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Founding ML/Data Science Engineer Job Opening In San Francisco – Now Hiring MLabs


Job description

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, you'll 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.

You'll 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 candidates. If 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.

We’re supporting this client with their hiring, and you’ll 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:

At MLabs, we are committed to offer equal opportunities to all candidates.

We ensure no discrimination, accessible job adverts, and providing information in accessible formats.

Our goal is to foster a diverse, inclusive workplace with equal opportunities for all.

If you need any reasonable adjustments during any part of the hiring process or you would like to see the job-advert in an accessible format please let us know at the earliest opportunity by emailing human-resources@mlabs.city.

MLabs Ltd collects and processes the personal information you provide such as your contact details, work history, resume, and other relevant data for recruitment purposes only.

This information is managed securely in accordance with MLabs Ltd’s Privacy Policy and Information Security Policy, and in compliance with applicable data protection laws.

Your data may be shared only with clients and trusted partners where necessary for recruitment purposes.

You may request the deletion of your data or withdraw your consent at any time by contacting legal@mlabs.city.

Required Skill Profession

Computer Occupations


  • Job Details

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Unlock Your Founding ML Potential: Insight & Career Growth Guide


Real-time Founding ML Jobs Trends (Graphical Representation)

Explore profound insights with Expertini's real-time, in-depth analysis, showcased through the graph here. Uncover the dynamic job market trends for Founding ML in San Francisco, United States, highlighting market share and opportunities for professionals in Founding ML roles.

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The Work Culture

An organization's rules and standards set how people should be treated in the office and how different situations should be handled. The work culture at MLabs adheres to the cultural norms as outlined by Expertini.

The fundamental ethical values are:

1. Independence

2. Loyalty

3. Impartiapty

4. Integrity

5. Accountabipty

6. Respect for human rights

7. Obeying United States laws and regulations

What Is the Average Salary Range for Founding ML/Data Science Engineer Positions?

The average salary range for a varies, but the pay scale is rated "Standard" in San Francisco. Salary levels may vary depending on your industry, experience, and skills. It's essential to research and negotiate effectively. We advise reading the full job specification before proceeding with the application to understand the salary package.

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Key qualifications for Founding ML/Data Science Engineer typically include Computer Occupations and a list of qualifications and expertise as mentioned in the job specification. The generic skills are mostly outlined by the . Be sure to check the specific job listing for detailed requirements and qualifications.

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Interview Tips for Founding ML/Data Science Engineer Job Success

MLabs interview tips for Founding ML/Data Science Engineer

Here are some tips to help you prepare for and ace your Founding ML/Data Science Engineer job interview:

Before the Interview:

Research: Learn about the MLabs's mission, values, products, and the specific job requirements and get further information about

Other Openings

Practice: Prepare answers to common interview questions and rehearse using the STAR method (Situation, Task, Action, Result) to showcase your skills and experiences.

Dress Professionally: Choose attire appropriate for the company culture.

Prepare Questions: Show your interest by having thoughtful questions for the interviewer.

Plan Your Commute: Allow ample time to arrive on time and avoid feeling rushed.

During the Interview:

Be Punctual: Arrive on time to demonstrate professionalism and respect.

Make a Great First Impression: Greet the interviewer with a handshake, smile, and eye contact.

Confidence and Enthusiasm: Project a positive attitude and show your genuine interest in the opportunity.

Answer Thoughtfully: Listen carefully, take a moment to formulate clear and concise responses. Highlight relevant skills and experiences using the STAR method.

Ask Prepared Questions: Demonstrate curiosity and engagement with the role and company.

Follow Up: Send a thank-you email to the interviewer within 24 hours.

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Be Yourself: Let your personality shine through while maintaining professionalism.

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Body Language: Maintain good posture, avoid fidgeting, and make eye contact.

Turn Off Phone: Avoid distractions during the interview.

Final Thought:

To prepare for your Founding ML/Data Science Engineer interview at MLabs, research the company, understand the job requirements, and practice common interview questions.

Highlight your leadership skills, achievements, and strategic thinking abilities. Be prepared to discuss your experience with HR, including your approach to meeting targets as a team player. Additionally, review the MLabs's products or services and be prepared to discuss how you can contribute to their success.

By following these tips, you can increase your chances of making a positive impression and landing the job!

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