Applied ML Engineer Jobs

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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.

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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.

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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.

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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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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.

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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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Follow the latest research related to Large Language Models (LLMs) and data quality, being familiar with relevant open-source datasets and models. Design and implement complex pipelines to generate large amounts of diverse data while optimizing available resources. Collaborate closely with teams such as Pretraining, Posttraining, Evals, and Product to ensure short feedback loops on the quality of models delivered. Suggest, conduct, and analyze data ablations or training experiments to improve the quality of generated datasets using quantitative insights.

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Design and develop novel algorithms and machine learning models for 2D/3D machine perception and mapping in real-world environments. Contribute to large-scale, automated mapping pipelines. Serve as a technical leader on the team by maintaining coding and machine learning development best practices and making architectural decisions. Help set the vision for the team and build out technical roadmaps. Coordinate cross-functional initiatives and collaborate with engineers from Mapping, Perception, Planner, Simulation, Data Science, and more. Drive the use of metrics and tools to guide development, validate algorithms, and measure progress.

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As an Applied Research Engineer at Labelbox, you will develop systems and methods to create, analyze, and leverage high-quality human-in-the-loop data for frontier AI model developers. This includes designing and implementing advanced systems that align human feedback into AI training processes such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO). You will work on techniques to measure and improve human data quality, develop AI-assisted tools to enhance the data labeling process, and investigate how different types of human feedback impact model performance and alignment. Your work will involve optimizing human feedback collection through novel algorithms, integrating breakthroughs into Labelbox's product suite to make human-AI alignment scalable, engaging with customers and the AI community to understand data needs and share best practices, publishing research, exploring new frontiers in human-AI collaboration, creating technical documentation, blog posts, and educational content, and driving industry innovation through these activities.

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Design, build, and maintain scalable machine learning systems including data ingestion, preprocessing, training, testing, and deployment. Develop and optimize end-to-end ML pipelines encompassing data collection, labeling, training, validation, and monitoring to ensure reliability and reproducibility. Implement robust MLOps practices such as model versioning, experiment tracking, CI/CD for machine learning, and continuous monitoring in production environments. Collaborate with product and engineering teams to integrate and deploy models into real-time products with a focus on efficiency and scalability. Ensure data quality, observability, and performance across all AI systems. Stay current with the latest AI infrastructure, tooling, and research to support ongoing innovation.