Senior · Staff · Principal Machine Learning Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

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Salary

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401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Principal From the posting source checked Jun 20, 2026

Work mode

Hybrid From the posting source checked Jun 20, 2026

In-office days

2 days From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Senior · Staff · Principal Machine Learning Engineer San Francisco | Hybrid | Remote Senior / Staff / Principal Machine Learning Engineer Location: Onsite San Francisco (5 days onsite AND hybrid options) We have multiple startups interested in talent. Here is a generic summary. Instead of a perfect job description, we present talented individuals to companies and allow them to share how that talent fits in the organization. Key Responsibilities: - Model Development: Designing and implementing ML algorithms and models, including deep learning models. - Data Handling: Preprocessing, analyzing, and preparing large datasets for model training and evaluation. - System Integration: Collaborating with software engineers to integrate ML models into production systems. - Performance Optimization: Continuously improving and optimizing ML models for accuracy, efficiency, and scalability. - Monitoring and Maintenance: Monitoring model performance in production, troubleshooting issues, and ensuring model reliability. - Staying Updated: Keeping abreast of the latest advancements in ML, AI, and related technologies. - Collaboration: Working with data scientists, software engineers, and other stakeholders to deliver effective ML solutions. Essential Skills: - Programming Languages: Strong proficiency in Python, R, or other relevant languages. - ML Frameworks: Experience with frameworks like TensorFlow, PyTorch, or scikit-learn. - Data Science Fundamentals: Solid understanding of statistical analysis, data modeling, and machine learning algorithms. - Problem-Solving: Excellent analytical and problem-solving skills to address complex challenges. - Communication: Effective communication skills to convey technical information to both technical and non-technical audiences. - Collaboration: Ability to work effectively in a team environment. Education and

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