Darwin Recruitment
Location:
United States (West Coast preferred, remote considered) About the Company We are a US-based company applying machine learning to high-impact, real-world problems. Our teams build and deploy ML systems that operate in production, supporting critical business and operational decisions. We value pragmatic engineering, strong ownership, and engineers who understand the trade-offs between model performance and real-world constraints.
Role Overview We are seeking a
Staff Applied Machine Learning Engineer
to design, build, and deploy end-to-end ML systems in production environments. This role is hands‑on and senior, working closely with engineering, product, and data teams to deliver models that drive measurable impact.
This is
not a research‑only role
– the focus is on applied ML, production systems, and business outcomes.
Responsibilities
Design, develop, and deploy machine learning models end-to-end, from data ingestion to production
Work with noisy, incomplete, or imperfect data to deliver practical ML solutions
Optimize models for performance, reliability, and scalability in production environments
Collaborate closely with product and engineering teams to align ML solutions with business needs
Monitor and maintain deployed models, ensuring ongoing performance and stability
Provide technical leadership and mentorship to other ML engineers
Qualifications
7+ years of experience in software engineering or machine learning roles
Proven experience deploying and maintaining ML models in production
Strong understanding of applied ML techniques and model evaluation
Experience working with real-world data and operational constraints
Proficiency in Python and common ML frameworks (e.g. PyTorch, TensorFlow, scikit-learn)
Familiarity with MLOps, model monitoring, and production pipelines
Strong communication skills and ability to work cross-functionally
Why You’ll Enjoy This Role
Work on ML problems with real-world impact
High ownership and influence at a senior technical level
Collaborative, engineering-led culture
Competitive compensation and flexible work arrangements
#J-18808-Ljbffr
United States (West Coast preferred, remote considered) About the Company We are a US-based company applying machine learning to high-impact, real-world problems. Our teams build and deploy ML systems that operate in production, supporting critical business and operational decisions. We value pragmatic engineering, strong ownership, and engineers who understand the trade-offs between model performance and real-world constraints.
Role Overview We are seeking a
Staff Applied Machine Learning Engineer
to design, build, and deploy end-to-end ML systems in production environments. This role is hands‑on and senior, working closely with engineering, product, and data teams to deliver models that drive measurable impact.
This is
not a research‑only role
– the focus is on applied ML, production systems, and business outcomes.
Responsibilities
Design, develop, and deploy machine learning models end-to-end, from data ingestion to production
Work with noisy, incomplete, or imperfect data to deliver practical ML solutions
Optimize models for performance, reliability, and scalability in production environments
Collaborate closely with product and engineering teams to align ML solutions with business needs
Monitor and maintain deployed models, ensuring ongoing performance and stability
Provide technical leadership and mentorship to other ML engineers
Qualifications
7+ years of experience in software engineering or machine learning roles
Proven experience deploying and maintaining ML models in production
Strong understanding of applied ML techniques and model evaluation
Experience working with real-world data and operational constraints
Proficiency in Python and common ML frameworks (e.g. PyTorch, TensorFlow, scikit-learn)
Familiarity with MLOps, model monitoring, and production pipelines
Strong communication skills and ability to work cross-functionally
Why You’ll Enjoy This Role
Work on ML problems with real-world impact
High ownership and influence at a senior technical level
Collaborative, engineering-led culture
Competitive compensation and flexible work arrangements
#J-18808-Ljbffr