Senior Applied Scientist

Design and develop methods to adapt foundation models (e.g., language, diffusion, or multimodal models) for enterprise‐specific tasks such as document understanding, workflow automation, or content generation. Contribute to one or more aspects of the post-training stack, including: Reinforcement learning or fine-tuning methods Architectural or parameter‐efficient adaptation techniques Inference‐time steering or controllability approaches Tooling for evaluation, debugging, or model development Privacy- or security‐preserving training techniques (e.g., differential privacy) Harnesses Implement and evaluate adaptation approaches under real‐world enterprise deployment constraints such as latency, safety, privacy, policy compliance, and compute efficiency. Partner with research and engineering teams to translate product or customer requirements into scalable model adaptation solutions. Explore post‐training techniques that improve domain specialization, tool use, planning, or agentic behaviors in enterprise environments. Drive technical work from concept to prototype, delivering new methods, systems components, or empirical insights that advance enterprise model customization. Document approaches and share best practices to improve organizational capabilities in post‐training and secure deployment of foundation models. Support mentorship and onboarding of interns or early‐career team members as appropriate. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) These requirements include but are not limited to the following specialized security screenings: Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. 3+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers).

presenting at conferences or other events in the outside research/industry community as an invited speaker. 3+ years experience conducting research as part of a research program (in academic or industry settings). 1+ year(s)

developing and deploying live production systems, as part of a product team. 1+ year(s)

developing and deploying products or systems at multiple points in the product cycle from ideation to shipping.

contributing to research, open-source systems, or production deployments involving foundation model training or adaptation.

in one or more of the following areas: Transformer or multimodal model architectures.

Reinforcement learning or post-training methods.

Distributed or large‐scale ML training systems.

Privacy-preserving ML (e.g., differential privacy).

Evaluation or benchmarking of AI systems.

Tool use, planning, or agentic model behaviors.

Deployment of AI solutions in enterprise or customer environments.

publishing academic papers as a lead author or essential contributor, or contributing to technical work presented at leading conferences in relevant research domains. 2+ years of experience building scalable ML systems or pipelines for training, adapting, or deploying AI models. 2+ years of experience with Python and machine learning frameworks (e.g., PyTorch or equivalent). Reinforcement learning or supervised fine‐tuning for foundation models. Scalable training systems for RLHF/RLAIF or other post‐training pipelines. Transformer architecture design or efficient adaptation techniques (e.g., LoRA-style methods). Inference‐time steering, controllability, or alignment approaches. Debugging, evaluation, or development tooling for foundation models.