---
title: 'Member of Technical Staff - Research, Post-Training at Modal'
canonical: 'https://feeny.ai/job/member-of-technical-staff-research-post-training-modal-new-york-zgntyn07gfp2'
type: 'job'
last_seen: '2026-09-08'
---

# Member of Technical Staff - Research, Post-Training at Modal

- **Company:** Modal
- **Location:** New York, NY
- **Employment:** full-time
- **Posted:** 2026-07-05
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/modal/7db771eb-e379-47fe-974f-765a8780dc10

## Job description

## ABOUT US:

AI needs a new infrastructure layer. We're building it at Modal.

Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.

Our customers include category-defining companies like Lovable https://modal.com/blog/lovable-case-study, Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.

We recently raised a $355M Series C https://modal.com/blog/modal-series-c at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.

Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.

## THE ROLE:

We're building a platform that covers the whole life of an LLM: training it, deploying it, and observing it in production. We already run multi-node training, elastic inference, sandboxes, and distributed volumes, and we control the infrastructure underneath. We’re looking for research depth in post-training to sit alongside our systems and product work.

## WHAT YOU'LL DO:

We are looking for research scientists with a strong track record in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our research team. This role is well suited to candidates interested in improving existing methods and developing new techniques for large-scale model training, optimization, and inference, extending models to long-context and long-horizon tasks, and improving inference-time efficiency, reliability, and robustness in high-stakes real-world deployments.

## PREFERRED QUALIFICATIONS:

- A PhD in computer science, machine learning, or a related field. Candidates with a master’s degree and significant research or industry experience will also be considered.
- A demonstrated record of research accomplishments in reinforcement learning, machine learning, foundation models, or related fields.
- Experience with large-scale training and inference infrastructure, including distributed systems and multi-node GPU clusters.
- Experience developing, training, optimizing, or deploying state-of-the-art large-scale models.
- First-author publications at leading venues such as NeurIPS, ICML, ICLR, CoRL, CVPR, UAI, JMLR, or TMLR.
- A mission-driven mindset and a strong desire to translate research advances into meaningful product impact.
- A collaborative spirit and the ability to work effectively across research and engineering teams.

## About Modal

## Company Overview
- **One-liner**: Modal provides high-performance AI infrastructure—a serverless, globally distributed GPU cloud for inference, training, sandboxes, and agent workloads.
- **Entity Type**: Private (raised over $466M, Series D from top-tier investors)
- **Headquarters**: New York, NY, USA (with offices in Stockholm, Sweden and San Francisco, CA, USA)
- **Founded**: Not publicly available in provided sources
- **Founders**: Erik Bernhardsson and Akshat Bubna

## Core Business
- Primary industry: Cloud infrastructure / AI compute / Developer tools
- Target customers: Developers and engineering teams building AI/ML products (B2B, from startups to enterprises)
- Mission or purpose: “Make it easier to iterate and ship applications for data, AI, and machine learning” and “Make cloud development work like magic.”

## Products & Services
- **[Modal Runtime](https://modal.com/)** – Serverless container platform with custom file system, scheduler, and container image builder. Sub-second cold starts and instant autoscaling from 0 to 1000+ GPUs.
- **[Modal Sandboxes](https://modal.com/)** – Isolated, ephemeral environments for running untrusted code (e.g., coding agents, RL rollouts). Programmatically spin up fresh environments with custom images.
- **[Modal Inference](https://modal.com/)** – Globally distributed inference with sub-10ms overhead latency, support for token streaming, WebRTC, WebSocket. Supports LLMs, audio, image/video generation.
- **[Modal Training](https://modal.com/)** – Fine-tuning and multi-node training on H100s, A100s, B200s with gang scheduling and InfiniBand networking. Single line of code to scale from single-GPU to multi-node clusters.
- **[Modal Batch / Async Inference](https://modal.com/)** – Run evaluations, embeddings, re-ranking, dataset generation at scale, thousands of GPUs fully parallel.
- **[Modal SDK](https://modal.com/)** – Python SDK that lets developers define infrastructure and workloads in code, then ship to the cloud.

## Market Standing
- **Valuation**: $4.65B (as of 2026, per [jobsbyculture.com](https://jobsbyculture.com/blog/working-at-modal-2026))
- **Key Metric**: Total funding raised – over $466M (per [modal.com/company](https://modal.com/company))
- **Notable Investors/Partners**: General Catalyst, Redpoint Ventures, Lux Capital, Amplify Partners, Creandum (per [modal.com/company](https://modal.com/company))
- **Growth Signals**: ~150 employees (2026); global offices in New York, Stockholm, San Francisco; rapid hiring across engineering, GTM, and G&A roles; strong traction in inference, training, and agent infrastructure.

## Competitive Advantages
- **Deep infrastructure stack**: Custom file system, container runtime, scheduler, and image builder built from scratch to optimize AI workloads.
- **Developer experience**: “Stay in Python, ship to the cloud” – composable primitives that specify everything from logic to hardware in one code file.
- **Instant elasticity**: Scale from zero to 1000+ GPUs in seconds, pay only for compute used (no reserved capacity).
- **Global GPU access with low latency**: Sub-10ms overhead for online inference via globally distributed compute.
- **Compliance and security**: SOC 2 and HIPAA compliant, data residency controls, battle-tested isolation for untrusted code.

## Strategic Focus
- **AI-native runtime**: Deepening support for inference, fine-tuning, reinforcement learning, and agent workflows.
- **Scaling for agents**: Developing sandboxes and execution layers purpose‑built for interactive coding agents and long‑running RL rollouts.
- **Expanding global capacity**: Adding GPU availability across more regions, leveraging elastic cloud capacity.
- **Enterprise readiness**: Investing in security, governance, team controls, and data residency to serve larger customers.

## Why Work Here
- **Culture and team**: Founded by engineers who created open‑source tools (Seaborn, Luigi); team includes academic researchers, olympiad medalists, and experienced engineering leaders. Flat, high‑trust environment.
- **Location / flexibility**: Offices in New York, Stockholm, and San Francisco; likely hybrid/remote‑friendly (many roles list multiple locations).
- **Compensation and growth**: Transparent salary culture (as highlighted in 2026 profile); strong growth trajectory backed by $466M in funding and a $4.65B valuation.
- **Perks**: $30/month free compute for personal projects (customer benefit, likely similar for employees); focus on developer experience and “magic.”

## Sources
1. [modal.com](https://modal.com/)
2. [modal.com/company](https://modal.com/company)
3. [jobs.ashbyhq.com/modal](https://jobs.ashbyhq.com/modal)
4. [linkedin.com/company/modal-labs](https://www.linkedin.com/company/modal-labs)
5. [jobsbyculture.com/blog/working-at-modal-2026](https://jobsbyculture.com/blog/working-at-modal-2026)

## Other roles at Modal

- [Member of Design Staff - Brand](https://feeny.ai/job/member-of-design-staff-brand-modal-new-york-ccfxde25jatb) — New York, NY
- [Detection and Response Engineer](https://feeny.ai/job/detection-and-response-engineer-modal-new-york-93gcmptvvjtd) — New York, NY
- [Revenue Operations](https://feeny.ai/job/revenue-operations-modal-san-francisco-tpwq37mk240a) — San Francisco, CA
- [Regional Director](https://feeny.ai/job/regional-director-modal-new-york-ywxxargrqp0g) — New York, NY
- [ML Research Intern](https://feeny.ai/job/ml-research-intern-modal-new-york-67tppdwsng1t) — New York, NY
- [Member of Technical Staff - Research, Inference](https://feeny.ai/job/member-of-technical-staff-research-inference-modal-new-york-1a5bpnmmvvnx) — New York, NY
- [People Operations Generalist](https://feeny.ai/job/people-operations-generalist-modal-new-york-bf7tqrws58xr) — New York, NY
- [Systems Engineering Manager](https://feeny.ai/job/systems-engineering-manager-modal-stockholm-c0pw8s1byfbj) — Stockholm, Sweden
- [Member of Technical Staff - Product (Growth)](https://feeny.ai/job/member-of-technical-staff-product-growth-modal-new-york-f2gm2kexkwpg) — New York, NY
- [Infrastructure Security Engineer](https://feeny.ai/job/infrastructure-security-engineer-modal-new-york-fjvfe2e55ns4) — New York, NY