Software Engineer Machine Learning
Software Engineer Machine Learning
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1 Vacancy
Job Description
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 Companys 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.
We are looking for a Machine Learning Software Engineer to join our ML tools and technology team at Snap Inc!
What youll do:
In this role youll 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
Masters 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 dont 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.
Employment Type
Full-Time
