing) in that the core deliverable is production-grade ML systems: model architecture, training/evaluation, MLOps, deployment to cloud, and scaling of inference. Spans data preprocessing and feature engineering through deep learning (transformers, generative AI), distributed systems for large-scale ML, and the technical leadership that aligns ML capability with business objectives.
Focus — AI / Machine Learning Engineering
AI / Machine Learning Engineering — builds, trains, deploys, and operates machine learning models and the production pipelines that serve them. Distinct from Data Science (hypothesis-driven statistical analysis and experimentation) and Data Engineering (data platform/pipeline plumbing) in that the core deliverable is production-grade ML systems: model architecture, training/evaluation, MLOps, deployment to cloud, and scaling of inference. Spans data preprocessing and feature engineering through deep learning (transformers, generative AI), distributed systems for large-scale ML, and the technical leadership that aligns ML capability with business objectives.
Material PAY and SKILL differential vs the function baseline.
Responsibilities by level
What this person actually does at each level on the professional track — escalating scope, not one generic blob. Your level is highlighted.
- Performs data preprocessing — cleans and transforms raw data into formats suitable for ML algorithms, handling missing values and engineering features under guidance.
- Trains models and evaluates model performance on defined segments of a larger project with senior oversight.
- Codes with guidance and documents results, shipping small features with support.
- Works with large datasets to ensure data quality, applying foundational algorithms and ML frameworks.
- Learns model deployment best practices, tools, and ML frameworks while assisting senior engineers.
- Takes an active role in end-to-end model development with day-to-day independence, planning own work against project milestones.
- Designs and selects appropriate algorithms for the problem at hand and conducts A/B tests to compare approaches.
- Leads model deployment to production and troubleshoots model performance issues encountered in live environments.
- Translates prototype models into production-ready solutions, ensuring smooth integration between ML components and broader software infrastructure.
- Serves as the bridge between junior engineers and senior production staff, coordinating project activities and mentoring associates.
- Architects scalable ML systems and optimizes algorithms for scalability and speed across complex, functionally impactful projects.
- Manages the complete lifecycle of models from data ingestion through serving, selecting methods and tools independently.
- Guides teams on advanced techniques and acts as go-to problem solver for thorny production and modeling issues.
- Collaborates across product, engineering, and stakeholder groups as a subject matter expert, advocating for improvements to product quality, security, and performance.
- Leads model deployment efforts and may supervise or lead the work of other engineers on complex initiatives.
- Drives research and development on advanced techniques (e.g., transformers, generative AI) and aligns ML goals with broad business objectives.
- Acts independently on strategic, broad, or unique ML assignments that contribute to company objectives.
- Conducts experiments to evaluate model performance in real-world conditions and resolves intangible, high-ambiguity technical problems.
- Leads recruitment and mentoring efforts and builds influential networks across functions, serving as a technical spokesperson on ML matters.
- Keeps the organization current on the latest advancements and sets technical direction for complex, high-scope ML systems.
- Shapes the organization's machine learning direction and drives measurable business impact with ML initiatives.
- Designs model architectures and defines field-shaping approaches to organization-wide ML problems with full independence.
- Partners with senior management to identify opportunities for leveraging ML and data science to drive business growth.
- Provides insights and recommendations that shape the overall technical direction of the company and guides the rest of the ML team.
- Provides high-level mentorship to senior engineers and influences peer professionals as a recognized internal thought leader.
- Sets long-term ML roadmaps and anticipates emerging challenges, influencing company-wide strategy and industry practices.
- Develops new models, architectures, or techniques to solve precedent-free ML problems with broad business and industry consequences.
- Operates with complete independence, setting direction for ML functions and cross-organizational initiatives.
- Contributes to the ML community by publishing research papers, presenting at conferences, and participating in open-source projects.
- Networks with executives, industry leaders, and external stakeholders, persuading and educating senior decision-makers on strategic ML priorities.
Level guidelines
The universal leveling rubric applied to this function — how scope, complexity, collaboration, and experience step up across levels.
| Level | Knowledge & Application | Complexity & Problem Solving | Collaboration & Interaction | Typical Degree & Years |
|---|---|---|---|---|
| P2 | Applies foundational ML algorithms, frameworks, and data preprocessing techniques to narrow, well-defined pieces of larger projects under detailed guidance. | Solves technical problems of moderate scope and complexity; exercises judgment in familiar modeling and data-cleaning contexts. | Builds productive working relationships within the project team; assists senior engineers and documents results. | 2+ years with a BA/BS, or an MS/PhD with no prior experience. |
| P3 | Applies end-to-end model development knowledge — algorithm selection, A/B testing, deployment — across diverse problems with moderate independence. | Evaluates identifiable factors to design models and troubleshoot production performance; plans own work day-to-day. | Networks with senior professionals, bridges junior and senior staff, and may coordinate project activities. | 5+ years (BA), 3+ years (MA), or PhD without prior experience. |
| P4 | Applies in-depth expertise in scalable ML system architecture, MLOps, and advanced techniques to complex, functionally impactful work. | Performs in-depth analysis of complex variables; selects methods independently and resolves thorny modeling and infrastructure issues. | Coordinates across product, engineering, and stakeholder groups as a subject matter expert; may lead or supervise project teams. | 8+ years, often with graduate education in a quantitative or CS field. |
| P5 | Applies expert, often unique knowledge of advanced ML and generative AI to strategic assignments that contribute to company objectives. | Resolves intangible, high-ambiguity problems with high independence; drives R&D into novel techniques. | Builds influential cross-functional networks and acts as a technical spokesperson; may supervise others on special tasks. | 12+ years with extensive ML and deep learning expertise. |
| P6 | Applies visionary, field-shaping expertise to define organization-wide ML architecture and direction. | Solves field-shaping problems with full independence; translates ML capability into business strategy. | Influences company and peer professionals as a recognized thought leader; partners with senior management. | 15+ years as a principal ML expert; often PhD plus industry leadership. |
| P7 | Advances the field — develops new theories, models, and technologies that influence company-wide and industry ML practice. | Solves ambiguous, precedent-free problems with broad business and industry consequences; defines long-term roadmaps. | Networks with executives, regulators, and industry leaders; persuades and educates senior stakeholders; mentors senior professionals. | 20+ years, or equivalent recognition through patents, publications, or significant industry contributions. |
Skills
Focus-specific skills the role applies — the relevance layer beyond the occupational base.
- Data preprocessing & feature engineering
- Cleans and transforms raw data into a format suitable for ML algorithms, ensuring data quality, handling missing values, and engineering features.
- Model training & evaluation
- Builds, trains, and assesses the performance of machine learning models.
- Algorithm design & selection
- Designs and selects appropriate algorithms and optimizes them for scalability and speed.
- MLOps
- Applies practices for deploying, monitoring, and scaling ML models in production, including CI/CD pipelines, automated testing, and performance monitoring.
- Model deployment
- Deploys ML solutions to production environments, including public clouds such as AWS, Azure, or GCP.
- ML pipeline development
- Designs and implements comprehensive pipelines covering data ingestion, feature engineering, model training, evaluation, and serving.
- Programming (Python, SQL, Java)
- Writes performant, resilient, maintainable code; Python dominates for data wrangling and model deployment, with SQL and Java also in demand.
- Deep learning
- Develops and implements deep learning and neural network models, including transformers and generative AI.
- Distributed systems & big data architecture
- Applies understanding of distributed systems and big data architectures to support large-scale ML.
- Cloud computing
- Uses cloud platforms and ML services such as AWS SageMaker, Google Cloud Vertex AI, or Azure Machine Learning.
- Containerization
- Packages and scales model deployments using containers and orchestration tools.
- Cross-functional collaboration
- Works with product managers, engineers, and stakeholders as a specialist and subject matter expert.
- Mentoring & technical leadership
- Guides and mentors junior engineers and leads teams on advanced techniques.
Provenance
The evidence base behind this profile — every layer is sourced; quality is scored by an adversarial review panel (1–5; passes at ≥4 on the minimum dimension).
Level — P7 — Staff / Distinguished Professional
Staff-level individual contributor: owns architecture across systems, sets technical direction, and multiplies the output of multiple teams without managing people.
- Scope
- Cross-organization / enterprise technical strategy
- Autonomy
- Operates autonomously at the enterprise level
- Complexity
- Industry-level, highly ambiguous problems
- Impact
- Enterprise-wide
- Decision rights
- Final technical authority across multiple domains
- Leadership
- Sets technical direction org-wide; develops principals
- Typical experience
- 15–22 yrs
Adjacent roles
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O*NET / SOC
- code=15-2051source=jfm-factory.resolve