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’s three core products are Snapchat, a visual messaging app that enhances your relationships with friends, family, and the world; Lens Studio, an augmented reality platform that powers AR across Snapchat and other services; and its AR glasses, Spectacles.

The Spectacles team is pushing the boundaries of technology to bring people closer together in the real world. Our fifth-generation Spectacles, powered by Snap OS, showcase how standalone, see-through AR glasses make playing, learning, and working better together.

We are looking for a Machine Learning Software Engineer to join our ML tools and technology team at Snap Inc!

What you’ll do:

In this role, you’ll help develop the next generation of on-device intelligence for Spectacles AR glasses. You will not only design and implement cutting-edge ML algorithms, but also build the infrastructure, tools, and workflows that make ML development, deployment, and monitoring at scale possible. Your work will enable seamless, real-time AR experiences, pushing the limits of performance, reliability, and efficiency on diverse hardware platforms. The ideal candidate brings a strong background in software engineering and computer vision, along with hands-on experience developing machine learning optimization algorithms and infrastructure for diverse hardware platforms.

• Design and implement ML workflows and infrastructure for training, fine-tuning, evaluating, and deploying models for AR and on-device applications, with a focus on computer vision and large language models (LLMs).

• Develop and extend ML optimization pipelines, model transformation tools, and runtimes to enable efficient deployment on AR hardware platforms.

• Build and maintain MLOps pipelines for automated training, testing, validation, monitoring, and continuous deployment of ML models.

• Explore and implement advanced model optimizations such as quantization, sparsity and compression techniques, ensuring models meet stringent on-device real-time and power constraints.

• Design benchmarking tools to evaluate correctness, robustness, and performance of ML solutions across hardware and software platforms.

• Collaborate with cross-functional teams to prototype, test, and validate new hardware acceleration approaches, driving them to production.

Knowledge, Skills & Abilities:

• Ability to contribute across the end-to-end lifecycle of machine learning solutions, including design, training, optimization, deployment, testing, and monitoring.

• Strong desire in advancing the internals of ML tooling, such as writing custom operators, improving runtime performance, and building scalable infrastructure for diverse hardware accelerators.

• Proven skill in developing efficient, reliable, and adaptable ML systems that scale across evolving architectures.

• Experience designing scalable training and evaluation systems with a focus on reproducibility and reliability.

• Deep understanding of quality assurance practices to validate ML performance across diverse environments and deployment contexts.

• Capacity to advance team-wide technical maturity by contributing to compilers, SDK integrations, and architectural design that support on-device intelligence at scale.

• Strong communication and collaboration skills, with the ability to align technical innovation with product needs.
  

Minimum Qualifications

• Master’s degree or PhD in Computer Science, Electrical/Computer Engineering, or a related technical field

• 3+ years of professional experience in the field of software engineering.

• 2+ years of experience in testing, deploying, and monitoring production ML systems.

• Proficiency with software development in Python or C++.

• Experience with machine learning frameworks (PyTorch, TensorFlow, etc.) and cloud platforms (GCP, AWS, etc).

Preferred Qualifications:

• Understanding of large language models, NLP and/or multimodal modeling.

• Experience with on-device ML SDKs/tooling (e.g., TensorFlow Lite, ExecuTorch, Core ML, SNPE/QNN).

• Experience in one or more of the following areas: ML performance and efficiency tuning, compiler optimization for ML workloads, hardware-accelerated ML inference, low-level programming models, or distributed ML systems optimization.

• Familiarity with QA automation frameworks and benchmarking at scale.

• Familiarity with the architectural patterns of large-scale software applications.

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!