Build & Own ML/DL Systems

• Apply ML/AI solutions with awareness of business needs, system constraints, and business context.
• Build and own ML/DL models across complex data types - geometries, part metadata, transactional data, and free-text notes.
• Own small-to-medium ML/DL subsystems and features end-to-end.
• Contribute NLP and document understanding pipelines for technical drawings and unstructured manufacturing specs; build reusable components on our AWS Bedrock-based AI Platform.

Experimentation & Technical Problem Solving

• Tackle different complex ML problems: identify data issues, navigate model choices, and design clear experiments.
• Work independently on assigned ML tasks while collaborating across teams.
• Suggest improvements at the feature level; explore and evaluate new techniques with guidance.

Collaboration & Mentorship

• Mentor junior peers to grow into ML; provide solid code, testing, and reviews.
• Contribute to feature-level design discussions and surface technical suggestions and improvements.

Technical

• Good grounding in the mathematical foundations of ML and a relevant degree (computer science, simulation science, or equivalent).
• 3-5 years of hands-on experience designing, training, and deploying complex ML/DL models in production using state-of-the-art frameworks (PyTorch, TensorFlow, scikit-learn, XGBoost).
• Proven ability to build models that serve as autonomous decision systems, applied to critical business problems with large, heterogeneous data.
• Strong Python engineering fundamentals: clean, modular, testable code; data pipelines; model versioning; CI/CD for ML; packaging for production (e.g. Docker, service wrappers).
• Solid ML experimentation practice: experiment tracking from scratch (e.g. W&B, MLflow), model evaluation, and iterative improvement tied to measurable business outcomes.
• Solid understanding of data transformation techniques, languages, and libraries (e.g. Pandas, Polars, SQL, dbt).
• Ability to work independently: break down larger problem definitions into concrete tasks, navigate model choices, and deliver production-ready ML implementations end-to-end.

Leadership & Collaboration

• Comfortable working independently on assigned ML tasks while staying closely coordinated with cross-functional teams.
• Willingness to mentor junior peers on code quality, testing, and ML best practices.
• Able to contribute meaningfully to feature-level design discussions and communicate technical trade-offs clearly.

Mindset

• Ownership-driven: takes small-to-medium ML/DL subsystems from problem framing through to production deployment.
• Curious and adaptable, applying and adjusting state-of-the-art approaches to new, domain-specific manufacturing problems.
• Outcome-focused, tying experimentation and iterative improvement to measurable business results.

Nice to Have

• Experience with manufacturing/industrial ML applications.
• Familiarity with LLM integration and Generative AI approaches.
• Experience with cloud-based ML infrastructure (e.g., AWS SageMaker, Snowflake).
• Exposure to MLOps practices: CI/CD pipelines, model monitoring, and observability.
• Experience collaborating in international teams across time zones.

What This Role Is Not:

• This is not a pure GenAI engineer role: you will develop complex DL models using tabular and non-tabular, domain-specific data - 3D geometries, CAD-derived features, technical drawings, machining data, and manufacturing metadata.
• This is not a Data Analyst role: models you build run in production and make final, customer-facing decisions, not small ML models that create recommendations for a human analyst.