AI Engineer
Job description
About the role
Accounting operations are accelerated by AI-driven automation that stream workflows. Intelligent features surface real-time insights for accounting teams at startups and mid-size tech companies.
Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.
Key facts
What you'll do
Financial workflows are handled by models to speed accounting operations for customers.
Real-time insights are delivered by platform features that serve accounting teams at startups and mid-size tech companies.
Machine learning models move through production pipelines so accounting software maintains consistent, scalable performance.
Models from OpenAI and Hugging Face undergo fine-tuning through LLM workflows to fit real-world accounting scenarios.
Product requirements are turned into shipped AI functionality by cross-functional collaboration across engineering, product, and design.
Ideas are experimented on and features are shipped quickly through rapid cycles within a lean team that moves fast.
Prototyping, deployment, and monitoring of models are owned end-to-end for customer value.
Practical business problems are solved by customer-facing applications instead of research-only work.
Requirements
The posting states a bachelor's degree requirement. A degree is required as stated in the listing.
Five to seven years of experience in machine learning, deep learning, and natural language processing is necessary.
Python proficiency and framework experience such as PyTorch or TensorFlow is required for building models.
ML models are deployed into production environments by candidates with hands-on experience.
LLM familiarity and the ability to fine-tune them for real-world use cases is expected.
Agile, lean team comfort is needed from those who move quickly and experiment often.
Ownership is taken end-to-end from prototyping through deployment and monitoring.
Applying AI to solve real business problems in a startup setting is expected.
Collaboration across engineering, product, and design functions is required.
Onsite work in San Francisco five days per week with the entire company is mandatory.
Practical notes
Work is done onsite at the headquarters in San Francisco with no remote option.
Team collaboration occurs five days per week with the entire company.
Typical interview steps
Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.
Good to know
Machine learning models power many features in modern accounting software.
Natural language processing helps systems understand financial text and transactions.
Large language models support tasks such as classification, summarization, and reasoning.
Production deployment connects models to live data and user workflows.
Cross-functional product teams work closely with design and engineering in fast-paced startups.
Career growth
Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.
About the company
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