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About This Role
CoreWeave, the AI Hyperscaler™, acquired Weights \& Biases to create the most powerful end\-to\-end platform to develop, deploy, and iterate AI faster. Since 2017, CoreWeave has operated a growing footprint of data centers covering every region of the US and across Europe, and was ranked as one of the TIME100 most influential companies of 2024\. By bringing together CoreWeave's industry\-leading cloud infrastructure with the best\-in\-class tools AI practitioners know and love from Weights \& Biases, we're setting a new standard for how AI is built, trained, and scaled.
The integration of our teams and technologies is accelerating our shared mission: to empower developers with the tools and infrastructure they need to push the boundaries of what AI can do. From experiment tracking and model optimization to high\-performance training clusters, agent building, and inference at scale, we're combining forces to serve the full AI lifecycle — all in one seamless platform.
Weights \& Biases has long been trusted by over 1,500 organizations — including AstraZeneca, Canva, Cohere, OpenAI, Meta, Snowflake, Square,Toyota, and Wayve — to build better models, AI agents and applications. Now, as part of CoreWeave, that impact is amplified across a broader ecosystem of AI innovators, researchers, and enterprises.
As we unite under one vision, we're looking for bold thinkers and agile builders who are excited to shape the future of AI alongside us. If you're passionate about solving complex problems at the intersection of software, hardware, and AI, there's never been a more exciting time to join our team.
About the Role:
Reporting to the Senior Engineering Manager for ML Workflows, this Senior Software Engineer will own the architecture and evolution of core systems in the Weights \& Biases platform.
ML Workflows owns what customers use to move models and data through W\&B. Artifacts is the versioned data layer that every object logged to W\&B runs through. Registry sits on top of it as the organization\-wide source of truth for what is production\-ready, and it is used by teams at Pinterest, Dropbox, and Canva. Automations is the event and action layer the rest of the platform builds on. Launch is execution infrastructure that runs customer ML and agent workloads.
You will own systems end to end, from design through production reliability, working across the W\&B backend, the Python SDK, and the frontend. You will partner closely with product managers, designers, and ML platform engineers, and you will talk directly to enterprise ML teams about their workflows. We hire senior engineers into the organization and match them to a team based on their strengths and interests, which we discuss with you during the interview process.
W\&B is part of CoreWeave, so these products are being built onto CoreWeave infrastructure, and there is real room to shape how that happens.
What You'll Do
- Own the architecture and evolution of a core platform area, designing systems that scale to billions of artifacts and high\-volume event and job throughput.
- Dive deep into system architecture to find optimization opportunities, solve complex bugs, and make the difficult calls that balance short\-term delivery against long\-term platform health.
- Work across the stack as the problem requires, from Go backend services and GraphQL APIs to the Python SDK and the TypeScript frontend.
- Lead migrations and re\-architecture of systems carrying live customer traffic, keeping the experience seamless for the customers who depend on them.
- Identify and refactor technical debt, improving maintainability and developer productivity.
- Establish engineering patterns and best practices that hold up as the platform grows.
- Collaborate with product and design to turn complex user requirements into clean technical implementations.
- Work directly with customers to understand their ML workflow challenges, and bring that feedback into the product cycle.
- Mentor engineers and raise the technical bar across the team.
What We're Looking For
We expect all of these:
- 6\+ years of software engineering experience, including time leading significant technical initiatives or architectural changes
- Strong proficiency in Go, or in another compiled language (C, C\+\+, C\#, Rust, Java) with the ability to ramp on Go quickly
- Working proficiency in Python
- Comfort diving into complex systems you did not write, to diagnose and fix difficult bugs across service boundaries
- A track record of mentoring engineers and raising the technical bar around you
- Strong communication, including the ability to explain complex technical decisions to different audiences
And real depth in at least one or two of these, which is also how we work out which team you join:
- Distributed systems and infrastructure. Designing and operating services in production, containers and Kubernetes, cloud infrastructure, orchestration.
- Frontend and full\-stack. TypeScript and React, complex state management, frontend performance work, taking a feature all the way through to the UI.
- Data at scale. Schema design, query performance, large\-scale storage, and analytical stores such as ClickHouse.
- APIs and developer surfaces. API design, GraphQL query optimization and data fetching strategies, SDKs and client libraries.
- Event\-driven systems. Message queues such as Kafka or PubSub, delivery semantics, idempotency, and retries.
We do not expect one person to have all of this. Depth in a couple of these areas and the range to work outside them is what we are after. Experience in ML infrastructure, MLOps, or developer tools is a plus.
The base pay and target total cash for this position range from $165,000 to $220,000\. The starting salary will be determined based on job\-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility)
What We Offer
The range we've posted represents the typical compensation range for this role. To determine actual compensation, we review the market rate for each candidate which can include a variety of factors. These include qualifications, experience, interview performance, and location.
In addition to a competitive salary, we offer a variety of benefits to support your needs. The benefits below reflect our US\-based offerings; for roles in other locations, benefits vary and are shared during the hiring process. These include:
- Medical, dental, and vision insurance \- 100% paid for by CoreWeave
- Company\-paid Life Insurance
- Voluntary supplemental life insurance
- Short and long\-term disability insurance
- Flexible Spending Account
- Health Savings Account
- Tuition Reimbursement
- Ability to Participate in Employee Stock Purchase Program (ESPP)
- Mental Wellness Benefits through Spring Health
- Family\-Forming support provided by Carrot
- Paid Parental Leave
- Flexible, full\-service childcare support with Kinside
- 401(k) with a generous employer match
- Flexible PTO
- Catered lunch each day in our office and data center locations
- A casual work environment
- A work culture focused on innovative disruption
California Applicants
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Equal Opportunity \& Accommodations
*CoreWeave is an equal opportunity employer, committed to fostering an inclusive and supportive workplace. All qualified applicants and candidates will receive consideration for employment without regard to race, color, religion, sex, disability, age, sexual orientation, gender identity, national origin, veteran status, or genetic information.*
*As part of this commitment and consistent with the* *Americans with Disabilities Act (ADA), CoreWeave will ensure that qualified applicants and candidates with disabilities are provided reasonable accommodations for the hiring process, unless such accommodation would cause an undue hardship. If reasonable accommodation is needed, please contact:* *[email protected].*
Export Control Compliance
This position requires access to export controlled information. To conform to U.S. Government export regulations applicable to that information, applicant must either be (A) a U.S. person, defined as a (i) U.S. citizen or national, (ii) U.S. lawful permanent resident (green card holder), (iii) refugee under 8 U.S.C. § 1157, or (iv) asylee under 8 U.S.C. § 1158, (B) eligible to access the export controlled information without a required export authorization, or (C) eligible and reasonably likely to obtain the required export authorization from the applicable U.S. government agency. CoreWeave may, for legitimate business reasons, decline to pursue any export licensing process.
Salary Context
This $165K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Weights & Biases, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($192K) sits 10% below the category median. Disclosed range: $165K to $220K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Weights & Biases AI Hiring
Weights & Biases has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Bellevue, WA, US. Compensation range: $220K - $220K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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