Member of Technical Staff - Research Software Engineer

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

Benefits offered by this job

Comprehensive medical, dental, and vision insurance

Fully paid parental leave

Daily lunches and dinners provided

Relocation support and paid time off

Job summary

B Capital in San Francisco is looking for an engineering professional to architect and optimize core training infrastructure for their AI models. You will work on distributed systems and large-scale data pipelines, focusing on performance and numerical stability. Successful candidates will have strong software engineering skills and experience in either distributed training or data infrastructure. The role offers top-tier compensation and comprehensive health and wellness benefits.

Qualifications

• Strong software engineering skills with knowledge of machine learning.
• Experience in distributed systems and performance optimization.
• Ability to implement research papers into scalable systems.

Responsibilities

• Designing and optimizing large-scale training loops and data pipelines.
• Implementing state-of-the-art techniques ensuring numerical stability.
• Building internal tooling for launching and monitoring experiments.

Skills

Distributed Training & Inference

Data Infrastructure

Tools

PyTorch

JAX

Kubernetes

Ray

Job description

Location

NYC; London; SF

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.

The Roles Mission

Bridge the gap between research and production by turning cutting-edge algorithms into scalable training systems. You will design and optimize the core infrastructure behind frontier AI models — from reinforcement learning training loops and distributed GPU training to massive-scale data pipelines.

Our systems train models across thousands of GPUs and process petabyte-scale datasets. We care deeply about numerical stability, throughput, and reproducibility.

What This Team Does

This team owns and evolves the core infrastructure behind our training systems.

We focus on:

• Reinforcement learning training infrastructure

• Distributed training and inference systems

• Experiment infrastructure and reproducibility

• Large-scale data pipelines

The goal is to build the engineering foundation that allows researchers to iterate quickly while training models at massive scale.

About the Role

You will architect and optimize the core training infrastructure that powers our models. This includes RL training loops, distributed GPU systems, and large-scale data pipelines.

You will work closely with researchers to transform new ideas into reliable, scalable training systems.

Responsibilities include:

• Designing and optimizing large-scale training loops and data pipelines.

• Implementing state-of-the-art techniques and ensuring they are numerically stable and computationally efficient.

• Building internal tooling for launching, monitoring, and reproducing complex experiments.

• Diagnosing deep bottlenecks across the training stack (GPU memory issues, communication overhead, dataloader stalls).

• Translating research prototypes into reusable, production-grade infrastructure.

What You'll Work With

Distributed Training

• GPU parallelism (data, tensor, pipeline, expert)

• Large-scale distributed training infrastructure

• Communication optimization (NCCL, RDMA, GPU interconnects)

• FSDP / ZeRO and model sharding

Orchestration & Runtime Systems

• Ray, Kubernetes, Slurm

• Distributed runtimes and async systems

• Containerization and sandboxing

Frameworks

• PyTorch

• JAX

• Megatron-style training stacks

• Triton / custom kernels

Data Infrastructure

• Large-scale dataset curation pipelines

• Deduplication and filtering systems

• Tokenization and preprocessing

• Distributed data processing frameworks

About You

• You are a strong software engineer who speaks the language of machine learning.

• You may not have a PhD, but you know how to implement a research paper.

• You have deep experience in at least one of the following: Distributed Training & Inference or Data Infrastructure

• You enjoy working at the boundary between:

  • Machine learning algorithms

  • Distributed systems

  • High-performance computing

• You care deeply about performance, numerical stability, and reproducibility.

• You thrive in high-agency environments and enjoy solving hard technical problems.

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.