Let's cut straight to the chase. If you're looking for the Together AI number of employees, the most current reliable figure, based on data from platforms like LinkedIn and Crunchbase, points to a workforce of between 80 and 120 people as of mid-2024. But that number alone is about as useful as knowing the horsepower of a car without seeing the engine. The real story isn't in the static headcount; it's in the explosive growth trajectory, the specific talent they're hunting for, and what that tells us about their strategy in the brutal AI infrastructure race.

I've been tracking AI startups for a while, and one mistake I see constantly is judging a company's scale solely by its employee total. A 50-person team focused purely on research is vastly different from a 50-person team split between engineering, sales, and support. For Together AI, their employee composition reveals a company aggressively building out a full-stack, product-centric operation, not just a research lab.

What You'll Find in This Guide

The Latest Together AI Employee Count & Sources

Pinpointing an exact, real-time number for a private company is tricky. They don't issue press releases every time they hire a new engineer. So we rely on a few key sources, each with its own caveats.

LinkedIn is usually the best proxy. As of now, the Together AI LinkedIn page shows around 100-120 employees listed. The catch? Not everyone updates their LinkedIn, especially senior researchers or recent hires. It's a solid indicator, but often a slight undercount.

Crunchbase and other startup databases often list ranges. Crunchbase currently suggests a range of 51-200. This broad range is typical for fast-growing companies where data lags behind reality.

Based on cross-referencing these sources with job postings and funding rounds (they raised over $120 million in early 2024), the consensus lands firmly in the 80-120 employee band. To put that in perspective, when they announced their Series A in late 2022, they were likely under 30 people. That's a 3-4x growth in about 18 months.

Key Takeaway: The number is dynamic. If you're reading this a few months from now, it's almost certainly higher. The growth rate is the more critical metric than the snapshot.

Together AI's Hiring Timeline & Growth Phases

Their hiring hasn't been a steady drip. It's come in waves, closely tied to funding and strategic pivots.

Phase 1: The Research Core (2021 - Mid 2022). The company started with a heavy focus on open-source AI models and research. The early team was small, maybe 10-20 people, dominated by machine learning PhDs and engineers from places like Stanford, CMU, and Facebook AI Research. The goal was to prove technical credibility.

Phase 2: The Product & Platform Push (Late 2022 - 2023). After securing initial funding, the hiring scope expanded dramatically. This is when you started seeing roles for infrastructure engineers, DevOps specialists, front-end developers, and product managers. This signaled the shift from "cool research project" to "scalable cloud platform." The headcount probably jumped from the 20s into the 50-70 range.

Phase 3: Scaling the Business (2024 - Present). The massive $120M+ round was rocket fuel. Now the job boards are flooded with roles not just in engineering, but in developer relations, sales engineering, marketing, and customer support. This is the phase where they build the machine to acquire and serve paying customers. This is why the employee count is pushing past 100.

Each phase required a different type of employee. Missing that evolution leads to a misunderstanding of their capabilities.

Inside Their Team Structure: Who Actually Works There?

Here’s where we move beyond the generic headcount. A breakdown of their team composition, pieced together from LinkedIn profiles and job descriptions, tells a much richer story.

Team/Function Estimated % of Workforce Key Focus Areas & Why It Matters
Research & Advanced Engineering ~30-40% Core model development, inference optimization, novel architectures. This is their technical moat. A significant portion holds PhDs.
Platform & Core Infrastructure Engineering ~40-50% Cloud infrastructure, distributed systems, API reliability, security. This team builds the runway that makes their research usable at scale. Critical for customer trust.
Product, Design, & Developer Experience ~10-15% UI/UX for their console, documentation, SDKs, and tooling. A growing area that shows focus on usability, not just raw power.
Go-to-Market & Operations ~5-10% Sales, marketing, developer relations, finance, HR. This is the newest and fastest-growing segment, indicating a serious push for commercialization.

Notice the balance. They haven't neglected the hard, unsexy work of infrastructure (which many AI startups do, to their later peril). Having nearly half the team on platform engineering is a strong signal of their intention to run a reliable, enterprise-grade service.

From what I've gathered talking to a few people in the space, their research team is still the crown jewel, but the glue holding it all together is that massive platform engineering effort. A common pitfall for companies at this stage is letting research priorities constantly derail platform roadmaps, creating a unstable product. Together AI's structure suggests they're trying to avoid that.

Decoding Together AI's Hiring Strategy & Culture

Look at their open jobs. It's a blueprint for their ambitions.

They're constantly hiring for Senior/Staff-Level Distributed Systems Engineers. This isn't entry-level stuff. They need people who can build fault-tolerant systems that run across thousands of GPUs. They're also looking for ML Engineers with production experience, not just researchers. The blend is key: turning cutting-edge papers into robust API endpoints.

On the culture side, the emphasis is on "builder" mentality and open source contribution. Many employees list projects on GitHub. There's a focus on moving quickly, but given the infrastructure demands, not recklessly. The remote-first model (with hubs in the Bay Area and New York) allows them to tap into a global talent pool, which is essential when competing with the salary scales of OpenAI, Anthropic, and Google.

Their biggest challenge in hiring? The same as every other top AI firm: insane competition. They're not just fighting other startups. They're fighting the deep pockets of Big Tech who can offer million-dollar compensation packages. Together AI's pitch hinges on impact, technical challenge, and the chance to shape the foundational layer of AI, which resonates with a certain type of engineer.

What This Means for Developers, Investors, and the AI Race

For a developer considering their API, the growing team size, especially in infrastructure, is a positive signal for reliability and future feature development. A 10-person company might pivot or disappear. A 100+ person company with dedicated support and devrel teams is building for the long haul.

For an investor or analyst (tying to the "stocks directions" category), the employee growth is a key due diligence metric. Burning through $120 million with a stagnant team is a red flag. Rapid, targeted hiring in core areas aligns with sensible capital deployment. The mix of research and engineering talent reduces key-person risk and builds a more defensible business. You can read more about their funding and valuation on authoritative sites like Crunchbase.

In the broader AI infrastructure race, their size puts them in a interesting tier. They're significantly larger than most niche open-source model shops but smaller than the cloud behemoths (AWS, GCP) and the leading pure-play AI companies (OpenAI). This gives them agility and focus. Their employee growth trajectory suggests they aim to own the "open, customizable, high-performance inference" segment of the market.

Your Questions on Together AI's Workforce, Answered

How hard is it to get a job at Together AI given their small size?
Extremely competitive. With a headcount around 100 and receiving thousands of applications, the bar is very high. They prioritize demonstrated expertise, especially in systems programming or ML research, over generic credentials. A strong portfolio of open-source contributions or detailed project write-ups is often more valuable than a fancy degree alone. Don't just apply online; engage with their open-source projects or community.
Does Together AI's employee count suggest they are over-hiring after their big funding round?
It's a valid concern, but the composition argues against it being bloat. Most hires are in core engineering and research—roles that directly generate product and IP. They aren't on a massive sales hiring spree before achieving product-market fit. The hiring seems aligned with building a full-stack platform. The risk isn't over-hiring, but whether they can integrate this new talent quickly enough to out-execute competitors with more resources.
What's the single biggest mistake people make when interpreting AI startup employee numbers?
Focusing only on the total. A 150-person company with 140 researchers and 10 engineers is a research institute, not a stable service provider. Conversely, a company with too many salespeople too early can collapse. You must look at the ratio. Together AI's roughly 40/50 split between research and core engineering is a healthy, product-focused balance for this stage. Ignoring team structure is like evaluating a restaurant by the total number of staff without noticing if they have any chefs.
Is Together AI still a startup, or has the employee growth changed its culture?
At 100+ people, they're transitioning from a pure startup to a scale-up. Early employees mention processes becoming more formalized, which is necessary but can feel slower. The key will be if they can retain the "builder" energy while adding the structure needed for reliability and security. Most signs point to them managing this transition better than most, as the technical founders are still deeply involved in day-to-day engineering decisions.
Where are most Together AI employees located? Are they remote?
They operate as a remote-first company, which is a major advantage in the talent war. However, there are significant clusters in the San Francisco Bay Area and New York City, where leadership and several senior engineers are based. This distributed model allows them to hire specialists from anywhere but maintains hubs for in-person collaboration when needed. This geographic diversity is reflected in their job postings, which are rarely tied to a single location.