Develop and deploy advanced models for parsing and interpreting unstructured data, experiment with techniques to improve language model accuracy, build data pipelines, assess model performance, and integrate models into the product.
Compensation
Base salary: $200,000 to $325,000 annually. Visa sponsorship is available.
Responsibilities
Develop and deploy advanced models for parsing and interpreting unstructured data.
Experiment with techniques to improve language model accuracy and build tools to evaluate their results.
Build data pipelines, assess model performance, and integrate models into the product.
Collaborate closely with company leadership and customers to shape product direction and engineering strategy.
Leading computer vision and video manipulation work using LLMs, VLMs, and traditional techniques to improve creator content and viewer engagement.
Compensation
183,800.00 - 248,700.00 USD annually
Responsibilities
Design and deploy computer vision and video manipulation models using LLMs, VLMs, and traditional techniques, taking solutions from research prototype to production.
Partner with senior Product and Twitch leadership to translate customer needs into well-defined scientific problems and deliver end-to-end solutions.
Evaluate and integrate generative AI capabilities across Amazon Bedrock and third-party platforms, selecting the approach that maximizes product quality.
Drive the team's scientific agenda by proposing new initiatives, authoring technical documents, and contributing to external publications.
Mentor fellow scientists and engineers through code reviews, design guidance, and technical assessments that raise the overall quality bar.
Design, build, and own scalable data pipelines that automate data discovery, selection, and curation to meet DNN training data needs.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 140,000 USD – 224,250 USD for Level 3; and 168,000 USD – 270,250 USD for Level 4.
Responsibilities
Design, build, and own scalable data pipelines that automate data discovery, selection, and curation to meet DNN training data needs.
Develop agentic workflows that proactively identify model data needs, plan and implement mining tasks using data tools and model inference, and refine mining strategies through human review and downstream model feedback.
Deploy and operate these pipelines and workflows as reliable production systems, with workflow orchestration, monitoring, and failure recovery.
Partner with MLEs and data analysts to turn recurring data needs and model failure cases into reusable mining capabilities, define acceptance criteria, and improve scenario coverage through feedback.
Requirements
BS or MS in Computer Science, Mathematics or Electrical Engineering, or a related quantitative field, or equivalent experience
Lead the development of next-generation Agentic Ambient Intelligence systems at the intersection of Autonomous AI Agents, Smart Home Automation and Local-First Infrastructure.
Compensation
Competitive Tax-Free Salary: A market-leading monthly package in AED, commensurate with experience.
Responsibilities
Design and implement autonomous agentic workflows using frameworks such as OpenClaw and Hermes Agentic OS
Develop multi-agent systems capable of self-improvement, tool use and complex reasoning
Integrate Vision frameworks and Voice AI (ElevenLabs) to create immersive, multimodal interaction layers
Leverage cloud and local AI tooling (Cursor, Claude, Ollama, LMStudio, OpenCode) to accelerate development and deployment
Build deep integrations between AI agents and Home Assistant (or equivalent frameworks) to enable physical-world actions
Design, build, and own scalable data pipelines that automate data discovery, selection, and curation to meet DNN training data needs.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:
140,000 USD – 224,250 USD for Level 3; and 168,000 USD – 270,250 USD for Level 4.
Responsibilities
Design, build, and own scalable data pipelines that automate data discovery, selection, and curation to meet DNN training data needs.
Develop agentic workflows that proactively identify model data needs, plan and implement mining tasks using data tools and model inference, and refine mining strategies through human review and downstream model feedback.
Deploy and operate these pipelines and workflows as reliable production systems, with workflow orchestration, monitoring, and failure recovery.
Partner with MLEs and data analysts to turn recurring data needs and model failure cases into reusable mining capabilities, define acceptance criteria, and improve scenario coverage through feedback.
Requirements
BS or MS in Computer Science, Mathematics or Electrical Engineering, or a related quantitative field, or equivalent experience
Architect, build, and scale a global data ecosystem for Physical AI and advanced data technologies.
Responsibilities
Develop and productionize AI-powered auto-labeling workflows using computer vision models, vision-language models, and AI agents, integrating human review where needed.
Design and implement MLOps capabilities for reproducible training and inference, model and dataset versioning, evaluation, deployment, and lifecycle management.
Build observability and data quality frameworks covering pipeline health, job execution, labeling quality, data lineage, and GT release readiness.
Optimize distributed processing and GPU inference workloads to improve throughput, resource utilization, cost efficiency, and release turnaround time.
Drive technical decisions and cross-functional execution with perception, autolabeling modeling, data operations, and partner-facing teams to translate requirements into production solutions.
Architect, build, and scale a global data ecosystem for AI-based ground truth generation for autonomous driving and robotics.
Responsibilities
Develop and productionize AI-powered auto-labeling workflows using computer vision models, vision-language models, and AI agents, integrating human review where needed.
Design and implement MLOps capabilities for reproducible training and inference, model and dataset versioning, evaluation, deployment, and lifecycle management.
Build observability and data quality frameworks covering pipeline health, job execution, labeling quality, data lineage, and GT release readiness.
Optimize distributed processing and GPU inference workloads to improve throughput, resource utilization, cost efficiency, and release turnaround time.
Drive technical decisions and cross-functional execution with perception, autolabeling modeling, data operations, and partner-facing teams to translate requirements into production solutions.
Requirements
BS, MS, or PhD in Computer Science, Software Engineering, Data Engineering, or a related field, or equivalent practical experience.
Software Development Engineer II, Robotics NavigationNewAmazon · N.Reading, US