Back to jobs

Principal Machine Learning Engineer, Matching & Recommendations

US TX Austin · Austin, Texas, United States

Pay
Pay not listedSimilar roles state $165K–$240K
Work setting
On-site — work setting source
Location type: On-site
From the employer’s posting
Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting
Team
Engineering › Machine Learning — team source
Department: Engineering › Machine Learning
From the employer’s posting

Before you apply

Sponsorship
Visa sponsorship not confirmed — sponsorship source
Are you currently authorized to work in the United States without the need for employer sponsorship?
From the employer’s application form
Apply at Bumble

What you’ll work on

Full posting
  • Define the technical and ML strategy for matching, ranking, and personalization systems

  • Design and deploy production models in PyTorch for high-traffic environments

  • Build and deploy production AI agents using fine-tuned LLMs

  • Architect end-to-end ML pipelines from data processing through deployment

  • Drive A/B testing and offline evaluation to improve model performance

From the employer’s posting
Define and lead the technical strategy for AI and Machine Learning systems that power recommendations, ranking, and personalization across Bumble products
Design, develop, and deploy production-grade models using modern ML frameworks such as PyTorch
Build and deploy production AI Agents using raw and fine-tuned foundational Large Language Models (LLMs)
Architect end-to-end ML pipelines, integrating data processing (e.g. Spark, Airflow) with model training, evaluation, and deployment workflows
Drive experimentation frameworks, including A/B testing and offline evaluation

What you’ll bring

All qualifications

Core experience

  • Deep expertise in machine learning, with hands-on experience building and deploying large-scale systems in production environments
  • Strong proficiency in Python and at least one major ML framework (e.g., PyTorch, TensorFlow), with experience in areas such as recommendation systems, ranking models, or NLP
  • Expertise in prompting and fine-tuning Large Language Models (LLMs) and building production AI Agents
  • Proven experience designing scalable data and ML pipelines using tools such as Spark, Airflow, or similar distributed systems
  • Demonstrated ability to operate as a senior individual contributor, influencing technical strategy and decision-making without direct authority
  • Experience partnering effectively across functions, collaborating with purpose, and taking ownership of outcomes in complex organizational environments
Qualification wording
Deep expertise in machine learning, with hands-on experience building and deploying large-scale systems in production environments
Strong proficiency in Python and at least one major ML framework (e.g., PyTorch, TensorFlow), with experience in areas such as recommendation systems, ranking models, or NLP
Expertise in prompting and fine-tuning Large Language Models (LLMs) and building production AI Agents
Proven experience designing scalable data and ML pipelines using tools such as Spark, Airflow, or similar distributed systems
Demonstrated ability to operate as a senior individual contributor, influencing technical strategy and decision-making without direct authority
Experience partnering effectively across functions, collaborating with purpose, and taking ownership of outcomes in complex organizational environments

Tools in this posting

  • Python
  • Spark
  • Airflow
  • PyTorch
  • TensorFlow
Source — Tool mentions in context
- Deep expertise in machine learning, with hands-on experience building and deploying large-scale systems in production environments - Strong proficiency in Python and at least one major ML framework (e.g., PyTorch, TensorFlow), with experience in areas such as recommendation systems, ranking models, or NLP - Expertise in prompting and fine-tuning Large Language Models (LLMs) and building production AI Agents
- Build and deploy production AI Agents using raw and fine-tuned foundational Large Language Models (LLMs), along with sub-agents, tools, and MCP integrations - Architect end-to-end ML pipelines, integrating data processing (e.g. Spark, Airflow) with model training, evaluation, and deployment workflows - Drive experimentation frameworks, including A/B testing and offline evaluation, to continuously improve model performance and product outcomes
- Expertise in prompting and fine-tuning Large Language Models (LLMs) and building production AI Agents - Proven experience designing scalable data and ML pipelines using tools such as Spark, Airflow, or similar distributed systems - Demonstrated ability to operate as a senior individual contributor, influencing technical strategy and decision-making without direct authority
- Define and lead the technical strategy for AI and Machine Learning systems that power recommendations, ranking, and personalization across Bumble products, delivering measurable improvements in user engagement and safety - Design, develop, and deploy production-grade models using modern ML frameworks such as PyTorch , ensuring scalability and reliability in high-traffic environments - Build and deploy production AI Agents using raw and fine-tuned foundational Large Language Models (LLMs), along with sub-agents, tools, and MCP integrations

About Bumble

is the parent company of Bumble Date, BFF, and Badoo.

In the employer’s words · Read in context · Company website ↗

Posting record

Report an error

Actively hiringFirst seen by us 2 days ago, still listed, and not a repost of an earlier posting.

Status in our records
Live
First seen by us
Oct 27, 2026 · 2 days ago
Recorded sightings
12
Last seen by us
Oct 30, 2026
Employer says posted
Oct 7, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

Your next step

  • Have your resume and examples of relevant work ready.
  • Check the listed location, eligibility and core experience before starting.
  • Ask the employer about the pay range before committing time to the process.

Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.

Already applied? Track this application

Job description

View original posting ↗

At Bumble, we’re building the Love Company — a place where healthy, equitable relationships and friendships can start and grow. Machine Learning sits at the heart of that mission, helping us understand what makes a meaningful connection and powering the matching, recommendation, and personalization experiences that bring people together.

As our Principal Machine Learning Engineer, Matching & Recommendations, you’ll help define the technical and ML strategy behind the next generation of Bumble’s recommendation systems. You’ll remain hands-on while tackling complex problems across retrieval, ranking, personalization, experimentation, and two-sided marketplace optimization, partnering across Engineering, Product, Data Science, and Analytics to turn ambiguous member problems into scalable ML solutions. You’ll lead through technical influence, raise the bar for ML engineering across the organization, and help us build systems that optimize not simply for engagement, but for better connections and meaningful member outcomes.

What you'll do

  • Define and lead the technical strategy for AI and Machine Learning systems that power recommendations, ranking, and personalization across Bumble products, delivering measurable improvements in user engagement and safety

  • Design, develop, and deploy production-grade models using modern ML frameworks such as PyTorch , ensuring scalability and reliability in high-traffic environments

  • Build and deploy production AI Agents using raw and fine-tuned foundational Large Language Models (LLMs), along with sub-agents, tools, and MCP integrations

  • Architect end-to-end ML pipelines, integrating data processing (e.g. Spark, Airflow) with model training, evaluation, and deployment workflows

  • Drive experimentation frameworks, including A/B testing and offline evaluation, to continuously improve model performance and product outcomes

  • Partner cross-functionally with Product, Engineering, and Data leadership to translate business challenges into impactful ML solutions, collaborating with purpose and influencing at senior levels

  • Mentor and elevate senior individual contributors, fostering a culture of Excellence, Curiosity, and continuous learning across the ML community

  • Take ownership of complex, ambiguous problem spaces, seeing initiatives through from insight to impact while adapting approaches with an agile mindset

  • Champion responsible AI practices, ensuring fairness, transparency, and user safety are embedded into all machine learning systems

About You

 
  • Typically requires 10–15 years of experience, though we welcome candidates with alternative backgrounds that demonstrate equivalent skills.

  • Deep expertise in machine learning, with hands-on experience building and deploying large-scale systems in production environments

  • Strong proficiency in Python and at least one major ML framework (e.g., PyTorch, TensorFlow), with experience in areas such as recommendation systems, ranking models, or NLP

  • Expertise in prompting and fine-tuning Large Language Models (LLMs) and building production AI Agents

  • Proven experience designing scalable data and ML pipelines using tools such as Spark, Airflow, or similar distributed systems

  • Demonstrated ability to operate as a senior individual contributor, influencing technical strategy and decision-making without direct authority

  • Experience partnering effectively across functions, collaborating with purpose, and taking ownership of outcomes in complex organizational environments

  • A track record of mentoring and uplifting others, role modeling Respect and Excellence while building inclusive, high-performing teams

  • Strong AI fluency, with the ability to independently design, evaluate, and optimize ML systems, and guide others in the responsible and effective application of AI

About Us

Bumble Inc. is the parent company of Bumble Date, BFF, and Badoo. The Bumble platform enables people to build healthy and equitable relationships, through Kind Connections. Founded by Whitney Wolfe Herd in 2014, Bumble was one of the first dating apps built with women at the center and connects people across dating (Bumble Date) and friendship (BFF). BFF is a friendship app where people in all stages of life can meet people nearby and create meaningful platonic connections and community based on shared interests. Badoo, which was founded in 2006, is one of the pioneers of web and mobile dating products.

AI Fluency

AI is important to us. We’re excited by people who are curious and experimental, and who think thoughtfully about how AI can amplify their impact and outcomes.

We encourage you to use AI responsibly as you prepare your application. Please don’t use it to fabricate experiences or answer questions live in interviews. We care deeply about authenticity and want to understand your real skills, judgment and voice, because building a meaningful, genuine connection with you matters to us.

Final Compensation

Will be determined based on factors such as the selected candidate’s qualifications, relevant experience, skill set, and other job-related considerations.

Inclusion at Bumble Inc.

Bumble Inc. is an equal opportunity employer and we strongly encourage people of all ages, color, lesbian, gay, bisexual, transgender, queer and non-binary people, veterans, parents, people with disabilities, and neurodivergent people to apply. We're happy to make any reasonable adjustments that will help you feel more confident throughout the process, please don't hesitate to let us know how we can help.

In your application, please feel free to note which pronouns you use (For example: she/her, he/him, they/them, etc).

AI in Bumble Inc. Hiring

At Bumble, we may use AI tools to support parts of our recruitment process — such as helping us record, transcribe, and summarize conversations, and supporting job alignment by comparing resumes and job descriptions to highlight skills and potential roles that may be a good match. These tools help us work more efficiently and stay focused on you during our conversations. Importantly, all hiring decisions are made by people. AI is used only to support our team’s efficiency and improve the candidate experience — not to evaluate or decide on your candidacy. Participation in AI-supported interviews and conversations is completely voluntary and will not impact your candidacy. If you’d prefer to opt out, simply let your recruiter or interviewer know at the start of a call, or anytime during the interview or conversation. Summaries and related data are retained only as long as needed in line with our internal data retention policies. If at any point you’d like a transcription or summary deleted, please contact your recruiter directly.

Fraudulent Candidate Detection

Our applicant tracking system analyzes signals relating to device, IP, email, and phone data associated with each application to protect applicants and our hiring process from fraudulent applications. These signals provide indicators for internal review only and do not constitute a definitive determination of identity or intent. No applicant is rejected, advanced, or otherwise affected based solely on this analysis without human review

For further information on how we hold and manage your data, please refer to our Privacy Policy.

Source details

View original posting ↗

Source notes

Source excerpts

Selected passages from the saved posting. Check the full description for conditions and exceptions.

Pay

No pay amount identified in the saved description.

Location & working pattern

Austin, Texas, United States

Working pattern and location restrictions need checking in the full posting.

Work authorization

No clear work-authorization passage found. Eligibility is unconfirmed.

See how this role fits your experience

Add your resume to compare the role’s scope, tools and requirements with your experience.

Find answers in the posting

AI
How answers work

AI selects complete passages from this posting. Check them for conditions and exceptions.

Uses this posting and your question. No profile needed.