Staff) - VC Backed Startups

SignalFire•San Francisco, CA

About The Position

SignalFire is building a Talent Network for Senior/Staff Applied AI Scientist & Researcher roles at VC-backed startups. This is an opportunity to get noticed by early-stage startups actively hiring in this field. SignalFire partners with over 200 innovative companies across various sectors, including AI, cybersecurity, healthtech, fintech, developer tools, and enterprise SaaS. They are seeking exceptional Senior and Staff Applied AI Scientists and Researchers who are passionate about developing advanced AI capabilities, solving complex technical problems, and translating research into product features. Joining this network provides visibility into exclusive, early-stage opportunities that may not be publicly advertised.

Requirements

5+ years of experience in machine learning, artificial intelligence, applied research, or a related technical field
Strong foundation in deep learning, statistics, optimization, and experimental design
Experience developing or adapting models for real-world product applications
Expertise in one or more areas such as natural language processing, generative AI, computer vision, multimodal learning, reinforcement learning, recommendation systems, or speech
Proficiency in Python and modern machine learning frameworks such as PyTorch, TensorFlow, or JAX
Experience with model training, fine-tuning, post-training, evaluation, or inference
Ability to design rigorous experiments and draw sound conclusions from incomplete or ambiguous results
Track record of translating research concepts into prototypes, production systems, or measurable product improvements
Ability to collaborate closely with research, engineering, product, and domain experts
Strong written and verbal communication skills, including the ability to explain complex technical concepts clearly
Staff-level candidates may be expected to define research direction, lead cross-functional initiatives, and influence broader AI strategy

Nice To Haves

Advanced degree in computer science, machine learning, statistics, mathematics, or a related field may be preferred, although equivalent applied experience may be considered

Responsibilities

Research, develop, and evaluate machine learning methods that improve product capabilities and customer outcomes
Design experiments to test new model architectures, training approaches, data strategies, and system designs
Adapt foundation models through fine-tuning, post-training, prompt optimization, retrieval, or other techniques
Develop evaluation frameworks and benchmarks for model quality, reliability, safety, and performance
Build prototypes and proofs of concept that demonstrate the potential of emerging AI techniques
Partner with AI/ML engineers and software engineers to translate successful experiments into production systems
Improve model accuracy, reasoning, latency, efficiency, robustness, and cost
Curate, generate, and evaluate datasets used for training, fine-tuning, and model assessment
Investigate model failures, edge cases, and unexpected behavior to identify opportunities for improvement
Stay current with relevant research and determine which advances can create practical product value
Communicate findings, tradeoffs, and technical recommendations to product, engineering, and executive stakeholders
Mentor other scientists and contribute to the company’s research culture, technical standards, and AI roadmap
Publish research, contribute to open-source projects, or represent the company within the broader technical community where appropriate