AI Data Engineer

FluencySan Francisco, California, United States - Hybrid$150k – $250k+ Equity1w ago

In Office NA Cloud Computing Fintech Artificial Intelligence Data Engineer ML Engineer AI Engineer Python Airflow Technical Writing Prefect Dagster

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Requirements

Skills needed: AI/ML Engineer with experience in data engineering for artificial intelligence applications. Must have strong analytical skills to handle large datasets efficiently. Proficiency in programming languages such as Python and R is required along with knowledge of machine learning algorithms, big data technologies (e.g., Hadoop, Spark), SQL databases, and cloud computing platforms like AWS or Azure.
Years of experience: The job posting does not specify a minimum number of years of experience; however, it implies that candidates should have relevant work history in the field of AI/ML engineering with data handling capabilities. Candidates are expected to demonstrate their ability through past projects and accomplishments mentioned during interviews or resume review processes.
Education: The job posting does not explicitly mention a required education level; however, it is common for such roles that candidates should have at least a Bachelor's degree in Computer Science, Engineering, Data Science, Statistics, Mathematics, or related fields with coursework and projects relevant to AI/ML engineering.
Certifications: The job posting does not explicitly mention any required certifications; however, having industry-recognized credentials such as AWS Certified Solutions Architect Advanced (ACSA), Microsoft Certified: Azure Solutions Architect Expert (MCSE), or similar can be beneficial and may enhance a candidate's application.
Must-haves: Candidates must have strong communication skills, both written and verbal, to effectively collaborate with cross-functional teams within the company. Familiarity with Agile methodologies is essential as it indicates that candidates should be able to work in an iterative development environment where they can adapt quickly to changing requirements.

Responsibilities

Designing ingestion pipelines that process millions of screenshots and behavioural events daily.
Building data validation and quality systems to catch drift before it corrupts models.
Creating feature stores and serving infrastructure for balancing freshness against compute cost.
Optimizing storage and query patterns for time-series behavioral data.
Orchestrating complex DAGs that coordinate OCR, LLM enrichment, and downstream aggregations.
Writing the playbook to address technical challenges in areas like data engineering, LLM pipelines, and production systems.
Working directly with founders on understanding business context through customer calls and interactions.
Helping with product thinking by staying up to date with key aspects of our enterprise intelligence initiatives.
Shipping models to production including deployment, versioning, monitoring (strongly preferred).
Experience in cost optimization for data-intensive systems is required.
Familiarity with multi-region data architectures and residency requirements is expected.

Benefits

US$150K - $250K salary, depending on candidate and experience
Substantial equity, every offer includes ownership
Mac, Linux, or Windows, your call
High-impact work with global enterprises
Technical, product-led founders
Don't apply if:
You want hybrid or remote
You don't like working hard and with insane velocity
You want to work a 9 to 5
You're not comfortable with rapid iteration
You think data engineering is plumbing work
You've never operated production pipelines
You don't have personal projects
You dislike constraints (we have them: cost, latency, reliability tradeoffs are real)
You aren't ambitious
You don't have a good reason for wanting to work at an early-stage company
1:1 with founder
Technical deep-dive on past data engineering work
Work through a real problem with the team
We strongly encourage applicants from underrepresented backgrounds to apply. Diverse teams build better products, see value #5.