Research Engineer II

Job Title:

Research Engineer II

Duration: 06 months with potential to extend

Location:

Menlo Park, CA (Hybrid - 3 days onsite per week)

• 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.

• 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

• 5-7 years of experience

• 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

• 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

• 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.

• 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

• Direct experience in generative AI and LLM research
• PhD degree or research focused Master degree in ML areas

• 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…