Research Engineer II

Spectraforce Technologies, Inc.; Menlo Park (CA); Hybrid; USD 120,000 - 180,000; Part time

Job summary

Spectraforce Technologies, Inc. in Menlo Park, CA, is seeking a Research Engineer II for a 6-month hybrid contract (3 days onsite weekly) to advance fundamental AI research. The role emphasizes scalable ML systems, collaboration with researchers, and delivering reproducible results.

The ideal candidate has 5–7 years of experience, strong Python and PyTorch skills, and a PhD or research-focused master's is preferred or open if in progress.

Qualifications

• 2+ years hands-on experience in ML, recommendation systems, AI or pattern recognition
• Experience developing ML models at scale, including interacting with LLMs and post-training
• Programming in Python; hands-on PyTorch experience
• Bachelor's degree in CS/CE or related field; equivalent practical experience
• PhD or research-focused master's preferred; flexible if currently completing degree

Responsibilities

• Carry out cutting-edge research to advance ML systems
• Design methods, tools, and infrastructure for state-of-the-art large language models
• Collaborate with researchers and cross-functional partners, communicate progress and results
• Suggest, collect, and synthesize requirements for an effective feature roadmap
• Code deliverables with the engineering team and publish research results

Skills

Hands-on ML experience

Model development at scale

Python proficiency

Education

Bachelor's degree in CS/related field

PhD or research-focused master's preferred

Tools

PyTorch

Job description

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).