Responsibilities
- Build with impact. Your work will empower millions of users through well-known and emerging Fintech Applications with access to financial services.
- Experiment with cutting edge ML modeling techniques.
- Work on both 0-1 stage problems as well as 1-10.
- Develop AI/MLmodels in a full life cycle, from offline training to online serving and monitoring.
- Collaborate with teams across Plaid to define ML roadmap.
- Dive deep into data and apply data driven decisions in day-to-day work.
- A high ownership, bottom-up driven team.
Qualifications
- 5+ years in training and serving AI/ML models in a production environment.
- Experience in building/working with data intensive backend applications in large distributed systems.
- Ability to code and iterate independently on top of data infrastructure tools like Python, Spark, Jupyter notebooks, standard ML libraries, etc.
- Take pride in taking ownership and driving projects to business impact.
- Data analytics and data engineering experience is a plus.
- Experience with the industry application of NLP is a plus.
- Experience with the FinTech industry is a plus.
- Ability to work with technical and non-technical teams
- Master's degree or equivalent work experience in Computer Science, Mathematics, Engineering, or a closely related field.
The target base salary for this position ranges from $228,960/year to $344,160/year [in Zone 1, in Zone 4 or encompassing all Zones]. The target base salary will vary based on the job's location.
Our geographic zones are as follows:
Zone 1 - San Francisco / New York City / Seattle
Zone 2 - Los Angeles / Washington DC / Austin / Boston / Sacramento / San Diego
Zone 3 - Atlanta / Portland / Chicago / Philadelphia / Denver / Miami / Dallas / Raleigh
Zone 4 - All other US cities
The base salary range listed for this full-time position excludes commission (if applicable), equity and benefits. The pay range shown on each job posting is the minimum and maximum target for new-hire salaries. Actual pay may be higher or lower depending on factors like skills, experience, and relevant education or training.
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