Company
Software (AI)
StaffAppliedScientist
Neural analysis suggests this role is
optimal for Lead candidates.
“Staff Applied Scientist. Skills: generative AI, agentic AI, deep learning, reinforcement learning, graph neural networks, Python, PyTorch, TensorFlow, JAX. developing and deploying generative AI, agentic AI, and deep learning-based solutions. build scalable solutions that solve meaningful real-world challenges”
What You'll Achieve.
shipping AI-powered products; build scalable solutions that solve meaningful real-world challenges; product impact; reliable production systems
Industry & Context.
Problem Solving; ambiguous problem framing
What They're Looking For.
Must Have
Ph. D. in Computer Science, Artificial Intelligence, or a related field, 5-7 years with Applied research in AI, Successfully shipping AI-powered products
Nice to Have
Equivalent industry experience, Large language models (LLMs), agentic systems, reinforcement learning, and/or graph neural networks, Hands-on experience designing and deploying agentic AI systems, including tool usage, function calling, planning, multi-agent orchestration, retrieval-augmented generation, and evaluating agent behavior in production environments, Pre-training and/or fine-tuning foundational large language models, Deep understanding of transformer architectures, deep learning, and generative AI, with experience in natural language processing (NLP) for generative and agentic AI applications, Experience building and deploying scalable AI/ML solutions in real-world applications, including those at national or global scales, Demonstrated ability to drive projects forward independently with minimal guidance, Ownership of ambiguous problem framing through research, prototyping, productionization, and post-launch iteration, Familiarity with supervised fine-tuning, RLHF/DPO, LoRA/PEFT, distillation, and large-scale distributed training, AWS certifications related to generative AI, Published research findings at top-tier conferences and journals
What You'll Do.
developing and deploying generative AI
and deep learning-based solutions
build scalable solutions that solve meaningful real-world challenges
and hands-on product impact
Ownership of ambiguous problem framing through research
and post-launch iteration
How You'll Work.
Team & Collaboration
partner with a diverse team of scientists, engineers, and product leaders; thrive in communicative, collaborative, cross-functional environments
Communication Scope
communicative
Process & Methodology
drive projects forward independently with minimal guidance
Full Job Description
Location: Seattle, WA (On-site downtown) - Hybrid Type: Full-Time Industry: Software (AI) Specialty: Identity verification, fraud prevention, background screening, and risk intelligence. OVERVIEW Our publicly traded client develops proprietary technology and analytics to deliver identity intelligence, powering critical solutions that help organizations operate confidently. Their solutions enable real-time identification and location of people, businesses, assets, and their relationships for risk mitigation, due diligence, fraud prevention, regulatory compliance, and customer acquisition. These solutions support frictionless commerce, enhance safety, reduce fraud, and lower related societal costs. KEY RESPONSIBILITIES Our client is looking for a collaborative and forward-thinking Staff Applied Scientist to join their Seattle-based AI team. In this role, your work will focus on developing and deploying generative AI, agentic AI, and deep learning-based solutions. You’ll partner with a diverse team of scientists, engineers, and product leaders to build scalable solutions that solve meaningful real-world challenges. This is an opportunity for someone who enjoys balancing research, experimentation, and hands-on product impact in a fast-moving environment where curiosity, creativity, and continuous learning are valued. QUALIFICATIONS Education Ph.D. in Computer Science, Artificial Intelligence, or a related field with a focus on: generative AI, agentic AI, deep learning, reinforcement learning, and/or graph neural networks. -or- Equivalent industry experience Experience: - 5-7 years with Applied research in AI. - Large language models (LLMs), agentic systems, reinforcement learning, and/or graph neural networks. - Successfully shipping AI-powered products (not just publishing or prototyping). Strengths: - Hands-on experience designing and deploying agentic AI systems, including tool usage, function calling, planning, multi-agent orchestration, retrieval-augmented generati
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