Senior Product Engineer, Growth & Lifecycle Infrastructure - Music & Audio
Lead efforts to drive the design and development of customer-facing multi-modal machine learning inference systems. Work with the Platform and Inference teams on building inference systems for the next generation of models, focusing on optimization, model tuning, and deployment. Partner with leading cloud providers to deliver hosted Stability AI inference solutions. Serve as a strategic thought partner for leaders across the organization on driving business impact through machine learning. Contribute to bringing new Stability models and pipelines into existence. Prototype and productionize inference platform improvements and new features.
Senior Deep Learning Engineer (음성 합성 개발)
Research and develop latest TTS models based on LLM and Flow Matching; develop and advance emotion controllable TTS models; build and improve quality of speech synthesis data using latest generative models; develop and apply multilingual and multi-speaker TTS models to services; optimize TTS models for server and on-device environments; develop real-time (streaming) speech synthesis systems and optimize latency; improve inference and training pipelines to enhance speech generation quality.
Head of ML
Define and drive a coherent vision for leveraging data to build automation products in surveying and design, translate this vision into a technical roadmap and execute it to advance product capabilities, build and grow the machine learning team including hiring and structuring as the organization scales, mentor ML engineers and researchers by providing technical direction and career growth guidance, stay hands-on by reviewing designs, code, and architecture to maintain credibility and connection with the team, and partner with product and engineering leadership to align research investments with product strategy and customer needs.
ML Engineer
Design, train, and evaluate computer vision and 3D ML models for extracting CAD-grade geometry and features from dense LiDAR and imagery. Drive ML research that translates directly into product capabilities by prototyping new approaches, running experiments, and identifying what’s shippable. Own models through the full product lifecycle including problem framing, data strategy, training, evaluation, and final integration into cloud-based CAD software. Develop evaluation methodology and metrics that reflect real surveying and engineering accuracy requirements. Collaborate with ML infrastructure engineers to scale training and inference of models and with product teams to align model behavior with user needs.
Staff Machine Learning Engineer
Own end-to-end ML system execution including data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using methods such as LoRA, QLoRA, SFT, DPO, and distillation. Architect and operate scalable inference systems managing latency, cost, and reliability. Design and maintain data systems for high-quality synthetic and real-world training data. Implement evaluation pipelines covering performance, robustness, safety, and bias in partnership with research leadership. Own production deployment including GPU optimization, memory efficiency, latency reduction, and scaling policies. Collaborate closely with application engineering to integrate ML systems into backend, mobile, and desktop products. Make pragmatic trade-offs, ship improvements quickly, and learn from real usage. Work under real production constraints including latency, cost, reliability, and safety. Detect, debug, and resolve production issues quickly to minimize user impact. Support and align team members to deliver high-impact ML work with minimal friction. Ensure iterations on models and systems are measurable, safe, and improve user experience over time.
Technical Lead, Machine Learning
Own end-to-end ML system execution including data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation. Architect and operate scalable inference systems balancing latency, cost, and reliability. Design and maintain data systems for high-quality synthetic and real-world training data. Implement evaluation pipelines covering performance, robustness, safety, and bias in partnership with research leadership. Own production deployment including GPU optimization, memory efficiency, latency reduction, and scaling policies. Collaborate closely with application engineering to integrate ML systems into backend, mobile, and desktop products. Make pragmatic trade-offs and ship improvements quickly while learning from real usage. Work within real production constraints such as latency, cost, reliability, and safety.
Senior Machine Learning Engineer
As a Senior Member of Technical Staff, Machine Learning, you are responsible for building core ML systems that power a proactive, long-horizon AI product and owning work end-to-end including data preparation, training, evaluation, inference, and iteration. You turn research ideas into working systems that run reliably in production, debug model failures and system issues using real production signals, iterate quickly by shipping, measuring outcomes, refining, and repeating. You collaborate closely with research, product, and engineering teams to deliver real user impact, mentor and review work from other ML engineers through example and technical judgment, and work under real production constraints like latency, cost, reliability, and safety.
Member of Technical Staff, Machine Learning
As a Member of Technical Staff, Machine Learning, the responsibilities include building and improving ML components across data, training, evaluation, and inference; fine-tuning and adapting models as part of larger production systems; implementing evaluation and testing to understand model behavior; helping build and maintain data pipelines for real-world and synthetic data; debugging model issues, performance problems, and production incidents; shipping improvements iteratively and learning from real user feedback; working closely with senior ML engineers and product teams; and working under real production constraints such as latency, cost, reliability, and safety.
Staff ML Systems Engineer, Distributed Systems
Design and build scalable distributed machine learning pipelines across data processing, model training, evaluation, and post-processing workflows. Architect distributed execution systems, including parallelization strategies, workload scheduling, resource allocation, and fault tolerance mechanisms. Develop reusable abstractions, frameworks, and libraries that simplify distributed pipeline development. Optimize performance across distributed CPU and GPU environments, improving throughput, utilization, and reliability. Design systems that effectively manage data partitioning, memory utilization, serialization overhead, and compute efficiency. Partner closely with ML engineers, data engineers, and infrastructure teams to productionize research workflows and enable large-scale model development. Establish best practices and engineering standards for distributed machine learning infrastructure. Evaluate and guide decisions around distributed computing frameworks, infrastructure technologies, and system design trade-offs. Improve observability, debugging, monitoring, and operational tooling for distributed systems at scale.
Field Engineering Intern - Summer 2026
The Field Engineering Intern will learn directly from ML engineers transitioning to customer-facing field engineering, gaining firsthand exposure to how deep ML expertise translates into real-world customer impact. They will work on real customer workloads running on advanced GPU infrastructure, supporting customer onboarding, optimization engagements, and production deployments across demanding ML use cases. They will review prior optimization work, evaluate strategies against current best practices, and recommend improvements. The intern will develop a structured optimization playbook and case studies capturing the team's methodology and quantifying the value of field engineering work in a repeatable, scalable format. Finally, they will present their work to company leadership at the close of the engagement.
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