---
title: 'Member of Technical Staff, Applied Research at Embedding VC'
canonical: 'https://feeny.ai/job/member-of-technical-staff-applied-research-embedding-vc-new-york-zbgchmyepex3'
type: 'job'
last_seen: '2026-11-04'
---

# Member of Technical Staff, Applied Research at Embedding VC

- **Company:** Embedding VC
- **Location:** New York, NY
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-10-16
- **Last confirmed live:** 2026-11-04
- **Apply:** https://jobs.ashbyhq.com/embedding-vc/99019d72-6e72-4a87-bab2-6452bae36591

## Job description

## About Us

Chakra Labs' mission is to encode human taste into intelligence. We build high-fidelity environments, evals, and datasets for frontier AI research, working with several of the top labs. Our work sits at the frontier of post-training, agent environments, data quality, and research infrastructure. We care about building systems that make models better in ways that are measurable, useful, and hard to fake.

## What You’d Work On

- Post-training loops. You’d help design and run model improvement workflows across supervised fine-tuning, preference optimization, and reinforcement learning approaches like GRPO. The work is not just launching training jobs; it’s figuring out what signal matters, how to collect it, and whether the model actually improved.
- Environment and task design. We build environments that feel real and scenarios that push agents past static benchmark behavior. You’d design tasks, tools, validators, reward signals, and evaluation harnesses that test meaningful capabilities instead of whatever is easiest to measure.
- High-fidelity trajectories. You’d create, inspect, and improve the data that teaches models how to behave. That means caring about taste, correctness, edge cases, and whether a trajectory would actually help a frontier model learn.
- Reward and evaluation systems. You’d work on reward functions, rubrics, validators, and analysis tools that turn messy model behavior into useful training signal. You should be interested in where evals lie, where rewards get hacked, and how to make measurements more robust.
- Training and research infrastructure. You’d run experiments across distributed GPU clusters, work with PyTorch and FSDP, and build the infrastructure needed to support model training, evaluation, and data generation at scale.
- Customer research problems. You’d work with frontier AI labs to translate ambiguous research goals into concrete environments, datasets, experiments, and deliverables.

## About You

- Machine learning fundamentals. You have Masters / PhD-level knowledge of machine learning fundamentals. You understand linear algebra, optimization, stochastic gradient descent, and can reason from first principles when a model or training run behaves unexpectedly.
- Hands-on post-training experience. You have worked with or deeply understand LLM fine-tuning, preference data, reward modeling, or reinforcement learning for language models. You know the difference between reproducing a recipe and understanding why it works.
- Environment-building instinct. You are excited by agent environments, multi-turn tool use, sandboxed tasks, eval harnesses, and the question of how to test capabilities that do not fit neatly into a benchmark.
- Python and PyTorch fluency. You are strong in Python and comfortable building with PyTorch, FastAPI, and modern ML infrastructure. You can work at a high level, but you are also comfortable dropping into lower-level primitives when the abstraction leaks.
- Strong engineering judgment. You can move between research ambiguity and production constraints. You know when to iterate quickly, when to be rigorous, and when a result is too fragile to trust.
- Experience. No hard rule. Roughly 3-5 years is what we imagine, but more or less experience works if the expectations above resonate with you.

What Makes This Different

- The work is concrete. You are not just “touching the latest AI stack.” You are building the environments, trajectories, evals, rewards, and training loops that frontier labs use to improve models.
- It is research-facing, but production-minded. Our customers are AI researchers and labs pushing the edge of what agents can do. The systems you build need to support real experiments, real users, and real deadlines.
- Ownership, not theater. You own whole problems, not isolated tickets. One week you might be designing a new environment; the next you might be debugging a training run, improving a reward function, or scaling an eval pipeline.
- Ambiguity is part of the job. There is no fixed playbook for this work. Data, post-training, and agent evaluation are changing quickly. If you need every problem to be fully specified before you begin, this role will be challenging.
- The team. Our team is ex-Stripe, Snap, AWS, Microsoft, Airtable — you'll work with a small team who has years of shipping high-impact products over the last decade.

## About Embedding VC

## Company Overview
- **One-liner**: Embedding VC is an early-stage venture capital firm investing in Generative AI startups, backing engineering, product, and research visionaries.
- **Entity Type**: Private (Venture Capital Firm)
- **Headquarters**: Menlo Park, California, United States
- **Founded**: 2023
- **Founders**: Roger Luo (Founder & Managing Partner)

## Core Business
- Primary industry/industries: Venture Capital, Private Equity, Artificial Intelligence
- Target customers: Early-stage Generative AI startups (Seed and Series A)
- Mission or purpose statement: To back engineering, product, and research visionaries in the mission to develop AGI, believing they are at the onset of a new computing super cycle driven by AI innovations.

## Products & Services
- **Venture Capital Investments**: Early-stage (Seed and Series A) investments in Generative AI startups. Focus areas include AI-first applications, middleware for Gen AI, and AI + game + social.
- **Portfolio Support**: Provides capital and strategic support to portfolio companies, leveraging a network of investors who are current and former builders and operators.

## Market Standing
- **Valuation/Market Cap**: Not applicable (Venture Capital firm)
- **Key Metric**: Total Funding Deployed: Not publicly disclosed. The firm has made 6 investments as of early 2026, with 5 new investments in the last 12 months.
- **Notable Investors/Partners**: The firm's investors are current and former builders and operators. Co-investors include Long Journey Ventures, Mayfield, Correlation Ventures, 468 Capital, NFX, and Menlo Ventures.
- **Growth Signals**: Active job postings (37 as of mid-2026), with a monthly growth in postings of +5.7% and quarterly growth of +48.0%. The firm's website traffic has grown +231.9% monthly. The firm has made 5 new investments in the last 12 months, including a Series A round in Entelligence in January 2026.

## Competitive Advantages
- **Founder-Operator Network**: All investors in the fund are current and former builders and operators, providing deep, hands-on expertise to portfolio companies.
- **Niche Focus**: Dedicated exclusively to early-stage Generative AI, a high-growth, cutting-edge sector, allowing for deep specialization and a strong network within the AI ecosystem.
- **Strong Portfolio**: Investments in notable AI startups like Flora (AI-powered smart glasses, $52M raised), Omi (AI personal story recording), and Entelligence (AI for engineering leadership) demonstrate a keen eye for emerging trends.

## Strategic Focus
- **Generative AI Leadership**: To be the premier early-stage partner for founders building the next generation of AI-native companies.
- **AGI Mission**: The firm explicitly states a belief in the mission to develop AGI, signaling a long-term, high-conviction investment thesis.
- **Expanding Portfolio**: Actively seeking new investments in AI-first applications, middleware for Gen AI, and AI + game + social.

## Why Work Here
- **Culture**: A small, focused team of 17 employees (as of mid-2026) with a flat structure, offering significant ownership and impact. The firm is described as a partnership.
- **Remote/Hybrid Policy**: The firm has offices in Menlo Park (HQ) and Los Angeles, CA. Job postings indicate a mix of "In-Office" and "In-Office or Remote" roles, suggesting flexibility depending on the position.
- **Notable Perks/Engineering Culture**: Working at a VC firm offers exposure to the cutting edge of AI technology and the startup ecosystem. The team is composed of specialists and management, with a strong consulting and technical background. The firm is actively hiring for technical roles (Machine Learning Engineer, Software Engineer, Data Engineer) and product roles, indicating a hands-on, builder-oriented culture within the firm itself.

## Sources
1. [embedding.vc](https://www.embedding.vc/)
2. [linkedin.com](https://www.linkedin.com/company/embeddingvc)
3. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/embedding-vc)
4. [tracxn.com](https://tracxn.com/d/venture-capital/embeddingvc/__x4iWIoyDblSMImQx3cerpGqOt15eGHuGXfZJrtw3RK0)
5. [builtin.com](https://builtin.com/company/embedding-vc/teams)

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