Founding MLData Science Engineer
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Job Location:
San Francisco, CA - USA
Monthly Salary:
Not Disclosed
Posted on:
30+ days ago
Vacancies:
1 Vacancy
Job Summary
Founding ML/Data Science Engineer
Location: San Francisco CA (Hybrid) Employment Type: Full-time
About the Role
Our client is building the revenue execution system for B2B enterprises tackling the underpenetrated space of post-sales enablement. They are combining LLMs with structured customer data to automate account scoring cohort segmentation and expansion playbooks. As a Founding ML/Data Science Engineer youll work directly with the founders to architect build and scale this platform from 0 to 1 with full ownership of core features.
This role requires a hybrid engineer who can both architect production ML systems and make sophisticated modeling decisions. Youll need to be equally comfortable with classical ML and cutting-edge language models.
Core Responsibilities
- Design end-to-end ML systems covering model selection experimentation and production deployment.
- Build feature engineering pipelines to extract signal from both structured business data and unstructured text.
- Develop hybrid ML/LLM systems knowing how and when to leverage traditional ML versus language models for a given problem.
- Own the full modeling lifecycle: EDA feature engineering training validation and drift monitoring.
Requirements
- 3 years of experience in applied ML.
- Strong ML fundamentals (gradient descent regularization bias-variance tradeoffs).
- Experience with the full spectrum of machine learning: classical ML deep learning and LLMs.
- Production experience with model serving feature stores and training pipelines.
- Statistical rigor in experimental design hypothesis testing and causal inference.
- Proven ability to ship end-to-end ML products.
Benefits
- 3 years of experience in applied ML.
- Strong ML fundamentals (gradient descent regularization bias-variance tradeoffs).
- Experience with the full spectrum of machine learning: classical ML deep learning and LLMs.
- Production experience with model serving feature stores and training pipelines.
- Statistical rigor in experimental design hypothesis testing and causal inference.
- Proven ability to ship end-to-end ML products.
Due to the high volume of applications we anticipate we regret that we are unable to provide individual feedback to all you do not hear back from us within 4 weeks of your application please assume that you have not been successful on this occasion. We genuinely appreciate your interest and wish you the best in your job search.
About MLabs
MLabs is a full-stack software consultancy working with leading blockchain AI and tech startups worldwide. Were supporting this client with their hiring and youll be joining their team directly.
Apply now to be part of one of the most exciting AI startups in San Francisco.
Commitment to Equality and Accessibility:
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Key Skills
- Apache Hive
- S3
- Hadoop
- Redshift
- Spark
- AWS
- Apache Pig
- NoSQL
- Big Data
- Data Warehouse
- Kafka
- Scala
