Level

Anyscale

Software Engineer, ML Developer Experience

Anyscale is hiring a Software Engineer, ML Developer Experience for a remote role open to applicants in United States. It pays $226k-$283k a year and Level rates it ; you can apply on Anyscale.

Level's read on this job

  • Builds AI ●●●● is how Level rates this software engineer role: the job is building AI systems.
  • Pay on this posting starts 34% higher than the typical software engineer posting on Level.
  • This is listed as a remote role.
  • The posting mentions OpenAI, PyTorch and MLflow among its tools and skills.
  • Anyscale has 20 other open roles on Level.

AI in this role

Build and scale the ML developer experience platform and interfaces for AI workloads using Ray and distributed computing infrastructure.

openaipytorchmlflowraypythonclisdkkubernetesdocker
fine-tuningml-opsdistributed-systemsmachine-learningdeveloper-experiencecloud-infrastructure

About Anyscale:

At Anyscale, we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels. We're commercializing Ray, a popular open-source project that's creating an ecosystem of libraries for scalable machine learning. Companies like OpenAI, Uber, Spotify, Instacart, Cruise, and many more, have Ray in their tech stacks to accelerate the progress of AI applications out into the real world.

With Anyscale, we're building the best place to run Ray, so that any developer or data scientist can scale an ML application from their laptop to the cluster without needing to be a distributed systems expert.

Proud to be backed by Andreessen Horowitz, NEA, and Addition with $250+ million raised to date.


About the role

Anyscale is looking for a Software Engineer to join the ML Developer Experience (MLDevX) team. MLDevX owns the experience layer of the Anyscale platform: the interfaces through which users and coding agents discover, configure, run, observe, debug, and productionize AI workloads. Every user journey crosses this layer through the CLI, SDKs, APIs, UI, Workspaces, MCP, or the workflows and integrations built on top of them. Together, these form the user’s primary interface into Anyscale, turning distributed computing from a systems problem back into a coding problem. We build the common contracts, tools, control-plane services, and architecture that power these surfaces.

You will work across the stack from developer tooling to cloud infrastructure and the Ray runtime. Manage long-running operations and make failures across jobs, tasks, actors, nodes, and GPUs easier to diagnose. The systems you build must scale with the platform, remain predictable through failures, and be intuitive for developers, programmable for applications, and operable by coding agents.

This is a high impact individual-contributor role with end-to-end ownership. You will work directly with users and field teams to identify high leverage problems, shape the product and technical design, and build and operate the solution. The scope spans the AI workload loop - data preparation, fine-tuning and post-training, serving, evaluation, and iteration as well as the developer loop: code, submit, monitor, debug, optimize, and re-submit. We are looking for someone with strong product judgment, a willingness to understand the user base, and the technical depth to build high quality software for everyone from a developer learning Ray for the first time to an AI-native company or enterprise running production workloads at scale.

A snapshot of projects you may work on

  • Build the next generation of developer tooling and MLOps capabilities on Ray, designed for both developers and coding agents.

  • Develop an agent-first CLI and cohesive SDK, API, and MCP surfaces with self-discovery, structured errors, dry-run support, and consistent behavior across platform resources.

  • Work across the Anyscale Workspaces stack to improve the path from local code to distributed execution, including environments, dependencies, images, authentication, workload submission, and debugging.

  • Build cohesive experience, tools and frameworks for the AI development lifecycle, including data preparation, fine-tuning and post-training, evaluation, production serving, dataset management, experiment tracking, and lineage.

  • Build the path from a trained model to a reliable production endpoint, including model registration, deployment workflows, performance benchmarking, and LLM-specific service metrics.

  • Surface observability across the CLI, SDK, UI, and agent-facing interfaces so users can diagnose failures across jobs, tasks, actors, nodes, and GPUs.

  • Design open integrations using durable standards such as OpenAI-compatible APIs and OpenTelemetry, along with stable Jobs and Services interfaces for external orchestrators.

  • Design and operate the highly available backend services and platform architecture that power these capabilities across serverless and bring-your-own-cloud environments.

  • Work closely with users and field teams to scope, ship, and iterate on the product, and collaborate with distributed systems and machine learning experts to push the boundaries of AI infrastructure.

We'd love to hear from you if you have

  • Bachelor's degree in Computer Science, Engineering, or equivalent practical experience

  • 5+ years of experience writing high-quality production code

  • A solid background in algorithms, data structures, and system design

  • Experience working with modern machine learning tooling — PyTorch, MLflow, data catalogs, and similar

  • Hands-on experience building and operating highly available services in production

  • Strong product instincts and a track record of shipping developer-facing tools that people choose to use

Bonus points if you have

  • Experience building and maintaining open-source projects

  • Experience building and operating machine learning infrastructure in production

  • Experience building highly available serving systems

  • Experience using Ray

 

Anyscale Inc. is an Equal Opportunity Employer. Candidates are evaluated without regard to age, race, color, religion, sex, disability, national origin, sexual orientation, veteran status, or any other characteristic protected by federal or state law.

 

Anyscale Inc. is an E-Verify company and you may review the Notice of E-Verify Participation and the Right to Work posters in English and Spanish.

How we rate this

Software Engineer, ML Developer Experience at Anyscale rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Fine-tuningML OpsDistributed SystemsMachine learningDeveloper ExperienceCloud InfrastructureOpenAIPyTorch

Also common in software engineer jobs at this level

Pay

This job starts at
$226k
Typical software engineer job
$168k

This posting starts 34% above the typical software engineer job. See software engineer jobs

Questions you could be asked

  1. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  2. How do you monitor a model once it's live, and how do you know it needs retraining?
  3. Tell me about a project where distributed systems was part of your work. What did you do?
  4. Tell me about a project where machine learning was part of your work. What did you do?
  5. Tell me about a project where developer experience was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: Fine-tuning, ML Ops, Distributed Systems, Machine learning, and Developer Experience. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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