Rad Hires is hiring: Senior Machine Learning Engineer - NYC in New York
Rad Hires, New York, NY, United States
Senior Machine Learning Engineer - NYC
Base Salary: $220K base; $280–300K total comp
Work Model: Hybrid, Midtown Manhattan (2–3 days on-site)
About the Role
We’re looking for an experienced Machine Learning Engineer to design, build, and scale intelligent data products that support real-world applications. This position bridges data engineering, model development, and productization — transforming large datasets and unstructured information into production-ready systems.
About the Client
You’ll be joining a global organization that operates at the intersection of data, risk modeling, analytics, and financial advisory , supporting large enterprises in making high-impact strategic decisions. The team works with highly complex datasets and delivers advanced analytical and predictive solutions that directly influence business outcomes.
You’ll collaborate with engineering, product, and data teams to create end-to-end ML pipelines, optimize model performance, and drive experimentation with cutting-edge NLP and LLM techniques. This role is ideal for someone who thrives on building robust, scalable ML infrastructure from the ground up.
What You’ll Do
Design and maintain scalable ETL/ELT pipelines to support analytical and ML systems.
Build, train, validate, and deploy machine learning models with reproducible and automated workflows.
Implement MLOps best practices, including packaging, orchestration, CI/CD, and production monitoring.
Manage data structures, feature stores, and metadata to ensure high reliability and accessibility.
Develop data products and APIs to expose ML insights to internal systems and stakeholders.
Continuously optimize system performance, cost efficiency, and scalability across cloud environments.
Establish standards for observability: logging, alerts, data lineage, documentation.
Collaborate with product and business teams to define success metrics and measurable impact.
Mentor engineers and data scientists in ML system design and operations.
What You Bring
7+ years in machine learning engineering, data engineering, or applied data science.
Degree in CS, Engineering, Math, Statistics, or equivalent experience.
Advanced Python and SQL skills, with production-grade coding practices.
Strong knowledge of testing, data validation, and ML quality assurance.
Experience with cloud data platforms (Databricks, Snowflake, BigQuery).
Familiarity with dbt or similar transformation frameworks.
Expertise with scikit-learn, PyTorch, or Transformers for model development.
Proven experience implementing MLOps workflows with CI/CD and containerization (K8s or serverless).
Experience with NLP tools and techniques (spaCy, NLTK, prompt engineering).
Proficiency integrating ML systems with REST APIs.
Ability to design scalable and observable production ML systems.
Experience mentoring or leading small technical teams.
Bonus Points
Experience deploying ML models at scale with latency/cost optimization.
Background in consulting, tech, or fast-paced product environments.
Familiarity with financial/risk modeling.
Knowledge of BI tools (Looker, Tableau, Power BI).
Experience with vector databases (e.g., LanceDB) and RAG pipelines.
Experience using data labeling platforms like Label Studio.
Why Join
You’ll help build the ML and data foundation of a global analytics-driven organization, influence architectural decisions, and work with cutting-edge NLP and generative AI technologies — all within a high-impact team focused on measurable business outcomes.
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In Summary: Senior Machine Learning Engineer - NYC Base Salary: $220K base; $280–300K total comp Work Model: Hybrid, Midtown Manhattan (2–3 days on-site) Position bridges data engineering, model development, and productization — transforming large datasets into production-ready systems .
En Español: Ingeniero de aprendizaje automático senior - NYC Salario base: $220K base; $ 280300K total comp Modelo de trabajo: híbrido, Midtown Manhattan (23 días en el sitio) Sobre la función Buscamos un ingeniero de Machine Learning experimentado para diseñar, construir y escalar productos de datos inteligentes que soportan aplicaciones del mundo real. Este puesto conecta a la ingeniería de datos, desarrollo de modelos y producción transformando grandes conjuntos de datos e información no estructurada en sistemas listos para la producción. Gestionar estructuras de datos, almacenes de características y metadatos para garantizar una alta fiabilidad y accesibilidad. Desarrollar productos de datos y APIs para exponer conocimientos sobre ML a los sistemas internos y las partes interesadas. Optimizar continuamente el rendimiento del sistema, la eficiencia en costos y la escalabilidad en entornos de nube. Establecer estándares de observabilidad: registro, alertas, linaje de información, documentación. Colaborar con equipos de producto y negocios para definir métricas de éxito e impacto medible. Ingenieros mentores y científicos de datos en diseño y operaciones de sistemas ML. Lo que trae 7+ años en ingeniería automática, ingeniería de datos o ciencia aplicada de datos. Grado en CS, ingenieria, matemáticas, estadística o experiencia equivalente. Habilidades avanzadas en Python y SQL, con prácticas de ensayo de producción-grados. Conocimiento sólido de pruebas, validación de datos , y calidad de las plataformas ML. Experiencia con grandes empresas (Data Prochips, Snowworks), expertos en desarrollo de servicios informáticos y análisis de computadoras. Antecedentes en entornos de consultoría, tecnología o productos rápidos. Familiarización con el modelado financiero/riesgo. Conocimiento de las herramientas BI (Looker, Tableau, Power BI). Experiencia con bases de datos vectoriales (por ejemplo, LanceDB) y tuberías RAG.
Base Salary: $220K base; $280–300K total comp
Work Model: Hybrid, Midtown Manhattan (2–3 days on-site)
About the Role
We’re looking for an experienced Machine Learning Engineer to design, build, and scale intelligent data products that support real-world applications. This position bridges data engineering, model development, and productization — transforming large datasets and unstructured information into production-ready systems.
About the Client
You’ll be joining a global organization that operates at the intersection of data, risk modeling, analytics, and financial advisory , supporting large enterprises in making high-impact strategic decisions. The team works with highly complex datasets and delivers advanced analytical and predictive solutions that directly influence business outcomes.
You’ll collaborate with engineering, product, and data teams to create end-to-end ML pipelines, optimize model performance, and drive experimentation with cutting-edge NLP and LLM techniques. This role is ideal for someone who thrives on building robust, scalable ML infrastructure from the ground up.
What You’ll Do
Design and maintain scalable ETL/ELT pipelines to support analytical and ML systems.
Build, train, validate, and deploy machine learning models with reproducible and automated workflows.
Implement MLOps best practices, including packaging, orchestration, CI/CD, and production monitoring.
Manage data structures, feature stores, and metadata to ensure high reliability and accessibility.
Develop data products and APIs to expose ML insights to internal systems and stakeholders.
Continuously optimize system performance, cost efficiency, and scalability across cloud environments.
Establish standards for observability: logging, alerts, data lineage, documentation.
Collaborate with product and business teams to define success metrics and measurable impact.
Mentor engineers and data scientists in ML system design and operations.
What You Bring
7+ years in machine learning engineering, data engineering, or applied data science.
Degree in CS, Engineering, Math, Statistics, or equivalent experience.
Advanced Python and SQL skills, with production-grade coding practices.
Strong knowledge of testing, data validation, and ML quality assurance.
Experience with cloud data platforms (Databricks, Snowflake, BigQuery).
Familiarity with dbt or similar transformation frameworks.
Expertise with scikit-learn, PyTorch, or Transformers for model development.
Proven experience implementing MLOps workflows with CI/CD and containerization (K8s or serverless).
Experience with NLP tools and techniques (spaCy, NLTK, prompt engineering).
Proficiency integrating ML systems with REST APIs.
Ability to design scalable and observable production ML systems.
Experience mentoring or leading small technical teams.
Bonus Points
Experience deploying ML models at scale with latency/cost optimization.
Background in consulting, tech, or fast-paced product environments.
Familiarity with financial/risk modeling.
Knowledge of BI tools (Looker, Tableau, Power BI).
Experience with vector databases (e.g., LanceDB) and RAG pipelines.
Experience using data labeling platforms like Label Studio.
Why Join
You’ll help build the ML and data foundation of a global analytics-driven organization, influence architectural decisions, and work with cutting-edge NLP and generative AI technologies — all within a high-impact team focused on measurable business outcomes.
#J-18808-Ljbffr
In Summary: Senior Machine Learning Engineer - NYC Base Salary: $220K base; $280–300K total comp Work Model: Hybrid, Midtown Manhattan (2–3 days on-site) Position bridges data engineering, model development, and productization — transforming large datasets into production-ready systems .
En Español: Ingeniero de aprendizaje automático senior - NYC Salario base: $220K base; $ 280300K total comp Modelo de trabajo: híbrido, Midtown Manhattan (23 días en el sitio) Sobre la función Buscamos un ingeniero de Machine Learning experimentado para diseñar, construir y escalar productos de datos inteligentes que soportan aplicaciones del mundo real. Este puesto conecta a la ingeniería de datos, desarrollo de modelos y producción transformando grandes conjuntos de datos e información no estructurada en sistemas listos para la producción. Gestionar estructuras de datos, almacenes de características y metadatos para garantizar una alta fiabilidad y accesibilidad. Desarrollar productos de datos y APIs para exponer conocimientos sobre ML a los sistemas internos y las partes interesadas. Optimizar continuamente el rendimiento del sistema, la eficiencia en costos y la escalabilidad en entornos de nube. Establecer estándares de observabilidad: registro, alertas, linaje de información, documentación. Colaborar con equipos de producto y negocios para definir métricas de éxito e impacto medible. Ingenieros mentores y científicos de datos en diseño y operaciones de sistemas ML. Lo que trae 7+ años en ingeniería automática, ingeniería de datos o ciencia aplicada de datos. Grado en CS, ingenieria, matemáticas, estadística o experiencia equivalente. Habilidades avanzadas en Python y SQL, con prácticas de ensayo de producción-grados. Conocimiento sólido de pruebas, validación de datos , y calidad de las plataformas ML. Experiencia con grandes empresas (Data Prochips, Snowworks), expertos en desarrollo de servicios informáticos y análisis de computadoras. Antecedentes en entornos de consultoría, tecnología o productos rápidos. Familiarización con el modelado financiero/riesgo. Conocimiento de las herramientas BI (Looker, Tableau, Power BI). Experiencia con bases de datos vectoriales (por ejemplo, LanceDB) y tuberías RAG.