Senior Machine Learning Engineer at Itaú, driving innovation with data and AI solutions. Collaborating across teams to implement robust machine learning architectures and ensure scalable deployments.
About the role
- ML Research & Systems Intern at Cirrus Venture Labs developing scalable AI/ML innovations across Edge, Voice, Sense, and Control domains.
Responsibilities
- Rapid Prototyping: Assist in building and testing ML models for edge intelligence, specifically focusing on audio, sensor, and control signals.
- Data & Model Engineering: Support the design of model architectures and contribute to data labeling strategies, synthetic data generation, and augmentation pipelines.
- Edge Optimization: Explore and implement model compression techniques—such as quantization, pruning, and knowledge distillation—to ensure models run efficiently on embedded systems.
- Exploration & Benchmarking: Stay current on foundation/SLM trends and academic research; help define benchmarks and evaluation metrics to measure the success of CVL’s ML prototypes.
- Cross-Functional Collaboration: Partner with firmware, silicon, and systems engineers to understand the physical constraints of hardware and how they impact algorithmic accuracy.
Requirements
- Educational Background: Currently enrolled in a Master’s or Ph.D. program in Computer Science, Electrical Engineering, or a related field with a focus on ML/AI.
- Technical Depth: Strong foundational understanding of CNNs, RNNs (LSTMs), or Transformer-based architectures.
- Programming Proficiency: Hands-on experience with Python and ML frameworks (e.g., PyTorch, TensorFlow, JAX).
- Signal Processing Exposure: Basic familiarity with processing time-series, audio, or sensor data.
- Curiosity & Adaptability: Eager to work in a high-ambiguity "startup" environment within a larger corporation.
- Communication: Ability to explain complex technical findings to a multi-disciplinary team of hardware and software engineers.
- Embedded Interest: Experience or coursework in C/C++ or working with resource-constrained hardware (e.g., Raspberry Pi, ESP32, ARM Cortex-M).
- Optimization Tools: Familiarity with TFLite, ONNX, or similar edge deployment toolchains.
- Research Record: Previous research experience or publications in areas like anomaly detection, reinforcement learning, or generative AI for signal processing.
- Open Source: Contributions to open-source ML projects or active participation in academic labs.
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
Machine Learning Engineer responsible for developing and deploying advanced ML and AI solutions at Zendesk. Collaborating with stakeholders to deliver impactful business outcomes using latest machine learning technologies.
Lead advanced machine learning model development and optimization at PayPal. Collaborate with teams to deploy scalable ML solutions in production environments.
Senior Machine Learning Engineer at Pivotal Health developing ML systems for healthcare reimbursement. Collaborating across teams to build and maintain reliable, production - grade machine learning systems.
Machine Learning Engineer working with Algorithm team on customer onboarding processes. Focus on execution and automation of models using computer vision and AI in sports industry.
Senior Machine Learning Engineer at Troveo designing and optimizing machine learning pipelines for AI video models. Collaborating with cross - functional teams to build scalable video data solutions.
AI/ML Engineer developing AI systems for data security at Nightfall. Collaborating with engineers and contributing to AI features.
Internship in cheminformatics and machine learning at Genesis Molecular AI, driving drug discovery projects and improving internal tools.
Software Engineer focusing on ML infrastructure for drug discovery at Genesis AI. Leading engineering efforts to enhance scalable platforms for generative modeling and large - scale simulations.
AI/ML Engineer developing machine learning systems for TymeX's digital banking platform. Collaborating across teams to enhance customer interaction and personalization through AI technology.