Scale AI
Machine Learning Research Scientist / Engineer, Reasoning
Scale AI, New York, New York, us, 10261
Overview
Machine Learning Research Scientist / Engineer, Reasoning at Scale AI. This role operates at the forefront of AI research and real-world implementation, with a strong focus on reasoning within large language models (LLMs). The ideal candidate will study the data types critical for advancing LLM-based agents, including browser and software engineering (SWE) agents. You will play a key role in shaping Scale’s data strategy by identifying the most effective data sources and methodologies for improving LLM reasoning. Success requires a deep understanding of LLMs, planning algorithms, and novel approaches to agentic reasoning, as well as creativity in tackling data generation, model interaction, and evaluation. You will contribute to impactful research on language model reasoning, collaborate with external researchers, and work closely with engineering teams to bring state-of-the-art advancements into scalable, real-world solutions. Responsibilities
Study data types critical for advancing LLM‑based agents (including browser and SWE agents). Identify data sources and methodologies for improving LLM reasoning. Collaborate with external researchers and engineering teams to translate research into scalable, real‑world solutions. Contribute to research on language model reasoning and novel approaches to data generation, model interaction, and evaluation. Qualifications
Practical experience working with LLMs, with proficiency in frameworks like PyTorch, JAX, or TensorFlow; ability to rapidly interpret research literature and turn ideas into working prototypes. A track record of published research in top ML and NLP venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, CoLLM, etc.). At least three years of experience solving complex ML challenges, in research or product development, particularly related to LLM capabilities and reasoning. Strong written and verbal communication skills and ability to work across teams. Nice to have
Hands-on experience fine-tuning open-source LLMs or leading bespoke LLM fine-tuning projects using PyTorch/JAX. Experience building applications and evaluations related to LLM-based agents (tool-use, text-to-SQL, browser agents, coding agents, GUI agents). Experience with agent frameworks such as OpenHands, Swarm, LangGraph, or similar. Familiarity with advanced agentic reasoning techniques (e.g., STaR and PLANSEARCH). Proficiency in cloud-based ML development (AWS or GCP). Compensation and Benefits
Compensation packages include base salary, equity, and benefits. The base salary range for this full-time role in San Francisco, New York, and Seattle is $220,000–$325,000 USD. Employees are granted equity-based compensation, subject to Board approval. Benefits include comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO; additional benefits such as a commuter stipend may apply. The recruiter can share the specific salary range for your location and equity eligibility during hiring. We do not conduct LeetCode-style problem-solving assessments. Our research interviews assess prototype and debugging ability, depth of research understanding, and alignment with Scale’s culture. EEO statement and accommodations: Scale is an inclusive, equal opportunity workplace. We provide reasonable accommodations for applicants with disabilities. If you need assistance, please contact accommodations@scale.com. We comply with applicable pay transparency provisions and data privacy policies. Notes
Please reference the job posting subtitle for the location. This description reflects current job requirements and is subject to change.
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Machine Learning Research Scientist / Engineer, Reasoning at Scale AI. This role operates at the forefront of AI research and real-world implementation, with a strong focus on reasoning within large language models (LLMs). The ideal candidate will study the data types critical for advancing LLM-based agents, including browser and software engineering (SWE) agents. You will play a key role in shaping Scale’s data strategy by identifying the most effective data sources and methodologies for improving LLM reasoning. Success requires a deep understanding of LLMs, planning algorithms, and novel approaches to agentic reasoning, as well as creativity in tackling data generation, model interaction, and evaluation. You will contribute to impactful research on language model reasoning, collaborate with external researchers, and work closely with engineering teams to bring state-of-the-art advancements into scalable, real-world solutions. Responsibilities
Study data types critical for advancing LLM‑based agents (including browser and SWE agents). Identify data sources and methodologies for improving LLM reasoning. Collaborate with external researchers and engineering teams to translate research into scalable, real‑world solutions. Contribute to research on language model reasoning and novel approaches to data generation, model interaction, and evaluation. Qualifications
Practical experience working with LLMs, with proficiency in frameworks like PyTorch, JAX, or TensorFlow; ability to rapidly interpret research literature and turn ideas into working prototypes. A track record of published research in top ML and NLP venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, CoLLM, etc.). At least three years of experience solving complex ML challenges, in research or product development, particularly related to LLM capabilities and reasoning. Strong written and verbal communication skills and ability to work across teams. Nice to have
Hands-on experience fine-tuning open-source LLMs or leading bespoke LLM fine-tuning projects using PyTorch/JAX. Experience building applications and evaluations related to LLM-based agents (tool-use, text-to-SQL, browser agents, coding agents, GUI agents). Experience with agent frameworks such as OpenHands, Swarm, LangGraph, or similar. Familiarity with advanced agentic reasoning techniques (e.g., STaR and PLANSEARCH). Proficiency in cloud-based ML development (AWS or GCP). Compensation and Benefits
Compensation packages include base salary, equity, and benefits. The base salary range for this full-time role in San Francisco, New York, and Seattle is $220,000–$325,000 USD. Employees are granted equity-based compensation, subject to Board approval. Benefits include comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO; additional benefits such as a commuter stipend may apply. The recruiter can share the specific salary range for your location and equity eligibility during hiring. We do not conduct LeetCode-style problem-solving assessments. Our research interviews assess prototype and debugging ability, depth of research understanding, and alignment with Scale’s culture. EEO statement and accommodations: Scale is an inclusive, equal opportunity workplace. We provide reasonable accommodations for applicants with disabilities. If you need assistance, please contact accommodations@scale.com. We comply with applicable pay transparency provisions and data privacy policies. Notes
Please reference the job posting subtitle for the location. This description reflects current job requirements and is subject to change.
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