Research Scientist Intern, Machine Learning for Human-Computer Interaction (PhD) - Meta
Redmond, WA
About the Job
Meta Reality Labs Research is seeking a highly motivated and skilled research intern to join our team in developing cutting-edge technologies for AR glasses and VR headsets. Our mission is to apply machine learning methods to problems in AR/VR input and interaction, with a focus on collaboration with domain experts.As a research intern, you will work on projects that span modeling, human-in-the-loop experimentation, and design of novel sensors and tracking algorithms. You will have access to cutting-edge technology, resources, and testing facilities, and will collaborate with a diverse and interdisciplinary team of researchers and engineers.Your primary focus will be on developing novel, robust, and sensor processing algorithms for wearables, using techniques such as self-supervised learning, robust decoding of biometric signals, confound mitigations, and sensor-fusion techniques. Your work will be at the intersection of signal processing, machine learning, and human-computer interaction, and you will have the opportunity to work with world-leading collaborators and mentors in these fields.Work with researchers to help enable their work across the following research disciplines: - Machine Learning for Human-Computer Action- AI Machine LearningOur internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year. Some projects may require a minimum of 16 consecutive weeks.
RESPONSIBILITIES
Research Scientist Intern, Machine Learning for Human-Computer Interaction (PhD) Responsibilities:
MINIMUM QUALIFICATIONS
Minimum Qualifications:
PREFERRED QUALIFICATIONS
Preferred Qualifications:
RESPONSIBILITIES
Research Scientist Intern, Machine Learning for Human-Computer Interaction (PhD) Responsibilities:
- Plan and execute cutting-edge research and design of novel adaptive algorithms for various sensory models.
- Implement signal processing, classification, fusion, and confounder mitigation algorithms and systems that are robust, efficient, easily tunable, and yield seamless interactions.
- Troubleshoot, debug, and upgrade existing software, contribute to code documentation and maintenance of the analysis pipeline.
- Collaborate with the larger research team, present and share results with key stakeholders.
- Advance the state of the art (SOTA) in sample-efficient modeling for understanding human cognition and action writ large.
- Use SOTA models to deliver advances in downstream experiments or demos – it has to work in real life.
- Successful internships may also lead to publishable outcomes in top-tier journals or at leading international conferences.
MINIMUM QUALIFICATIONS
Minimum Qualifications:
- Currently has, or is in the process of obtaining, a PhD in computer science, electrical or computer engineering, machine learning, state-estimation, robotics, or relevant technical field.
- 3+ years of experience with scientific programming languages such as Python or Matlab or similar.
- Experience with online and offline signal processing of biological time series data and sensor-fusion techniques.
- Excellent research skills involving defining problems, exploring solutions, and analyzing and presenting results.
- Interpersonal skills: cross-group collaboration and cross-culture collaboration. Be able to clearly articulate ideas and goals.
- Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.
PREFERRED QUALIFICATIONS
Preferred Qualifications:
- Familiarity with AR/VR technologies and literature.
- Experience with biometric or biosignal modeling for real-time interactive scenarios (e.g., gesture recognition, task inference).
- Experience with machine learning and related frameworks, such as sklearn or Pytorch.
- Experience working directly with domain experts in non-ML fields, for example mechanical engineers or designers, neuroscientists, physicists, etc.
- Experience writing research software used by others, for example as part of an academic collaboration, an open-source project, or equivalent.
- Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as CVPR, ICML, NeurIPS, SIGGRAPH, Society for Neuroscience, or similar.
- Experience working and communicating cross functionally in a team environment.
- Intent to return to degree program after the completion of the internship/co-op.
- Availability for minimum 16 consecutive week internship.
Source : Meta