Hireovo · AI & Machine Learning
Machine Learning Engineer
San Francisco, CA · FULL TIME
About the role
Senior Machine Learning Engineer talent pool in San Francisco, focused on models that can be trained, evaluated, and explained.
This talent-pool listing is for a senior Machine Learning Engineer based in San Francisco, United States. Day to day, the focus is models that can be trained, evaluated, and explained. A typical stretch of time is spent preparing a dataset, training a baseline, and writing down where the model fails.
Responsibilities
- Own the dataset card so the reviewer who signs off on model quality can see the missing labels without a separate explanation.
- Every week, bring the train-test split note up to date and resolve the leaked rows before the next cycle.
- Write the baseline report in plain language and mention a metric that flatters the model in the same note.
- If the error analysis stalls, say so the same day and state what you already did about a slice that fails quietly.
- Keep the feature list in the shared tracker, not in a personal folder.
- Treat the evaluation note as finished only after you have checked the held-out result.
Requirements
- Roughly 5 to 8 years of training and evaluating supervised models that matches this machine learning engineer profile, or a shorter record with the same kind of finished work.
- Practical skill with Python and SQL in machine learning engineer work, not a certificate alone.
- Able to explain why a metric improved and which slice did not to a colleague who was not in the room.
- Careful with leakage between training and evaluation data; this is checked in review of machine learning engineer work.
- For the machine learning engineer brief in San Francisco, use the working language of that assignment in United States.
Nice to have
- Error analysis by slice is useful on machine learning engineer assignments in San Francisco, and it is not required.
- People who have already done a baseline before a complex model can start this machine learning engineer brief with less coaching.
- Dataset documentation is a plus for machine learning engineer work based in San Francisco.
Skills
Python, SQL, model evaluation, feature design, experiment notes, data slicing, version control, technical writing
Working conditions
This machine learning engineer listing in San Francisco is hybrid: some days at the assignment's premises in San Francisco, and some days from home, on a split the employer sets. The usual week is Monday to Friday. The work is desk-based. Training jobs may run outside the working day and need a check the next morning.
Location
The hiring base for this machine learning engineer listing is San Francisco, United States. English is the working language for this San Francisco brief.
Qualified people are considered without regard to race, color, religion, sex, national origin, age, disability, or other characteristics protected by applicable law.
This is a talent-pool listing. Your profile will be reviewed and matched to employer openings that fit; submitting an application does not guarantee an interview or placement.
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