Role Description Want to own the data infrastructure behind some of the most naturalistic voice models in production? You'll be joining a well-funded speech AI startup — just closed their Series A — with strong enterprise traction and revenue that more than doubled last quarter. They're building ultra-realistic voice technology that handles natural laughter, breathing, seamless language switching, and accurate pronunciation across languages and accents. Their models are powering hundreds of millions of conversations monthly. Before training a single model, they built their own corpus — full-duplex, studio-quality conversational speech annotated by PhD linguists. As their MLE, you'll own the pipelines that turn that raw material into clean, training-ready data. - Own end-to-end data pipelines from raw audio ingestion through to versioned, training-ready datasets - Build quality systems that catch annotation errors and alignment issues before they reach a training run - Maintain the training infrastructure that keeps GPUs fed — dataloaders, streaming datasets, multi-modal batching - Build and iterate on tooling across speech representations including neural codecs, semantic tokens and mel features - Handle full- and half-duplex pipeline work including two-channel alignment and overlap handling Qualifications - Strong engineering fundamentals with experience building ML data pipelines at scale - Hands-on experience with speech or audio data - Solid understanding of speech representations and the tradeoffs between them - Experience with multi-channel audio data including diarisation and alignment Requirements - Experience with multilingual data pipelines (Nice to have) - Large-scale training infrastructure experience — FSDP, DeepSpeed, Ray (Nice to have) - Annotation tooling and human-in-the-loop systems (Nice to have) Benefits - Remote-friendly - Competitive base plus stock

Role Description We’re looking for a Senior Machine Learning Engineer who can bring their depth and breadth of experience in applied data science and optimization at industry scale to help guide our Index-wide technology strategy for machine learning and optimization, and to drive pragmatic execution and iterative improvement of the same. - Design and implement enterprise-scale MLOps systems and platforms (including data ingestion, feature pipelines, model training, validation, deployment and monitoring), setting standards for high-performance ML products. - Productionalize and support scalable and efficient Machine Learning models and solutions. - Define and enforce standards for model lifecycle management, including versioning, monitoring, alerting, traceability and highly available low-latency inference systems. - Refine and contribute to advanced data management strategies, optimizing for performance given extensive data loads. - Mentor teams and provide guidance for machine learning engineering deployment practices, promoting a culture of excellence. - Navigate complex problem-solving situations, contributing to decisions that affect the broader business's strategic direction. - Utilize CI/CD best practices to ensure the enterprise keeps with innovative practices and remains efficient, through engagement with ML and MLOps evolution outside of the organization. - Enable safe deployment strategies like shadow, canaries & gradual rollout. Qualifications - Advanced degree in Computer Science, Engineering, or a related field, or equivalent experience. - Expertise in high-performance backend technologies, preferably including Golang. - Extensive experience with cloud and/or on-premises distributed systems, data-intensive applications, and large-scale backend system design. - Deep knowledge of machine learning operationalization, including current trends, tools, and frameworks. - Proven problem-solving prowess, capable of pioneering novel solutions to sophisticated technical challenges. - Experienced in software development, deployment, and continuous improvement of complex CI/CD pipelines. - A love of technology, or thirst for knowledge, or curiosity about the world, or a desire to solve the hardest problems. Benefits - Comprehensive health, dental, and vision plans for you and your dependents. - Paid time off, health days, and personal obligation days plus flexible work schedules. - Competitive retirement matching plans. - Equity packages. - Generous parental leave available to birthing, non-birthing, and adoptive parents. - Annual well-being allowance plus fitness discounts and group wellness activities. - Commuter benefits and discounts, where available. - Employee assistance program. - Mental health first aid program that provides an in-the-moment point of contact and reassurance. - One day of volunteer time off per year and a donation-matching program. - Bi-weekly town halls and regular community-led team events. - Multiple resources and programming to support continuous learning. - A workplace that supports a diverse, equitable, and inclusive environment.

Role Description As a Senior Machine Learning Engineer in Computer Vision, you will design and deliver advanced vision systems that power mission-critical applications for global and Fortune 500 companies. You’ll work across deep learning, large-scale data pipelines, and high-performance infrastructure, owning models end-to-end from experimentation to production deployment. This role is designed for engineers who think systems-level, understand the real-world constraints of ML at scale, and can turn ambiguous visual problems into high-impact, production-ready solutions. You’ll shape architectures, guide model strategy, and bring modern vision capabilities into enterprise environments where reliability, speed, and accuracy matter. Functional Responsibilities: - Develop and fine-tune models for tasks like image classification, object detection, segmentation, and generative modeling using TensorFlow, PyTorch, or Keras. - Implement techniques such as resizing, normalization, data augmentation, and feature extraction to improve model performance. - Optimize and deploy computer vision models on cloud platforms (AWS, GCP, Azure), edge devices, and specialized hardware (GPUs, TPUs). - Use CI/CD, model versioning, and monitoring tools to ensure reliable and scalable deployment of vision models. - Improve model speed and performance using quantization, pruning, and hardware acceleration techniques. Qualifications - +5 years of hands-on experience developing and deploying machine learning models in production environments. - Proven experience writing production-level code, with strong proficiency in Python. - Strong Python programming skills with proficiency in deep learning frameworks (TensorFlow, PyTorch, or Keras). - Expertise in designing, training, and fine-tuning models for: - Image classification (ResNet, EfficientNet) - Object detection (Faster R-CNN, YOLO, SSD) - Image segmentation (U-Net, Mask R-CNN) - Strong understanding of image preprocessing techniques (resizing, normalization, data augmentation). - Experience with computer vision libraries such as OpenCV and torchvision. - Experience with transfer learning and adapting pre-trained models. - Ability to deploy models on cloud platforms (AWS, GCP, Azure) and specialized hardware (GPUs, TPUs). - Familiarity with MLOps tools for automating ML pipelines. Benefits - Ownership through equity participation. - Annual company retreat. - Education bonus for continuous learning. - Company-wide winter break. - Paid time off. - Optional in-person events and meetups. - Tailored career roadmaps. - High-performance culture.

• Drive critical initiatives forward rapidly as part of an agile, embedded expert team • Deploy Principal-Vetted talent directly into partner technical teams • Participate in high-level engineering efforts to simplify cloud growth