This is a key field-based role responsible for engaging the patient community, conducting patient education programs, partnering with advocacy groups, and supporting infusion sites of care as the subject matter expert for REMS requirements as well as product and disease education.
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\nA successful candidate will be skilled in developing real-time object detection, sensor fusion, and state estimation algorithms using data from diverse mission sensors such as EO/IR cameras, radars, and IMUs. The role requires strong algorithmic thinking, deep familiarity with airborne sensing systems, and the ability to deliver performant software in simulation and real-world conditions.
\nShield AI is committed to developing cutting-edge autonomy for unmanned aircraft operating across all Department of Defense (DoD) domains, including air, sea, and land. Our Perception Engineers are instrumental in creating the situational awareness that underpins autonomy, ensuring our systems understand and respond to the operational environment with speed, precision, and resilience.
\nResponsibilities:
\nRequired Qualifications:
\nPreferred Qualifications:
\n$182,720 - $274,080 a year
\nXML job scraping automation by YubHub
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\nYou will have access to our massive GPU cluster for training and inference. Some core technologies we use include Python, torch, diffusers, and the fal Python SDK. You'll work alongside a team dedicated to quickly iterating on and deploying new AI breakthroughs. You have work published in ICCV, ICML, Neurips, CVPR.
\nWhat we offer at fal includes interesting and challenging work, competitive salary and equity, a lot of learning and growth opportunities, health, dental, and vision insurance (US), regular team events and offsites.
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\nYour work will focus on developing, fine-tuning, and operationalizing machine learning models to enhance user experiences. Sometimes your work will require entirely novel training or architecture developments. While other times it will require fine-tuning pre-existing models with novel datasets.
\nYou will have access to our massive GPU cluster for training and inference.
\nSome core technologies we use include Python, Torch, Diffusers, and the Fal Python SDK.
\nYou'll work alongside a team dedicated to quickly iterating on and deploying new AI breakthroughs.
\nOur compensation package includes a salary range of $170,000 - $250,000, plus equity, and a comprehensive benefits package.
\nWe offer interesting and challenging work, a lot of learning and growth opportunities, visa sponsorship, and relocation assistance to San Francisco.
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\nKey responsibilities include efficiently working a route to complete customer and compliance orders, observing all safety requirements and regulatory guidelines, actively capturing documentation in the Mobile Data Terminal, and recognizing abnormal operating conditions. The technician will also assist in connecting and disconnecting gas service, manually turning meters on and off, and conducting investigations of high bill complaints.
\nThe ideal candidate will possess a high school diploma or GED, mechanical aptitude, and basic knowledge and use of hand tools and equipment. Prior experience with heavy equipment operations, technical school training, and welding experience are preferred.
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\nYou will work on integrating Hugging Face's open-source libraries like Transformers and Diffusers, with major cloud platforms or managed SaaS solutions. This role involves bridging and integrating models with different cloud providers, ensuring the models meet expected performance, designing and developing easy-to-use, secure, and robust developer experiences and APIs for our users, writing technical documentation, examples and notebooks to demonstrate new features, and sharing and advocating your work and the results with the community.
\nThe ideal candidate will have deep experience building with Hugging Face Technologies, including Transformers, Diffusers, Accelerate, PEFT, Datasets, expertise in Deep Learning Framework, preferably PyTorch, optionally XLA understanding, strong knowledge of cloud platforms like AWS and services like Amazon SageMaker, EC2, S3, CloudWatch and/or Azure and GCP equivalents, experience in building MLOps pipelines for containerizing models and solutions with Docker, familiarity with Typescript, Rust, and MongoDB, Kubernetes are helpful, ability to write clear documentation, examples and definition and work across the full product development lifecycle, and bonus experience with Svelte & TailwindCSS.
\nWe are actively working to build a culture that values diversity, equity, and inclusivity. We are intentionally building a workplace where people feel respected and supported—regardless of who you are or where you come from. We believe this is foundational to building a great company and community.
\nXML job scraping automation by YubHub
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\nWe are looking for a Cloud Machine Learning engineer responsible to help build machine learning solutions used by millions leveraging cloud technologies. You will work on integrating Hugging Face's open-source libraries like Transformers and Diffusers, with major cloud platforms or managed SaaS solutions.
\nResponsibilities:
\nAbout You\nYou'll enjoy working on this team if you have experience with and interest in deploying machine learning systems to production and build great developer experiences. The ideal candidate will have skills including:
\nXML job scraping automation by YubHub
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