We’re making machine learning accessible to every software developer.

Machine learning can now do some extraordinary things, but it’s still hard to use. You spend all day battling with messy Python scripts, broken Colab notebooks, perplexing CUDA errors, misshapen tensors. It’s a mess.

The reason machine learning is so hard to use is not because it’s inherently hard. We just don’t have good tools and abstractions yet.

We’re making machine learning accessible to all software engineers. You should be able to import an audio transcriber the same way you import an npm package. You should be able to fine-tune GPT as easily as you can fork something on GitHub.

Read more about what we’re doing and why on our blog.

Who we are

Andreas Jansson

Andreas Jansson

Built research tools & infrastructure at Spotify. PhD in ML for music.

Auni Nair

Auni Nair

she/her

Operations, econ major, pilates enthusiast.

Ben Firshman

Ben Firshman

he/him

Product at Docker, creator of Docker Compose.

Carl Peaslee

Carl Peaslee

he/him

Former founder. First engineer at Pathpoint. MFA in creative writing.

Charlie Gleason

Charlie Gleason

he/him

Designer and engineer, keen on the intersection of design and code, and other people's dogs. Formerly Heroku, Salesforce.

Chenxi Whitehouse

Chenxi Whitehouse

PhD candidate in visual language understanding.

Cory Wilkerson

Cory Wilkerson

he/him

Retool, GitHub; California

Dan Nelson

Dan Nelson

he/him

ML Engineering at Rent the Runway and Indeed. Former bioengineer, barista.

Deepfates

Deepfates

Internet person.

Dominic Baggott

Dominic Baggott

Engineering at GOV.UK, NHS.UK. Leadership at a civic tech agency.

Dylan

Dylan

boy/boy

Chaser of balls. Best at barking.

F

F

ey/em or they/them

Recovering: physicist, game developer, civic & health technologist, engineering manager.

fofr

fofr

AI Experimenter

Gandalf Hernandez

Gandalf Hernandez

he/him

EM at Discord and Spotify building ML platforms. Photography, diving, [bh]iking.

Jake Dahn

Jake Dahn

he/him

Generalist hacker. Exploring the intersection of art and technology with AI.

Andrew Karpos

Andrew Karpos

Finance at Scale AI, former hedge fund investor. Tennis player.

Luis Catacora

Luis Catacora

he/him

Model Hacker Replicant. Previously Engineering at Capital One.

Matt Rothenberg

Matt Rothenberg

he/him

Design and Engineering at GitHub Next, Heroku. French speaker, street photographer.

Mattt Zmuda

Mattt Zmuda

Engineering at GitHub, Heroku. Purveyor of open-source software.

Dan Buch

Dan Buch

he/him/they/them

Aspiring scrapper. Learning to live with clouds.

Morgan Fainberg

Morgan Fainberg

he/him

Engineering, Infrastructure, and Open Source at Salesforce, Red Hat, HP. Repository of useless information.

Nick Stenning

Nick Stenning

he/him

Infrastructure and humans at Microsoft, Travis CI, and the UK’s Government Digital Service.

Philip Potter

Philip Potter

he/him

Engineering, operations, and security for the Government Digital Service.

Pontus Aurdal

Pontus Aurdal

Engineering at Telenor, early stage startups. Perennial sauna bather.

Priansh Shah

Priansh Shah

he/him

Likes fashion and defense, former founder, 🌸 pink/acc

Queso

Queso

he/him

Fetch enthusiast. The goodest boy.

Will Sackfield

Will Sackfield

Turning coffee into code, one line at a time.

Sakib

Sakib

he/him

Born too late to explore the world. Born too early to explore the universe. Born just in time for the AI uprising.

Tim Fall

Tim Fall

he/him

Three chatbots in a trenchcoat

Zeke Sikelianos

Zeke Sikelianos

he/him/they/them

Engineering at GitHub, npm, Heroku. Creator of Swagger.

Our investors

Matt Bornstein

a16z

Matt Bornstein

Martin Casado

Martin Casado

Stephanie Zhan

Sequoia

Stephanie Zhan

Adam Wiggins

Founder of Heroku

AI Grant

Alexandr Wang

Founder of Scale

Amjad Masad

Founder of Replit

Andrej Karpathy

OpenAI

Brian Whitman

Spotify

Dylan Field

Founder of Figma

Guillermo Rauch

Founder of Vercel

James McInerney

Research at Netflix

Lachy Groom

Stripe

Lukas Biewald

Founder of Weights & Biases

Lisha Li

Founder of Rosebud

Kurt Mackey

Founder of Fly.io

Manu Sharma

Founder of Labelbox

Nancy Xu

Founder of Moonhub

Nuno Job

Founder of Decipad

Paul Yacoubian

Founder of Copy.ai

Radu Spineanu

Founder of Two Tap

Richard Socher

Founder of You.com

Scott Johnston

CEO of Docker

Slater Stich

Activation Fund

Solomon Hykes

Founder of Docker

Suhail Doshi

Founder of Playground AI

SV Angel

Thomas Dohmke

CEO of GitHub

Tristan Handy

Founder of dbt

Y Combinator

Zach Holman

Early engineer at GitHub


Join us

We’re looking for a few people to join our small team.

We’re a kind, creative, hard-working bunch. We care about our work and our users. We’re all here to do the best work of our careers. We’re looking for the same in the people we work with.

We ship like there’s no tomorrow. But, we don’t ship crap. We optimize for speed and quality, but cut scope.

When starting this company, we thought: instead of getting a job at the best place to work, let’s make that best place to work. We want to move fast and push each other, but in an inclusive, supportive environment. We want to work with the sharpest people, but not those who have ego about it. And, we just want to have fun while we’re at it. You’ll help us make that place.

We operate as a remote-first company across American and European timezones. We have a beautiful office in San Francisco where some of us work, but you can be located anywhere from UTC-08 to UTC+02.

We want our team to feel invested in what we’re building. We pay market salary, but well-above market equity, along with all the usual things.

Open roles

If you don't see a specific role but you're excited about making AI and machine learning more accessible to developers, get in touch. Email us at jobs@replicate.com.

Machine learning performance engineer

You’re an engineer who lives and breathes high-performance machine learning. You have a deep understanding of how to make AI models run faster and more efficiently, and you’re excited about pushing the boundaries of what’s possible with current hardware.

At Replicate, we’re building the fastest way to deploy machine learning models. Your role will be crucial in optimizing the performance of the diverse range of models we host, ensuring they run as efficiently as possible on our infrastructure.

We’re looking for the right person, not just someone who checks boxes, so you don’t need to satisfy all of these things. But, you might have some of these qualities:

  • Strong applied engineering skills. You’ve deployed machine learning models in scaled-up production environments and know the challenges that come with it.
  • Deep expertise in CUDA programming and GPU acceleration techniques. You can write custom kernels in your sleep.
  • Proficiency in C++ and Python. You’re comfortable diving deep into low-level optimizations and high-level model architectures alike.
  • Extensive experience with deep learning frameworks like Torch or JAX. You know their strengths, weaknesses, and how to squeeze every ounce of performance out of them.
  • A solid grasp of machine learning algorithms, especially with a focus on diffusion models, large language models, or other generative AI techniques.
  • Familiarity with model quantization techniques, distillation, model pruning, etc. You understand the tradeoffs and know when to apply which technique.
  • You stay up-to-date with the latest developments in ML performance optimization. When a new technique drops, you’re already thinking about how to implement it.

You might be particularly good for this job if:

  • You’ve written custom CUDA kernels to significantly improve model latency and can share war stories about the process.
  • You can discuss the tradeoffs between fp8 and int8 quantization in depth, and have applied either (or both) to whatever hot new model dropped last week.
  • You get excited about diving into academic papers on ML optimization techniques and turning them into practical, production-ready code.

Email us jobs@replicate.com

Senior data engineer

You’re a generalist data and analytics expert who thrives on developing data infrastructure at scale. You act like an owner and feel a desire to lead; you’ve likely been a data engineer at traditional companies but you’re ready to amplify your impact as the first data hire at a leading AI startup.

Replicate is building the fastest way to deploy machine learning models. We offer access to all types of open source models across modalities and levels of customization. The business is complex and we need a solid data foundation to guide decisions and hold ourselves accountable. You’ll be responsible for owning this foundation across the company.

We’re looking for the right person, not just someone who checks boxes, so you don’t need to satisfy all of these things. But you probably have some of these qualities:

  • Think like a software engineer: You put things in GitHub, use continuous integration, give things good names, make it consistent with how we do things in the product. We want to create data systems that integrate with the rest of our systems, not their own silo with a different culture.
  • Experience building 0→1: You’ve set up data stacks and pipelines from scratch in the past.
  • Expert with SQL and other common analytics tools: You know your way around BI tools like Metabase and can leverage them to deliver actionable insights.
  • Agility and efficiency: You can move fast and prioritize effectively, delivering 80/20 solutions that address core needs.
  • Experience with usage-based businesses: You understand the nuances of analyzing usage-based businesses from the ground up.
  • Collaboration and partnership: You can work collaboratively with Finance, Product, Infrastructure, and Growth teams to create and maintain dashboards that measure key business metrics. You’re a solid communicator and can help teams pull together a story; you go beyond reporting metrics, diving deeper to understand what’s driving the numbers.

This role is based in our San Francisco office.

Email us jobs@replicate.com

Systems performance engineer

You’re an engineer who dreams in cycles and optimizations. You have a deep passion for squeezing every ounce of performance out of complex systems, whether that’s rendering the latest AAA game title or optimizing large-scale distributed systems.

At Replicate, we’re building the fastest way to deploy machine learning models. We’re looking for someone who can bring their performance optimization expertise to the cutting edge of AI infrastructure.

We’re looking for the right person, not just someone who checks boxes, so you don’t need to satisfy all of these things. But, you might have some of these qualities:

  • Mastery of C++ and a deep understanding of systems programming. You know your way around pointers, memory management, and low-level optimizations.
  • Experience with high-performance computing environments. Whether it’s game engines, financial systems, or scientific simulations, you’ve worked on projects where every microsecond counts.
  • Comfort with parallel programming models. You’ve wrestled with multi-threading, SIMD instructions, or GPU programming (like CUDA or similar).
  • A track record of optimizing large-scale production deployments. You understand the challenges of performance at scale and how to address them.
  • An insatiable curiosity about new technologies and techniques. You’re always looking for the next tool or approach that could give your code that extra boost.
  • Excellent problem-solving skills. You enjoy diving into complex performance issues and emerging with elegant, efficient solutions.

You might be particularly good for this job if:

  • You’ve optimized rendering pipelines for video games or real-time graphics applications.
  • You’ve worked on high-frequency trading systems where nanoseconds make a difference.
  • You’ve contributed to open-source projects focused on performance-critical libraries or tools.
  • You get excited about diving into unfamiliar codebases and finding unexpected optimizations.
  • You have experience profiling and optimizing applications across different hardware architectures.

While prior experience with AI or machine learning isn’t required, you should be excited about applying your performance optimization skills to this domain. We believe your expertise in cycles, memory management, and systems-level thinking will translate well to the challenges of optimizing AI model inference and training.

Email us jobs@replicate.com

Technical recruiter

You’ll be responsible for working with hiring managers and our teams to understand needs, develop recruiting strategies, and ensure a candidate experience that feels like Replicate. The goal is to get the right people into Replicate, whatever it takes.

You are a technical recruiter with a deep understanding of the startup space (bonus points for experience in gen ai), excellent communication skills and a proven track record in sourcing, engaging, and hiring candidates for highly technical roles.

We’re looking for the right person, not just someone who checks boxes, so you don’t need to satisfy all of these things. But, you might have some of these qualities:

  • Outstanding communication skills. You’ll be the first person interacting with most candidates.
  • Comfort running a full desk. You like to be the one who handles sourcing to closing and building those trusting relationships with your candidates.
  • Deep experience hiring software engineers. You are a tough critic and take ownership to ensure on the best candidates get through our screens.
  • Scrappy, startup energy. You are joining a small team and will help continue to build the foundation of recruiting at Replicate.

This role is based in our San Francisco Office.

Email us jobs@replicate.com