Machine Learning Engineer

Posted 7 Hours Ago
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Paris, Île-de-France, FRA
Hybrid
Mid level
Artificial Intelligence • Cloud • Machine Learning • Mobile • Software • Virtual Reality • App development
Snap is a technology company.
The Role
Develop and deploy machine learning models and systems for time-series wearable sensing and brain-computer interface applications. Collaborate with research, software, and hardware teams to integrate algorithms, resolve issues, and evaluate model performance. Build training and testing tools, write high-quality code, communicate technical tradeoffs, and apply advanced ML techniques to physiological sensor data and real-time systems.
Summary Generated by Built In

Snap Inc is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together.


The Company operates Snapchat, a visual messaging app that enhances your relationships with friends, family, and the world, and Specs Inc., a wholly-owned subsidiary dedicated to making computing more human, in addition to Bitmoji, Saturn, and other digital services.


Specs Inc. is a wholly-owned subsidiary of Snap Inc. dedicated to making computing more human. The company develops Specs, advanced eyewear that seamlessly integrates digital experiences into the physical world.


Specs feature see-through lenses that place digital objects directly into three-dimensional space, powered by Snap OS, a proprietary, context-aware operating system designed for natural interaction with your hands and voice.


Specs Inc. also provides Lens Studio, a full suite of advanced developer tools that powers immersive augmented reality experiences across Specs, Snapchat, and other services.


We’re looking for a Machine Learning Engineer to join the Brain-Computer Interface team at Snap Inc!


In this role, you will be working on the state of the art of machine learning (ML) for time series data, developing the next generation of wearable device sensing. Working from our Paris office (9th arrondissement), you will be collaborating closely with other Specs hardware and software teams around the world.


What you’ll do:


  • Design, implement and deploy ML models and systems
  • Collaborate across software and research teams to deploy new ML systems and algorithms, and resolve integration issues 
  • Develop tools for training, testing and evaluating ML algorithm performance
  • Write clean, well designed and tested code
  • Communicate technical tradeoffs clearly through design docs and reviews 

Knowledge, Skills & Abilities:


  • Strong knowledge in Python 
  • Deep understanding of ML principles, algorithms and systems
  • Ability to understand, debug and improve existing code as well as develop new algorithms using advanced time series and ML techniques
  • Great spoken and written communication skills; ability to clearly explain complex ideas in a cross-functional team through design docs and presentations

Minimum Qualifications: 


  • MSc in Machine Learning or Computer Science, or equivalent field
  • Strong knowledge in Python or C++
  • 3+ years of research or engineering experience with ML approaches
  • Experience with ML frameworks (e.g. PyTorch, mlflow), as well as with cloud environments (Google Cloud, AWS).
  • Fluency in English
  • Experience with engineering workflows including version control (git) and code reviews
  • Familiarity with AI agents to support engineering and ML workflows 

Preferred Qualifications:


  • PhD in Machine Learning or Computer Science, or equivalent field
  • Expertise in algorithms for digital signal processing, time series, EEG or other physiological sensors data
  • Experience with real-time ML and sensor systems
  • Experience with continuous integration and code quality tools
  • Experience developing AI agentic tools to support engineering workflows and ML experimentation

If you have a disability or special need that requires accommodation, please don’t be shy and provide us some information.

"Default Together" Policy at Snap: At Snap Inc. we believe that being together in person helps us build our culture faster, reinforce our values, and serve our community, customers and partners better through dynamic collaboration. To reflect this, we practice a “default together” approach and expect our team members to work in an office 4+ days per week. 

At Snap, we believe that having a team of diverse backgrounds and voices working together will enable us to create innovative products that improve the way people live and communicate. Snap is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws. EOE, including disability/vets.

Our Benefits: Snap Inc. is its own community, so we’ve got your back! We do our best to make sure you and your loved ones have everything you need to be happy and healthy, on your own terms. Our benefits are built around your needs and include paid parental leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap’s long-term success!

Skills Required

  • MSc in Machine Learning, Computer Science, or an equivalent field
  • At least 3 years of research or engineering experience with machine learning approaches
  • Strong knowledge of Python or C++
  • Experience with machine learning frameworks such as PyTorch and MLflow
  • Experience with cloud environments such as Google Cloud or AWS
  • Fluency in English
  • Experience with version control, including Git, and code reviews
  • Familiarity with AI agents supporting engineering and machine learning workflows
  • PhD in Machine Learning, Computer Science, or an equivalent field
  • Expertise in digital signal processing, time-series algorithms, EEG, or physiological sensor data
  • Experience with real-time machine learning and sensor systems
  • Experience with continuous integration and code quality tools
  • Experience developing AI agentic tools for engineering workflows and machine learning experimentation

What the Team is Saying

Xiaolin
Yvette
Matt
Jasmeet
Xueyin (Sherry)
Amir
Jung
Xu
Talia Mason
Maureen Ufomadu
Vincent Pagnard-Jourdan
Pulkit Trivedi

Snap Inc. Compensation & Benefits Highlights

  • Healthcare Strength — Health coverage includes multiple medical plan options (PPO/HDHP/HMO), dental and vision, HSA/FSA, mental-health support (e.g., Lyra), and wellness programs. Family-building benefits and services like One Medical are also part of the package.
  • Parental & Family Support — Parental leave is described as generous—up to 26–28 weeks for birthing parents and up to 16 weeks for non-birthing parents—alongside adoption, surrogacy, fertility preservation, backup childcare, caregiver assistance, and return-to-work support.
  • Retirement Support — A 401(k) with company match (immediate vesting cited), plus pre-tax, Roth, and after-tax contributions including a Mega Backdoor Roth option, supports long-term savings. Additional financial wellness tools complement the retirement program.

Snap Inc. Insights

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The Company
HQ: Santa Monica, CA
5,000 Employees
Year Founded: 2011

What We Do

We contribute to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together.

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Snap Inc. Teams

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About our Teams

Snap Inc. Offices

Hybrid Workspace

Employees engage in a combination of remote and on-site work.

Our “default together” approach is an 80/20 model where we are asking team members to spend 80% of the time, on average, in the office, with the remaining 20% of the time spent remote.

Typical time on-site: 4 days a week
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