• Build, train, and ship ML models for identity verification use cases such as biometric matching, liveness / anti-spoofing, identity document processing (OCR/extraction), and fraud detection (team assignment based on experience). • Prepare large, noisy datasets: ingestion, validation, cleaning, deduplication, labeling strategy, and dataset QA to improve model performance and reliability. • Design experiments, evaluation protocols, and success metrics (offline and online), iterate based on measurable business impact (detection rates, fraud losses, false positives). • Develop production-grade training and inference pipelines on AWS with strong reproducibility, monitoring, and cost controls. • Productionize models as resilient services and libraries in Python; collaborate with platform teams on APIs, latency and observability. • Contribute to the transformation of our IDV engine: modernizing legacy components, improving modularity, and raising quality, performance, and maintainability. • Work closely with Product, Customer Success, and Platform Engineering teams to ensure ML solutions meet privacy, compliance, and reliability requirements. • Support other engineers through design reviews, code reviews, and knowledge sharing; help raise the technical bar across the team.

Description Infotrend Summer Internship: During this internship, you will play a pivotal role in accelerating a fast-moving project encompassing software stack development, Artificial Intelligence, Computer Vision, and network interoperability. Internship will be 8 weeks long from June to Early August. This internship project focuses on continuing the development of an LLM+VLM tool that supports both multimodal content and external enterprise data sources. The objective is to give users a unified way to ask questions, analyze documents, and build retrieval workflows, whether the data comes from local files, images, scanned documents, and external sources. The intern(s) will join an existing team to continue the framework that allows the platform to securely connect to external systems. This includes support for authentication, configuration, connection health checks, source discovery, and document ingestion into existing analysis and RAG pipelines. The project will emphasize clean architecture so new connectors can be added later with minimal changes to the core platform. Attention will be given to multi-user and multi-organization requirements such as role-based access control, token-based access, and separation of data between organizations and users. Because some tasks may take time to complete, the internship will integrate a job management model for long-running operations such as batch analysis, ingestion, and indexing. This includes thinking about queue visibility, task status, and scalable execution in a multi-user environment. This project is well-suited for a student interested in AI systems, backend development, API design, multimodal AI, and secure enterprise integration. A strong understanding of distributed and concurrent systems, Docker, Python, REST, LLM/VLM API usage, and on-premises infrastructure is expected of candidates. By the end of the internship, the students will present the features and abilities added to corporate leaders. Expected Impact: - Improves users’ ability to query, analyze, and retrieve information from a wider range of sources through a unified interface. - Strengthens the platform’s security, scalability, and tenant isolation for real organizational use. - A reusable foundation for future connectors and future enterprise integrations. - Improves operational efficiency through batch processing, indexing, and queue-aware job execution. Requirements - A strong understanding of distributed and concurrent systems, Docker, Python, REST, LLM/VLM API usage, and on-premises infrastructure is expected of candidates. - Must be currently enrolled in an accredited college or university pursuing a degree in Computer Science, Software Engineering, Artificial Intelligence, or a related field Fields/ Disciplines Used: - Artificial intelligence (AI) fundamentals - Multimodal AI / vision-language models (VLMs) - Large language models (LLMs) - Computer vision concepts - Retrieval-augmented generation (RAG) - Backend software engineering - API design and integration - Distributed and concurrent systems - Network interoperability - Enterprise systems integration - Identity and access management (RBAC) - Cloud and on-prem infrastructure - Containerization using Docker This internship offers a stipend of $1,000 per month. This is a paid internship opportunity and does not include additional compensation or benefits such as health insurance, retirement plans, or paid time off. The internship is designed to provide hands-on experience, skill development, and exposure to real-world projects in a professional environment.

Role Description As a Machine Learning Researcher at Bland, you'll be working on foundational research and development across the core components of our voice stack: speech-to-text, large language models, neural audio codecs, and text-to-speech. Your work will define how our agents understand, reason, and speak in real time at enterprise scale. This is not a narrow research role. You will take ideas from theory to large-scale training to production inference systems serving millions of calls per day. You will design new modeling approaches, validate them with rigorous experimentation, and collaborate with engineering teams to deploy them into real customer environments. What You Will Do - Build and Scale Next-Generation TTS Systems - Design and train large scale text-to-speech models capable of expressive, controllable, human-sounding output. - Develop neural audio codec-based TTS architectures for efficient, high-fidelity generation. - Improve prosody modeling, question inflection, emotional expression, and multi-speaker robustness. - Optimize for real-time, low-latency inference in production. - Advance Speech-to-Text Modeling - Build and fine-tune large scale ASR systems robust to accents, noise, telephony artifacts, and code switching. - Leverage self-supervised pretraining and large-scale weak supervision. - Improve transcription accuracy for real-world enterprise scenarios, including structured extraction and conversational nuance. - Pioneer Neural Audio Codecs - Research and implement neural audio codecs that achieve extreme compression with minimal perceptual loss. - Explore discrete and continuous latent representations for scalable speech modeling. - Design codec architectures that enable downstream generative modeling and controllable synthesis. - Develop Scalable Training Pipelines - C curate and process massive audio datasets across languages, speakers, and environments. - Design staged training curricula and data filtering strategies. - Scale training across distributed GPU clusters focusing on cost, throughput, and reliability. - Run Rigorous Experiments - Design ablation studies that isolate the impact of architectural changes. - Measure improvements using both objective metrics and perceptual evaluations. - Validate ideas quickly through focused experiments that confirm or eliminate hypotheses. Qualifications - Experience with self-supervised learning, multimodal modeling, or generative modeling. - Ability to derive new formulations and implement them efficiently. - Hands-on experience building or scaling TTS, STT, or neural audio codec systems. - Familiarity with large scale speech datasets and real-world audio variability. - Strong intuition for audio quality, prosody, and conversational dynamics. - Experience training and serving large models on modern accelerators. - Knowledge of inference optimization techniques, including quantization, kernel optimization, and memory efficiency. - Understanding of real-time constraints in telephony or streaming environments. - Track record of designing controlled experiments and meaningful ablations. - Comfortable working with both offline benchmarks and live production metrics. - Ability to move quickly from hypothesis to validation. - Comfortable in fast-moving startup environments. - Strong ownership mindset from research through deployment. - Excited by ambiguous, unsolved problems. Benefits - Healthcare, dental, vision, all the good stuff - Meaningful equity in a fast-growing company - Every tool you need to succeed - Beautiful office in Jackson Square, SF with rooftop views - Competitive salary: $160,000 to $250,000

• Own the Visual Gateway: Deliver the features that identify the first thing millions of users see when they open our apps. • Drive Systematic Quality: Improve the reliability and velocity of our experimentation systems, ensuring our visual tests are statistically sound and performant. • Scale Discovery: Build the components that allow us to personalize the 'look and feel' of the platform, not just the content list. • Feature & Component Ownership: Design and implement specific solutions for Multi-Armed Bandit (MAB) systems and visual feature pipelines. • Self-governing Delivery: Own the end-to-end implementation of defined tasks, from data ingestion to production deployment, with moderate autonomy. • System Optimization: Proactively identify and fix bottlenecks in team systems to improve quality, reliability, or engineering velocity. • Collaborative Quality: Participate actively in design and code reviews, providing constructive feedback and ensuring high technical standards within your scope. • Data-Driven Execution: Set up and monitor online experiments (A/B tests and bandit rollouts) to measure the impact of presentation features on user interaction.