Scribe Logo

Scribe

Staff Machine Learning Engineer

Reposted 12 Days Ago
In-Office
San Francisco, CA
200K-250K Annually
Senior level
In-Office
San Francisco, CA
200K-250K Annually
Senior level
The Staff Machine Learning Engineer will lead ML strategy, architect AI infrastructure, mentor engineers, and develop impactful AI systems.
The summary above was generated by AI

tl;dr - We're looking for a highly motivated and skilled Staff Machine Learning Engineer to build the future of AI productivity software. You will lead high-impact research and development, drive our core AI technical strategy, pioneer the deployment of robust and scalable state-of-the-art models — see our work on ScribeAgenthere —to millions of users, and mentor our growing team of elite engineers.

About this role

In this pivotal role, you will be a technical cornerstone for our growing team of elite engineers (currently 5, trained at the world's best ML programs), shaping our AI/ML technical direction and long-term vision. The hiring manager is Mouad Hadji, and you will be responsible for:

  • Acting as the technical leader by driving the design of our ML technical roadmap and aligning it closely with strategic business objectives

  • Architecting and deploying reliable, scalable AI infrastructure in collaboration with Engineering and DevOps teams to support millions of users

  • Defining ML engineering best practices across the organization, including model development, evaluation, deployment, and monitoring standards

  • Mentoring and growing the ML team, providing technical guidance and helping engineers level up their skills

  • Leading cross-functional initiatives with product, engineering, and leadership teams to ensure ML work directly enhances user experiences

  • Spearheading the end-to-end development of our most complex and impactful AI systems, from research to production

  • Representing Scribe's technical brand through conference talks, blog posts, and open-source contributions

Check out this post from our amazing ML Engineer, Atishay Jain, about what it's like to build at Scribe.

About you

You’d be a great fit for this role if you:

  • Have 5+ years of industry experience working deep in the weeds on hard ML problems, with at least 2+ years in a technical leadership role

  • Have hands-on experience architecting and deploying LLMs in production at scale, with deep understanding of infrastructure challenges

  • Demonstrate a track record of shipping impactful ML products that drive significant business value

  • Demonstrate technical leadership experience, including defining technical roadmaps, leading cross-team initiatives, and mentoring engineers

  • Have proven expertise in building ML platforms and infrastructure that enable rapid experimentation and reliable production deployment

  • Excel at technical communication, able to explain complex ML concepts to both technical and non-technical stakeholders

  • Have deep expertise with the full ML stack from distributed training (e.g., PyTorch, JAX, Accelerate, FSDP) to production serving and monitoring

  • Bring strong intuitions about ML systems design, including trade-offs between model complexity, latency, cost, and performance

  • Have experience with advanced techniques including multi-modal models, alignment (RLHF, constitutional AI), and efficient inference

  • Think strategically about AI's role in product development and can translate business needs into technical solutions

About Scribe

Scribe is where exceptional people come to do the best work of their careers. More than 94% of the Fortune 500 use Scribe to document and scale how work gets done. We’re growing fast — since our founding in 2019, we’ve grown to over 4 million users across 600,000 businesses. Based in San Francisco, we've raised $55M in funding from top-tier investors and are honored to have been named as a Forbes Next Billion Dollar Startup and LinkedIn Top Startup. Join us in our mission to uplevel how people do work.

How we work

We are builders aspiring to master our crafts. We care deeply about our teammates and want to win, together. We embrace the following values:

  • Accelerate impact

  • Raise the bar

  • Make our users heroes

  • Clear is kind

  • Rapid learning machine

  • One team, one dream

Full-Time US Employee Benefits Include

  • Some of the nicest and smartest teammates you’ll ever work with

  • Competitive salaries

  • Comprehensive healthcare benefits

  • Exciting and motivating equity

  • Flexible PTO

  • 401k

  • Parental Leave

  • Commuter Benefits (SF office employees)

  • WFH Stipend

Compensation

$200,000 - $250,000 base + Equity + Benefits. We consider several factors when determining compensation, including location, experience, and other job-related factors.

Location

Hybrid position (need to commute to downtown San Francisco 3+ days a week) unless this is someone with more experience (5+ years) or immensely talented.

At Scribe, we celebrate our differences and are committed to creating a workplace where all employees feel supported and empowered to do their best work. We believe this benefits not only our employees but our product, customers, and community as well. Scribe is proud to be an Equal Opportunity and Affirmative Action Employer.

Top Skills

Accelerate
Cloud Infrastructure
Jax
Large Language Models (Llms)
Python
PyTorch
HQ

Scribe San Francisco, California, USA Office

427 Brannan S, San Francisco, California, United States, 94107

Similar Jobs

Yesterday
Remote or Hybrid
Santa Clara, CA, USA
173K-303K Annually
Mid level
173K-303K Annually
Mid level
Artificial Intelligence • Cloud • HR Tech • Information Technology • Productivity • Software • Automation
As a Staff Machine Learning Engineer, you will design and implement infrastructure and platform features for AI workloads, collaborate with teams, improve SRE practices, and mentor colleagues.
Top Skills: AnsibleDockerGitlab CiGoHelmJ2EeJavaKubernetesLinuxNvidia GpusPrometheusPythonSplunk
4 Days Ago
Hybrid
Sunnyvale, CA, USA
195K-298K Annually
Senior level
195K-298K Annually
Senior level
Automotive • Big Data • Information Technology • Robotics • Software • Transportation • Manufacturing
The Staff ML Engineer will design and implement backend components for the ML Inference Platform, ensuring efficient model serving and collaborating with ML teams to enhance AI infrastructure at GM.
Top Skills: Aws)AzureC++Cloud Platforms (GcpGoMl InferenceModel Serving Frameworks (TritonPythonRayserveVllm)
5 Days Ago
Remote or Hybrid
Santa Clara, CA, USA
173K-303K Annually
Senior level
173K-303K Annually
Senior level
Artificial Intelligence • Cloud • HR Tech • Information Technology • Productivity • Software • Automation
The Staff Machine Learning Engineer will design and implement infrastructure for AI workloads, ensure GPU clusters' efficiency, and collaborate with teams on AI-driven solutions, while mentoring colleagues and contributing to operational improvements.
Top Skills: AnsibleGitlab CiGoHelmJavaKubernetesNvidia GpusPrometheusPythonSplunk

What you need to know about the San Francisco Tech Scene

San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.

Key Facts About San Francisco Tech

  • Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
  • Major Tech Employers: Google, Apple, Salesforce, Meta
  • Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
  • Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
  • Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
  • Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine

Sign up now Access later

Create Free Account

Please log in or sign up to report this job.

Create Free Account