Member of Technical Staff - Post-Training

B Capital; San Francisco (CA); On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Top-tier compensation

Comprehensive health insurance

Fully paid parental leave

Paid time off

Daily meals provided

Job summary

B Capital is looking for a talented engineer to join their team in San Francisco, focusing on building systems that transform powerful pre-trained models into efficient agents. The role involves collaborating across teams, driving innovative research, and significantly contributing to ML capabilities.

We offer top-tier compensation and robust health benefits, fully paid parental leave, and opportunities for connection with teammates. This is a unique chance to make a substantial impact in a dynamic startup environment.

Qualifications

• Deep understanding of machine learning fundamentals.
• Experience with large-scale LLM training.
• Strong engineering skills, especially with complex ML codebases.

Responsibilities

• Build systems to transform pre-trained models into aligned agents.
• Drive research and engineering initiatives for large-scale optimization.
• Collaborate across teams for model capability improvements.

Skills

Machine learning fundamentals

Large-scale LLM training

Strong engineering skills

Data modeling techniques

Communication capabilities

Job description

Location

SF, NYC, London

Employment Type

Full time

Location Type

On-site

Department

Engineering

Our Mission

Reflection’s mission is to build open superintelligence and make it accessible to all.

We’re developing open weight models for individuals, agents, enterprises, and even nation states. Our team of AI researchers and company builders come from DeepMind, OpenAI, Google Brain, Meta, Character.AI, Anthropic and beyond.

About the Role

• Build systems that transform powerful pre-trained models into aligned and general agents.

• Drive research and engineering initiatives that push the frontier of post-training, from data curation to large-scale optimization.

• Develop data generation pipelines, reward models, reinforcement learning algorithms, and inference-time scaling techniques.

• Collaborate across pre-training and post-training teams to deliver step-function gains in model capability.

• Contribute to shaping our understanding of how large models learn to reason, follow instructions, and improve through reinforcement learning.

About You

• Deep understanding of machine learning fundamentals and practical experience with large-scale LLM training.

• Strong engineering skills, comfortable diving into complex ML codebases and distributed systems.

• Experience improving model behavior through data, reward modeling, or RL techniques.

• Evidence of owning ambitious research or engineering agendas that led to measurable model improvements.

• Thrive in a fast-paced, high-agency startup environment; bias toward action and clarity of execution.

• Able to work fluidly across research and infra boundaries

• Strong communication capabilities and comfort working collaboratively

• Passionate about advancing the frontier of intelligence.

What We Offer

We believe that to build superintelligence that is truly open, you need to start at the foundation. Joining Reflection means building from the ground up as part of a small talent-dense team. You will help define our future as a company, and help define the frontier of open foundational models.

We want you to do the most impactful work of your career with the confidence that you and the people you care about most are supported.

• Top-tier compensation: Salary and equity structured to recognize and retain the best talent globally.

• Health & wellness: Comprehensive medical, dental, vision, life, and disability insurance.

• Life & family: Fully paid parental leave for all new parents, including adoptive and surrogate journeys. Financial support for family planning.

• Benefits & balance: paid time off when you need it, relocation support, and more perks that optimize your time.

• Opportunities to connect with teammates: lunch and dinner are provided daily. We have regular off-sites and team celebrations.