Machine Learning Engineer
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Requirements
• Degree or recent experience relating to Machine Learning • Familiarity with implementing safe, ethical, and compliant ML systems (familiarity with ISO 42001/NIST AI RMF and the associated common controls) • Experience deploying, monitoring, and improving ML models at a technology company • A strong grasp of Python • Experience training and experimenting with deep learning models as well as serving them in production • Experience with transformers and other HuggingFace libraries • Experience building and consuming APIs • An ability to build consensus while creating space for others • Excellent prioritization and time management skills • Experience with NLP and large language models, a plus • Experience with machine learning models which are not deep learning (e.g. decision trees), a plus • Experience using Docker and AWS (SageMaker endpoints, SageMaker notebooks, S3, IAM), a plus • Your own unique talents! Your background has given you a unique perspective and set of transferable skills that aren't always in alignment with a given role - but those are qualities we value at Greenhouse. If you don't meet 100% of the qualifications outlined above, we still strongly encourage you to apply • Applicants must be currently authorized to work in the United States on a full-time basis. • If you are based in California, we encourage you to read this important information for California residents linked here. • The national pay range for this role is $141,000 - $176,750. Individual compensation will be commensurate with the candidate's experience and qualifications. Certain roles may be eligible for additional compensation, including stock option awards, bonuses, and merit increases. Additionally, certain roles have the opportunity to receive sales commissions that are based on the terms of the sales commission plan applicable to the role. • Greenhouse provides a variety of benefits to employees, including medical, dental, and vision insurance, basic life insurance, mental health resources, financial wellness benefits, and a fully paid parental leave program. For US-based employees, we offer short-term and long-term disability coverage, a 401(k) plan and company match. U.S. based employees also receive, per calendar year, up to 14 scheduled paid holidays and up to 80 hours of paid sick leave. Non-exempt employees accrue up to 20-25 days of paid vacation time annually, depending on tenure, and exempt employees have flexible paid time off (PTO). • The anticipated closing date for this role is February 20th, 2026
Responsibilities
• Own well-scoped ML components or models: You will take ownership of specific components, executing on defined problems with guidance • Train and deploy models using established patterns, and ship models to production following existing tooling and standards • Monitor and debug models with help from senior engineers, and work to improve model performance incrementally • Implement AI governance, privacy, and security requirements as defined • Collaborate within the team and with partner functions, while communicating progress, risks, and blockers clearly • You should have • You should have • Degree or recent experience relating to Machine Learning • Familiarity with implementing safe, ethical, and compliant ML systems (familiarity with ISO 42001/NIST AI RMF and the associated common controls) • Experience deploying, monitoring, and improving ML models at a technology company • A strong grasp of Python • Experience training and experimenting with deep learning models as well as serving them in production • Experience with transformers and other HuggingFace libraries • Experience building and consuming APIs • An ability to build consensus while creating space for others • Excellent prioritization and time management skills • Experience with NLP and large language models, a plus • Experience with machine learning models which are not deep learning (e.g. decision trees), a plus • Experience using Docker and AWS (SageMaker endpoints, SageMaker notebooks, S3, IAM), a plus • Your own unique talents! Your background has given you a unique perspective and set of transferable skills that aren't always in alignment with a given role - but those are qualities we value at Greenhouse. If you don't meet 100% of the qualifications outlined above, we still strongly encourage you to apply • Applicants must be currently authorized to work in the United States on a full-time basis. • If you are based in California, we encourage you to read this important information for California residents linked here. • The national pay range for this role is $141,000 - $176,750. Individual compensation will be commensurate with the candidate's experience and qualifications. Certain roles may be eligible for additional compensation, including stock option awards, bonuses, and merit increases. Additionally, certain roles have the opportunity to receive sales commissions that are based on the terms of the sales commission plan applicable to the role. • Greenhouse provides a variety of benefits to employees, including medical, dental, and vision insurance, basic life insurance, mental health resources, financial wellness benefits, and a fully paid parental leave program. For US-based employees, we offer short-term and long-term disability coverage, a 401(k) plan and company match. U.S. based employees also receive, per calendar year, up to 14 scheduled paid holidays and up to 80 hours of paid sick leave. Non-exempt employees accrue up to 20-25 days of paid vacation time annually, depending on tenure, and exempt employees have flexible paid time off (PTO). • The anticipated closing date for this role is February 20th, 2026