Senior Research Scientist

About Adaption Labs: Adaption Labs is a premier, hyper-growth artificial intelligence research laboratory and deep tech pioneer dedicated to building efficient intelligence that evolves and adapts alongside the world in real-time. Moving completely away from legacy, static neural networks that remain frozen post-training, Adaption Labs co-designs serving infrastructure, gradient-free exploration algorithms, and runtime user interfaces as a single connected system. Backed by elite global investors and operating with a high talent density, Adaption Labs builds flexible, personalized, and accessible machine learning architectures engineered to ensure technological innovation directly empowers users globally.

Position Overview

We are seeking a highly sophisticated, systems-minded Senior Research Scientist to join our core Modelling department in a full-time remote capacity. In this high-impact, frontier research seat, you will lead the formulation, training, and algorithmic optimization of next-generation machine learning models optimized for real-time alignment and continuous adaptation. Moving past abstract paper metrics, you will collaborate cross-functionally across software boundaries and hardware layers to deliver system-wide efficiency gains within large-scale computing environments. This role demands an elite mathematical thinker and programmer who enjoys creative, cross-stack problem solving and possesses deep expertise in training models that interact continuously with fluid real-world data streams.

Key Responsibilities

• Frontier Model Architecture Design: Design, train, and scale next-generation backend models specializing in real-time learning parameters, model efficiency, and gradient-free exploration loops.
• Cross-Stack System Optimization: Collaborate across algorithmic, software infrastructure, and hardware accelerator boundaries to unlock massive, platform-wide throughput and compute efficiency gains.
• Advanced Fine-Tuning & Alignment: Code and refine complex alignment strategies, implementing Reinforcement Learning from Human Feedback (RLHF), task-specific fine-tuning, and robust optimization techniques.
• Real-World Product Integration: Translate advanced theoretical concepts into functional, testable software blocks, deploying models into live production sandboxes to audit real-world user utility.
• Scalable Lab Experimentation: Orchestrate large-scale model training runs across distributed cloud or cluster environments, managing data weights, model partitioning, and convergence testing natively in Python.
• Algorithmic Standard Setting: Establish repeatable optimization methodologies, review neural network performance constraints, and contribute foundational breakthroughs to the broader machine learning engineering organization.

Required Skills & Qualifications

• A PhD or equivalent advanced research experience in a Computer Science or closely related quantitative field.
• 4-5+ years of verified commercial or post-doctoral history running deep learning model optimization, site reliability engineering, or artificial intelligence systems design within an industry laboratory operating computing infrastructures at scale.
• Deep, authoritative functional expertise in at least one specialized core domain: model runtime efficiency, real-time alignment topologies, or algorithmic stack optimization.
• Master-tier programming literacy utilizing Python paired with deep learning frameworks such as PyTorch, JAX, or TensorFlow.
• Demonstrated record of contributing to the global research community with peer-reviewed publications accepted at elite conferences, including NeurIPS, ICML, ICLR, ACL, or EMNLP.
• Location Context: Full-time remote parameters open to qualified research engineers base permanently across the United States, Canada, Europe, or via Global Remote tracks.

Preferred Strategic Indicators (Nice to Have)

• Prior experience designing adaptive interfaces, live streaming data ingestion frameworks, or dynamic multi-agent system configurations.
• Familiarity with functional performance monitoring, distributed GPU memory management, or model quantization techniques (INT8/FP4 compilation).
• An adaptable, systems-thinking mindset that thrives when navigating ambiguous, undefined technical problems.

What We Offer

• The exceptional professional canvas to directly direct, program, and invent the real-time continual learning systems defining the next era of machine intelligence.
• Highly competitive compensation metrics paired with comprehensive medical protections, equity options, and deep individual project autonomy.
• Profound work-from-home remote parameters providing a distributed global-first collective, flexible hours, and absolute geographic freedom.
• The Adaption Passport: Fuel your continuous learning path with a unique annual travel stipend designed to help you explore a brand-new country every year.
• A weekly home grocery or take-out lunch stipend, generous paid time-off parameters, and elite technical offsites inside creative workspaces.