Interested in this AI/ML Engineer role at Writer?
Apply Now →Skills & Technologies
About This Role
### About WRITER
WRITER is where the world's leading enterprises orchestrate AI\-powered work. Our vision is to expand human capacity through superintelligence. And we're proving it's possible – through powerful, trustworthy AI that unites IT and business teams together to unlock enterprise\-wide transformation. With WRITER's end\-to\-end platform, hundreds of companies like Mars, Marriott, Uber, and Vanguard are building and deploying AI agents that are grounded in their company's data and fueled by WRITER's enterprise\-grade LLMs. Valued at $1\.9B and backed by industry\-leading investors including Premji Invest, Radical Ventures, and ICONIQ Growth, WRITER is rapidly cementing its position as the leader in enterprise generative AI.
Founded in 2020 with office hubs in San Francisco, New York City, Seattle, Austin, Chicago, and London, our team thinks big and moves fast, and we're looking for smart, hardworking builders and scalers to join us on our journey to create a better future of work with AI.
### About the role
As a deployment engineer at WRITER, you'll be at the forefront of expanding human capacity through superintelligence. This isn't just a technical role; it's a deeply influential one where you'll partner directly with our leading enterprise customers. Your expertise will be crucial in uncovering their unique business challenges and architecting AI\-powered solutions that leverage our powerful platform and enterprise\-grade LLMs. You'll transform complex needs into tangible, high\-impact applications, creating champions and driving tangible business results. Your builder's mentality and passion for bringing cutting\-edge AI into the hands of real users will directly shape the future of work for some of the world's largest companies.
This is a critical role that directly impacts our customers' success and product evolution. You'll contribute significantly to WRITER's mission, working with a dynamic team to push the boundaries of what's possible with generative AI.
This is a hybrid role based out of our San Francisco, Chicago, Austin, New York City, Seattle, and London hubs.
### What you'll do
- Partner deeply with enterprise customers to identify strategic AI use cases, validating technical feasibility and owning the end\-to\-end implementation of tailored solutions
- Architect and deliver custom applications, templates, and integrations leveraging WRITER's platform, APIs, and Knowledge Graph capabilities to solve complex business challenges
- Translate intricate technical concepts and platform capabilities into clear, prescriptive solution recommendations, guiding customers through the generative AI landscape
- Collaborate relentlessly with internal Product and Engineering teams, providing crucial customer feedback that directly influences our product roadmap and drives continuous innovation
- Drive down customer time\-to\-value by developing scalable processes, robust documentation, and efficient workflows for technical integrations
- Champion the successful adoption and expansion of WRITER's AI solutions within customer accounts, ensuring maximum impact and return on investment
### ️ What you need
- Over 5 years of hands\-on experience in technical SaaS roles, with at least 3 years specifically in solutions architecture, technical consulting, or technical account management
- Deep technical fluency in Python and extensive experience leveraging APIs, especially in designing sophisticated prompts and understanding linguistic principles for AI applications
- Proven track record of architecting and delivering complex technical solutions for Fortune 500 enterprises in a fast\-paced, high\-growth B2B SaaS environment
- Exceptional communication and executive presence, capable of simplifying complex AI concepts for diverse audiences, from technical teams to C\-suite leadership
- A true builder's mindset with a passion for emerging technologies, always eager to experiment, learn, and iterate in the rapidly evolving world of generative AI
- A relentless drive to Connect with customers and internal teams, Challenge the status status quo with innovative solutions, and Own the successful outcomes for our most strategic accounts
Benefits \& perks (US Full\-time employees)
- Generous PTO, plus company holidays
- Medical, dental, and vision coverage for you and your family
- Paid parental leave for all parents (16 weeks)
- Fertility and family planning support
- Early\-detection cancer testing through Galleri
- Flexible spending account and dependent FSA options
- Health savings account for eligible plans with company contribution
- Annual work\-life stipends for:
+ Wellness stipend for gym, massage/chiropractor, personal training, etc.
+ Learning and development stipend
- Company\-wide off\-sites and team off\-sites
- Competitive compensation, company stock options and 401k
*WRITER is an equal\-opportunity employer and is committed to diversity. We don't make hiring or employment decisions based on race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other basis protected by applicable local, state or federal law. Under the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.*
By submitting your application on the application page, you acknowledge and agree to WRITER's Global Candidate Privacy Notice.
Compensation Range: $146\.4K \- $185K
Salary Context
This $146K-$185K range is below 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 Writer, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($165K) sits 23% below the category median. Disclosed range: $146K to $185K.
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.
Writer AI Hiring
Writer has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $185K - $185K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.