Navigating Digital Domain: A Guide to Internship Requirements and Opportunities
Gaining experience in the digital domain, particularly in fields like visual effects, computer science, and related technologies, often begins with internships. These programs provide invaluable real-world experience, networking opportunities, and a pathway to potential career advancement. This article explores the requirements and opportunities available in digital domain internships, drawing on examples from various organizations and programs.
Internship Programs Overview
Internship programs are designed to provide students and recent graduates with practical experience related to their academic and career goals. They offer a chance to apply theoretical knowledge in a professional setting, develop new skills, and build a professional network.
For instance, the Internal Services Department (ISD) in Los Angeles County initiated the Delete The Divide Internship Program in September 2022. This program, certified as a pre-apprenticeship under California Labor Code § 3100, offers civil service appointments and connects interns with IT apprenticeships nationwide. Interns in this program may participate in conferences, corporate tours, and community outreach events, preparing them for careers in computer and information technology. The program offers two employment opportunities in the roles of Technology Professional Intern I (TPI I) and Technology Professional Intern II (TPI II).
The EarthScope Student Career Internship Program offers real-world work experiences for undergraduate students, graduate students, and recent graduates (within 1 year of graduating at time of application) related to the interns’ academic and career goals. Project managers teach interns what it is like to work in a non-profit organization that facilitates geoscience research and education. Internship assignments vary each year based on business needs. Managers identify a specific scope of work, designate a role, and create a statement of work for the intern. The intern will be assigned real-world work, with an impact on our organization and the community we serve. The intern will receive a company orientation and then collaborate with their manager to develop a work and learning plan that aligns with the statement of work. Before beginning work, interns will receive appropriate training, including compliance and safety training if needed. In addition to a paid weekly stipend, EarthScope provides many formal and informal learning opportunities; these include team meetings and various company meetings.
Digital Domain: A Leader in Visual Effects and Immersive Experiences
Digital Domain stands out as a global visual effects studio renowned for its innovation and contributions to iconic films, advertising, and experiential entertainment. The studio's work includes blockbuster films like “Avengers: Infinity War” and “Ready Player One.” Beyond film, Digital Domain excels in creating immersive experiences, including holograms, location-based entertainment, and next-generation video games. The studio also has an award-winning Digital Human Group (DHG) responsible for pioneering the art and technology of digital and virtual humans for all media delivery platforms.
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Given its diverse services and operations, Digital Domain attracts top artists, technologists, and creatives. The studio typically offers multiple internship cohorts throughout the year.
Internship Opportunities at Mitsubishi Electric Research Laboratories (MERL)
Mitsubishi Electric Research Laboratories (MERL) offers a variety of research internships for graduate students and PhD candidates. These internships focus on cutting-edge research areas, including computer vision, control systems, robotics, machine learning, and signal processing.
Guidance, Navigation, and Control for Spacecraft
MERL seeks a highly motivated intern for a research position in guidance, navigation, and control of spacecraft. The ideal candidate is a PhD student with experience in one or more of the following topics: astrodynamics, relative motion dynamics, rendezvous, attitude dynamics and control, covariance steering, nonlinear estimation, sequential convex programming, and optimization-based control. Publication of results produced during the internship is expected. The duration of the internship is 3-6 months, and the start date is flexible.
Required Specific Experience:
- Current enrollment in a PhD program in Aerospace, Mechanical, Electrical Engineering, or a related field
- Strong programming skills in Matlab, Python, Julia, and/or C/C++
Full-Stack GNC Simulators for Space Applications
MERL is seeking a highly motivated graduate student to develop high-fidelity full-stack GNC simulators for space applications. The ideal candidate has strong experience with rendering engines, synthetic image generation, and computer vision, as well as familiarity with spacecraft dynamics, motion planning, and state estimation. The developed software should allow for closed-loop execution with the synthetic imagery, and ideally allow for real-time visualization. Publication of results produced during the internship is desired. The expected duration of the internship is 3-6 months with a flexible start date.
Required Specific Experience:
- Current enrollment in a graduate program in Aerospace, Computer Science, Robotics, Mechanical, Electrical Engineering, or a related field
- Experience with one or more of Blender, Unreal, Unity, along with their APIs
- Strong programming skills in one or more of Matlab, Python, and/or C/C++
Safety-Oriented Active SLAM System for Aerial Robots
MERL is seeking a self-motivated and highly qualified Ph.D. intern to contribute to the development of a safety-oriented active SLAM system for aerial robots. The work will involve the development of perception-aware safe planning algorithms, along with extensive validation in both simulation and on hardware, using drones equipped with onboard cameras. The intern will work closely with MERL researchers in robotics and autonomy. The internship is expected to lead to a publication in a top-tier robotics, computer vision, or control conference and/or journal. The position has a flexible start date (Summer/Fall 2026) and a duration of 3-6 months.
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Required Specific Experience:
- Current enrollment in a Ph.D. program in Mechanical Engineering, Electrical Engineering, Aerospace Engineering, Computer Science, or a closely related field, with a focus on Robotics, Computer Vision, and/or Control Systems.
- Hands-on experience with aerial robots, including real-world flight testing.
- Expertise in one or more of the following areas: active SLAM; 3D computer vision; coverage path planning; multi-agent pathfinding; perception-aware planning.
- Excellent programming skills in Python and/or C++, with prior experience using ROS2 and high-fidelity simulators such as Isaac Sim and/or MuJoCo.
- A strong publication record or demonstrated research potential in leading computer vision or robotics venues, such as ICRA, IROS, RSS, RA-L, T-RO, CVPR, ECCV, ICCV, or NeurIPS.
Preferred Experience:
- Strong software engineering skills, demonstrated through a publicly accessible codebase (e.g., GitHub or GitLab). Applicants are required to provide links to representative repositories.
- Experience with onboard perception, visual-inertial systems, or safety-critical autonomy.
- Familiarity with trajectory optimization, MPC, or optimization-based control for robots.
Sensor Reasoning Models
The Computation Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research on sensor reasoning models-algorithms that can understand, explain, and act on multi-sensor data (e.g., RF, infrared, LiDAR, event camera) through text-, visual-, and multimodal reasoning. Ideal candidates will be comfortable bridging modern perception (detection/segmentation/tracking) with higher-level reasoning capabilities. Experience with text, visual, and multimodal reasoning is highly preferred. The intern will work closely with MERL researchers to develop novel algorithms, design experiments using MERL’s in-house testbeds, and prepare results for patents and publication. The internship is expected to last 3 months, with a flexible start date from October 2025 onward.
Required Specific Experience:
- Reasoning with sensor data: Demonstrated work in text-, visual-, and multimodal reasoning (e.g., VQA over sensor streams, temporal/spatio-temporal reasoning, chain-of-thought, instruction following).
- LLMs & VLMs for sensor perception: Experience aligning or conditioning LLMs/VLMs on sensor outputs (e.g., point clouds, radar heatmaps, BEV features).
- Perception foundations: Solid understanding of state-of-the-art transformer-based (e.g., DETR) and diffusion-based (e.g., DiffusionDet) frameworks
- Datasets & evaluation: Hands-on experience with open large-scale multi-sensor datasets (e.g., nuScenes, Waymo Open Dataset, Argoverse) and open radar datasets (e.g., MMVR, HIBER, RT-Pose, K-Radar). Ability to design reasoning-centric benchmarks (e.g., QA over multi-sensor inputs, temporal prediction).
- Proficiency in Python and deep learning frameworks (PyTorch/JAX), plus experience with GPU cluster job scheduling and scalable data pipelines.
- Proven publication record in top-tier venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML (or equivalent).
- Knowledge of sensor (RF, infrared, LiDAR, event camera) fundamentals; for radar, familiarity with FMCW, MIMO, Doppler signatures, radar point clouds/heatmaps, and raw ADC waveforms.
- Familiarity with MERL’s recent radar perception research, e.g., TempoRadar, SIRA, MMVR, RETR.
Data-Driven Estimation and Control for Spatiotemporal Dynamical Systems
MERL is seeking an intern to work on data-driven estimation and control for spatiotemporal dynamical systems, with applications in indoor airflow optimization. The ideal candidate would be a PhD student in engineering, computer science, or related fields with a strong background in estimation, control, and dynamical systems theory. Preferred skills include knowledge of reinforcement learning, reduced-order modeling (ROM) and partial differential equations (PDEs). The intern will work closely with MERL researchers to develop novel algorithms, conduct numerical experiments, and prepare results for publication. The duration is expected to be at least 3 months with a flexible start date.
Interacting Particle Systems for Solving Inverse Problems
The Computational Sensing Team at MERL is seeking an intern to work with MERL researchers on algorithms based on interacting particle systems for solving inverse problems. The focus of the project is particle-efficiency and applicability to non-log-concave posterior distributions (which may result from nonlinear forward operators). The project includes algorithm design, (finite-particle) convergence analysis, and/or empirical evaluation for challenging inverse problems such as full waveform inversion. The ideal candidate would be a PhD student with a solid background in applied probability or Bayesian sampling. Programming skills in Python or MATLAB are required. The duration is anticipated to be at least 3 months with a flexible start date.
Multi-Modal Sensing and Understanding
The Computational Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research on multi-modal sensing and understanding -algorithms that can understand, explain, and act on multi-sensor data (e.g., RF, infrared, LiDAR, event camera). Ideal candidates will be comfortable bridging state-of-the-art perception (detection/segmentation/tracking) with higher-level semantic understanding and reasoning capabilities. Experience with text, visual, and multimodal reasoning is a plus. The intern will work closely with MERL researchers to develop novel algorithms, design experiments using MERL’s in-house testbeds, and prepare results for patents and publication. The internship is expected to last 3 months, with a flexible start date.
Required Specific Experience:
- Expertise in physical sensing across RF (radar, UWB, Wi-Fi), infrared, LiDAR, and event-camera modalities.
- Experienced with radar systems and concepts including FMCW and MIMO configurations, Doppler signature interpretation, radar point cloud and heatmap representations, and raw ADC waveforms;
- Solid understanding of state-of-the-art transformer-based (e.g., DETR) and diffusion-based (e.g., DiffusionDet) frameworks;
- Demonstrated work in text-, visual-, and multimodal semantic understanding and reasoning.
- Hands-on experience with open large-scale multi-sensor datasets (e.g., nuScenes, Waymo Open Dataset, Argoverse) and open radar datasets (e.g., MMVR, HIBER, RT-Pose, K-Radar).
- Proficiency in Python and deep learning frameworks (PyTorch/JAX), plus experience with GPU cluster job scheduling and scalable data pipelines.
- Proven publication record in top-tier venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML (or equivalent).
Generative Audio, Source Separation, Speech Enhancement, and Robust ASR
MERL is seeking graduate students interested in helping advance the fields of generative audio, source separation, speech enhancement, and robust ASR in challenging multi-source and far-field scenarios. The interns will collaborate with MERL researchers to derive and implement new models and optimization methods, conduct experiments, and prepare results for publication. Internships regularly lead to one or more publications in top-tier venues, which can later become part of the intern's doctoral work. The ideal candidates are senior Ph.D. students with experience in some of the following: audio signal processing, microphone array processing, probabilistic modeling, and deep generative modeling. Multiple positions are available with flexible start dates (not just Spring/Summer but throughout 2026) and duration (typically 3-6 months).
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AI-Augmented Optimization
MERL seeks a talented individual for a joint research project in AI-augmented optimization: Using LLMs and related technologies to aid in the formulation, transformation, and accelerated solution of typical Operations Research optimization problems. Depending on intern skill set, the project may also consider post-optimization enhancements - explanation, robustification, and generation of alternatives. The ideal applicant will have technical background and research experience on both sides (AI & OR) of the project, including familiarity with software environments used to do machine learning & mathematical programming. The internship has flexible dates in 2026.
The strongest candidates will have:
- A solid background in machine learning, linear algebra, convex optimization, algorithm analysis, computational geometry.
- Experience with Mixed-Integer Programs (linear, nonlinear, nonconvex)
- Experience deploying and fine-tuning LLM-type AI systems.
- Competence with related software packages such as Gurobi, CVX, PyOpt, PyTorch, etc.
- Fluency in python.
- Interest (& experience) in publishing in top-tier venues.
Solving Mixed-Integer Quadratic Programs via Continuous Dual Formations
MERL's OR group is seeking a talented individual to collaborate in an ongoing research into solving Mixed-Integer Quadratic Programs via continuous dual formations, e.g., co-positive programs.
The ideal application will have:
- Experience with Mixed-Integer Programs (preferably all of: linear, nonlinear, nonconvex)
- Competence with related software packages such as Gurobi, CVX, PyOpt, etc.
- Fluency in python.
- A solid background in linear algebra, convex optimization, algorithm analysis, computational geometry.
The internship will run 3-4 months and is available immediately; applicants with early 2026 availability will be given favorable consideration in hiring.
Transformer-Informed Stochastic MPC to Control Net-Zero Energy Buildings
MERL is looking for a research intern to develop efficient transformer-informed stochastic MPC to control net-zero energy buildings. This is an exciting opportunity to make a real impact in the field of cutting-edge deep learning and predictive control on a real system. Publication of results produced during the internship is desired. The expected duration of the internship is 3-6 months with a flexible start date.
The Ideal Candidate Will Have:
- Significant hands-on experience with stochastic MPC
- Publications in SMPC are a strong plus
- Fluency in Python and PyTorch
- Understanding of probabilistic time-series prediction
- Experience with convex programming
- Convex formulations of MPC/SMPC problems are a strong plus
- Completed their MS, or >50% of their PhD program
Multimodal Scene Understanding
MERL is looking for a graduate student interested in advancing the field of multimodal scene understanding, focusing on scene understanding using natural language for robot dialog and/or indoor monitoring with a large language model. The intern will collaborate with MERL researchers to derive and implement new models and optimization methods, conduct experiments, and prepare results for publication. Internships regularly lead to one or more publications in top-tier venues, which can later become part of the intern's doctoral work. The ideal candidates are senior Ph.D. students with experience in deep learning for audio-visual, signal, and natural language processing. Good programming skills in Python and knowledge of deep learning frameworks such as PyTorch are essential. Multiple positions are available with a flexible start date (not just Spring/Summer but throughout 2026) and duration (typically 3-6 months).
Required Specific Experience:
- Experience with ROS2, C/C++, Python
EarthScope Consortium Internship Opportunities
The EarthScope Consortium offers a Student Career Internship Program providing real-world work experiences for students and recent graduates. These internships are full-time for at least 11 weeks and typically offer remote options, depending on the position duties. The intern will be responsible for educational resources development, technical support documentation, and data processing/presentation. Example roles include:
Cloud OnRamp Intern
The Cloud OnRamp Intern will support EarthScope’s Cloud Research OnRamp initiative by contributing to the development and curation of cloud-based Jupyter Notebook resources used in geophysical research and training. The intern will work with the GeoLab JupyterHub platform to organize domain-specific example notebooks, improve documentation, and support short courses that introduce researchers to cloud-native workflows.
Software Engineering Intern
EarthScope benefits the geophysical community by providing software, instrumentation, and data processing expertise which enable the deployment of large scale experiments. EarthScope is seeking a talented and ambitious software engineering intern that will help to develop the software tools that make this possible. In addition, EarthScope is moving to AWS, so we seek you as the intern to apply skills to both on-prem and cloud systems as we migrate and scale fully to the cloud.
Computing and Data Science Academy Intern
The Computing and Data Science Academy Intern will support EarthScope’s technical courses by developing, improving, and consolidating pre-course modules that prepare participants for successful learning experiences. The intern will focus on building foundational instructional materials that ensure students arrive ready to engage with technical content.
Science Communication Intern
Intern will assist in creating content for outreach-focused science communication to broad audiences, as well as content to highlight EarthScope’s science-support activities to stakeholder communities. Specific projects will be selected based on skillset, interest, and growth goals. Tasks may include contributing to organizational news stories and science summaries, interviewing other interns, creating and posting content on social media, updating and creating webpages, digital or print graphic design, and creating videos. Successful applicants will have basic technical skills working in an online environment, be highly organized, be an enthusiastic learner, and have excellent attention to detail.
Seismology Skill Building Workshop (SSBW) Intern
The Seismology Skill Building Workshop (SSBW) Intern will support the modernization of EarthScope’s flagship seismology training program. Working with EarthScope staff and SSBW instructors, the intern will help migrate course materials into EarthScope’s Moodle learning platform and GeoLab JupyterHub computing environment. The role focuses on instructional design, aligning training with scientific infrastructure, curriculum organization, and assessment and evaluation.
General Requirements and Expectations
While specific requirements vary by organization and role, some common expectations exist for digital domain internships:
- Educational Background: Most internships require current enrollment in a relevant undergraduate or graduate program. For research-focused roles, PhD students are often preferred.
- Technical Skills: Strong programming skills in languages like Python, C++, MATLAB, and Julia are frequently required. Experience with specific software or frameworks relevant to the role is also beneficial.
- Research Experience: For research internships, a publication record or demonstrated research potential in relevant venues is highly valued.
- Specific Experience: Hands-on experience with relevant technologies, such as aerial robots, sensor systems, or deep learning frameworks, is often a key requirement.
- Soft Skills: Strong communication, collaboration, and problem-solving skills are essential for success in any internship.
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