New

Research Engineer II

Spectraforce Technologies
United States, California, Menlo Park
Nov 03, 2026
Job Title: Research Engineer II Duration: 06 months with potential to extend Location: Menlo Park, CA (Hybrid - 3 days onsite per week) Summary Client is seeking Research Engineers to join Fundamental AI Research (FAIR). We are committed to advancing the field of artificial intelligence by making fundamental advances in technologies to help interact with and understand our world. We are seeking individuals passionate in solving systems challenges to sustainably accelerate our reach to human-level intelligence. Candidates will have an opportunity to make fundamental advances in systems and apply their ideas at an unprecedented scale. Top 3 non-negotiable skills Hands-on Machine Learning Experience: At least two years of experience in machine learning, recommendation systems, artificial intelligence, or pattern recognition. Developing Models at Scale: Experience developing models at scale, often demonstrated through internships at large tech companies or research lab experience. Python Proficiency: Strong programming skills in Python are a requirement Number of years' experience 5-7 years of experience Degrees or certifications required Minimum Requirement: A bachelor's degree in computer science or a relevant technical field is required. Preferred Qualifications: A PhD or a research-focused master's degree in a relevant area (such as AI or machine learning) is listed as a preferred qualification. Flexibility: The team is open to hiring candidates who are currently completing their PhDs, provided they can work the required hours Good to have skills 2+ years of hands-on experience in one or more of the following areas: machine learning, recommendation systems, pattern recognition, data mining or artificial intelligence Experience with developing machine learning models at scale, such as programmatically querying LLMs and LLM post-training. Programming experience in Python and hands-on experience with frameworks such as PyTorch. Experience writing software and executing complex experiments involving large AI models and datasets. Proven experience to translate insights into business recommendations Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience Responsibilities Carry out cutting-edge research to advance the science and technology of machine learning systems. Design methods, tools, and infrastructure to push forward the state of the art in large language models. Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results. Suggest, collect and synthesize requirements and create an effective feature roadmap. Code deliverables in tandem with the engineering team. Publish research results and contribute to research that impacts client product development. Minimum Qualifications 2+ years of hands-on experience in one or more of the following areas: machine learning, recommendation systems, pattern recognition, data mining or artificial intelligence Experience with developing machine learning models at scale, such as programmatically querying LLMs and LLM post-training. Programming experience in Python and hands-on experience with frameworks such as PyTorch. Experience writing software and executing complex experiments involving large AI models and datasets. Proven experience to translate insights into business recommendations Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience Preferred Qualifications Direct experience in generative AI and LLM research PhD degree or research focused Master degree in ML areas How will performance be measured Research Output & Contribution: The ability to contribute directly to the team's research problems, such as building reinforcement learning environments or generating synthetic data to "raise the bar" for models. Infrastructure Effectiveness: Success in building functional, robust sandboxes and pipelines. It is not enough to just "throw it together"; they must create infrastructure that works reliably, even when faced with complex AI agent traces. Scientific Rigor: The capacity to understand the why behind research results. Performance is measured by more than just getting an agent to climb a benchmark; it requires applying a scientific framework to understand why a specific approach works (or fails). Adaptability & Handling Ambiguity: The ability to remain effective even when priorities shift rapidly. The team frequently faces changes in direction-such as being pulled off research projects to build critical company models (e.g., the Glimmer model)-and requires engineers who are comfortable working in a high-churn environment. Essentially, because these contractors are "deeply embedded" rather than working on narrow, isolated tasks, they are measured by their ability to function as full members of the research team who can handle both the scientific and engineering demands of the projects. Key Projects/Day-to-Day Responsibilities: Research Focus: The team is dedicated to recursive self-improvement-working on systems where AI builds AI to accelerate development. Research Tasks: Engineers are responsible for staying current by reading academic papers, building reinforcement learning environments, generating synthetic data to "raise the bar" for models, and researching multi-agent systems where multiple AIs contribute to a project. Engineering Tasks: Infrastructure development is a core responsibility. This includes building robust sandboxes that allow agents to function effectively and safely. Operational Requirements: The role requires high comfort with ambiguity, as priorities can shift rapidly based on company needs (e.g., being pulled off projects to support other critical models). Team Integration: Unlike traditional contractor roles, these engineers are deeply embedded in the team, working directly on research projects alongside junior and senior staff Purpose/Size of this team & where does this position fit within the team FAIR Foundations is a horizontal (foundational) pillar within FAIR that helps achieve client's mission of building Advanced Machine Intelligence and using it to power client products for the benefit of people. The Data Research Team is based in client Superintelligence Labs (MSL) within the Fundamental AI Research Organization (FAIR).