Job description

Main Duties & Responsibilities: - Take ownership of design and development of advanced machine learning models for complex scientific, product, and business problems, including those that require unsupervised, semi-supervised, or other non-standard modeling approaches. - Convert ambiguous, real-world challenges into well-scoped machine learning problems by defining objectives, selecting appropriate methods, designing evaluation frameworks, and determining whether results are meaningful and production-ready. - Build, compare, and evaluate models with rigorous attention to statistical validity, generalization behavior, interpretability, and practical utility in downstream workflows and products. - Move successful models and ML workflows from experimentation into production, delivering reliable, maintainable systems that support ongoing monitoring, retraining, versioning, and lifecycle management. - Apply sound MLOps practices appropriate to the complexity of each model, including reproducibility, drift detection, evaluation pipelines, rollback planning, and model governance. - Help establish how advanced machine learning is practiced across the team by defining experimentation standards, model development patterns, documentation expectations, and evaluation methods. - Assess the current tooling and infrastructure landscape, identify gaps that limit effective ML development and productionization, and define the capabilities needed to close those gaps in collaboration with data and platform engineering teams. - Collaborate closely with Data Engineering, Analytics, Product, and Science stakeholders to identify high-value ML opportunities, sharpen problem definitions, and ensure model outputs are actionable within real workflows and products. Skills & Requirements: - Substantial hands-on experience designing, developing, evaluating, and productionalizing machine learning models in real-world production environments. - Deep applied knowledge of machine learning and statistics, including the ability to formulate complex modeling problems and select methods based on data characteristics, objectives, and constraints. - Demonstrated experience designing and executing rigorous ML experiments covering supervised and unsupervised approaches, model comparison, feature and representation selection, and evaluation where ground truth is incomplete or noisy. - Strong Python skills and experience writing clean, maintainable code for machine learning, statistical modeling, experimentation, and production system development. - Proven ability to move models from experimentation into production, including deployment, monitoring, retraining workflows, reproducibility, versioning, and testing. - Sound judgment about when advanced ML is the right tool and when simpler statistical, deterministic, or rules-based approaches are more appropriate. - Ability to independently investigate ambiguous modeling problems, develop a technically grounded approach, and clearly communicate the reasoning, tradeoffs, and limitations of that approach to both technical and non-technical audiences. - Experience contributing to or establishing ML practices, technical standards, and experimentation patterns within a team or organization. Preferred Skills: - Experience with modern ML tooling such as feature stores, model registries, experiment tracking platforms, orchestration frameworks, model monitoring solutions, or managed ML platforms. - Experience with Snowflake, Snowpark ML, Snowflake Feature Store, Snowflake Model Registry, AWS, or comparable cloud and data-platform-based ML capabilities. - Background working with ML systems applied to scientific, industrial, IoT, manufacturing, biotechnology, or other complex physical-world data domains is highly valued. - Familiarity with generative AI techniques including LLM workflows, retrieval-augmented generation (RAG), structured prompting, and model evaluation. - Comfortable operating in a high-ambiguity environment where practices, tooling, and team structure are still being defined, as well as willingness and ability to create structure, establish standards, and guide others in that context. tags: ml engineer, machine learning engineer, artificial intelligence engineer, ai engineer

Requirements

Must-haves

Build, compare, and evaluate models with rigorous attention to statistical validity, generalization behavior, interpretability, and practical utility in downstream workflows and products.
Move successful models and ML workflows from experimentation into production, delivering reliable, maintainable systems that support ongoing monitoring, retraining, versioning, and lifecycle management.
Apply sound MLOps practices appropriate to the complexity of each model, including reproducibility, drift detection, evaluation pipelines, rollback planning, and model governance.
Help establish how advanced machine learning is practiced across the team by defining experimentation standards, model development patterns, documentation expectations, and evaluation methods.
Substantial hands-on experience designing, developing, evaluating, and productionalizing machine learning models in real-world production environments.
Deep applied knowledge of machine learning and statistics, including the ability to formulate complex modeling problems and select methods based on data characteristics, objectives, and constraints.
Strong Python skills and experience writing clean, maintainable code for machine learning, statistical modeling, experimentation, and production system development.
Proven ability to move models from experimentation into production, including deployment, monitoring, retraining workflows, reproducibility, versioning, and testing.
Sound judgment about when advanced ML is the right tool and when simpler statistical, deterministic, or rules-based approaches are more appropriate.
Experience contributing to or establishing ML practices, technical standards, and experimentation patterns within a team or organization.

Nice-to-haves

Experience with modern ML tooling such as feature stores, model registries, experiment tracking platforms, orchestration frameworks, model monitoring solutions, or managed ML platforms.
Experience with Snowflake, Snowpark ML, Snowflake Feature Store, Snowflake Model Registry, AWS, or comparable cloud and data-platform-based ML capabilities.
Background working with ML systems applied to scientific, industrial, IoT, manufacturing, biotechnology, or other complex physical-world data domains is highly valued.
Familiarity with generative AI techniques including LLM workflows, retrieval-augmented generation (RAG), structured prompting, and model evaluation.
tags: ml engineer, machine learning engineer, artificial intelligence engineer, ai engineer

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlpythonawsaillm

Platform: bullhorn
Effort: medium