ML/AI Engineer - Vehicle Intelligence
Develop AI-powered vehicle intelligence features that understand user intent, trip goals, vehicle state, and system constraints. Apply reinforcement learning, planning, optimization, and data-driven modeling to improve vehicle-level decisions across energy, comfort, charging, routing, and proactive vehicle preparation. Build models using vehicle telemetry, navigation data, user behavior, weather, traffic, cabin conditions, charging patterns, and fleet data. Create personalization models that learn user routines, comfort preferences, driving patterns, charging habits, and trip priorities while preserving privacy and user control. Use simulation, digital twins, and scenario-based testing to train, evaluate, and validate AI behavior before production deployment. Collaborate with autonomous driving and VLA teams to define interfaces for sharing user intent, route objectives, vehicle constraints, energy targets, comfort preferences, and system-level recommendations. Integrate ML models into production vehicle and cloud platforms, considering latency, compute efficiency, reliability, safety, explainability, and over-the-air update readiness. Work cross-functionally with Product, UX, Systems Engineering and Controls.
Member of Technical Staff (Machine Learning Engineer)
Translate cutting-edge research into production-ready machine learning systems. Design, build, and deploy end-to-end ML models and pipelines. Develop and optimize models for image and video processing. Own the full ML lifecycle including experimentation, training/fine-tuning, evaluation, and deployment. Rapidly prototype using open-source models and adapt them for product needs. Conduct experiments, analyze results, and iterate to improve performance. Collaborate with researchers and cross-functional teams (product, engineering, design) to deliver ML solutions at scale. Participate with advancements in machine learning and apply them to continuously improve products.
Technical Lead Manager - Training Runtime, Data(set) Movement
The Technical Lead Manager will own datasets throughout the training infrastructure and set the direction for how training jobs read data, including APIs, storage contracts, versioning model, benchmarks, debugging tools, and reliability guarantees to make data access consistent across current and future training frameworks. Responsibilities include designing and building a unified dataset read platform for multiple training frameworks; defining dataset APIs, storage-format expectations, registration/versioning, and migration paths to ensure reproducible and maintainable data access; building reliability into the read path such as stateful iteration, caching, fast restart, recovery, and clear operational contracts; developing terminal and web-based visualizers to inspect data late in the pipeline; writing and reviewing production code in core data loading, service, caching, and reliability paths; and partnering with teams working on training frameworks, reinforcement learning, multimodal models, storage, runtime, and cluster infrastructure. Over time, the role will expand to owning broader data movement systems including checkpoint loads/saves and snapshot transfers, working closely with technical leads and infrastructure teams.
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.
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