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About This Role
Job Purpose:
Own the marketing strategy for TrainAI, one of our flagship products, across a portfolio of named target accounts. Partner with the sales lead on account planning, then build the account-specific narrative, content and engagement that grow each account from first project to scaled revenue, while contributing to broader cross-account marketing motions.
About RWS Train AI Data Services
Training or fine-tuning artificial intelligence (AI) requires data. LOTS of data. However, not just any data will do – our clients need responsible AI data that’s targeted, accurate and reliable to ensure machine learning (ML) success. But preparing AI training data is a monumental task that can take up the vast majority of AI project time, leaving AI teams with precious little time to focus on developing, deploying and evaluating ML models. RWS Train AI helps our clients address this challenge head-on.
With a seamless blend of technological understanding and human intelligence, RWS Train AI provides complete, end-to-end data collection and content generation, data annotation or labelling, human-in-the-loop data validation and generative AI data services for all types of AI, in any language, at any scale, based on the principles of responsible AI. Today, Train AI supports four of the world’s top five technology companies, enhancing the performance of their generative AI applications by providing services such as prompt engineering, response refinement and red teaming with locale-specific domain experts across a broad range of topic areas and educational levels.
For further information, please visit: RWSTrainAI Job Overview: Key Responsibilities
Based on each target account’s unique story, work closely with TrainAI BDM(s) to complete the following for assigned accounts:
Account planning and intelligence
- Provide input into TrainAI account plans: account research, stakeholder map, key entry points/opportunities for TrainAI, bridge localization relationships into data services, ABM strategy etc.
- Run continuous account research so positioning and ABM strategy stays current and relevant to what the account is actually doing
Account narrative and assets
- Custom-tailor TrainAI cross-channel communications and ABM campaigns to directly address each account’s specific needs and challenges: personalized, account-specific sales enablement materials (presentations, one-pagers, brochures etc.), proposals/RFP responses, emails, ads, web content etc.
- Sustain a weekly communications cadence with TrainAI BDM(s), to plan and execute strategic communications into each target account on a regular basis
Reporting
- Work with marketing operations to maintain detailed, real-time TrainAI ABM reporting on key metrics/KPIs: account engagement, buyer engagement, MQAs, sales cycle length, deal size and revenue impact
ABM framework
- Contribute to a shared AI-based TrainAI ABM framework so each successful ABM play transfers to other TrainAI accounts
Other cross-account activity
Support cross-account TrainAI marketing motions as needed: brand awareness campaigns, organic social media management, value-added content production and event participation.
Skills & Experience
Required
- 5+ years’ ABM or strategic account marketing experience into large, complex B2B enterprise or hyperscaler accounts with technical buyers and lengthy, multi-stakeholder sales cycles
- Passionate about the AI industry with a willingness to learn and develop sufficient technical knowledge to be able to hold a real conversation about AI training data with an ML lead
- AI-first ways of working:
- Curious, fast-learning and genuinely willing to evolve how they work
- Already uses AI to compress research-to-asset from weeks to hours
- Keeps judgment on quality – knows when the AI draft is confidently wrong
- Grows into extending and improving reusable skills and agents rather than only consuming them
- Familiar with ABM tech/tools such as 6Sense, Influ2, Mutiny etc.
- Comfortable being the only marketer in the room with sales, and commercially literate – can read a pipeline and a stakeholder map
- Works fluently across disciplines and specialist teams
Preferred
- Experience working in the AI data services space a definite plus
Benefits
At RWS, we believe great people do their best work when they feel supported, empowered, and able to thrive both professionally and personally. That's why we offer:
- + Generous time away from work – Start with 25 days of paid time off (15 vacation days and 10 sick days), plus company holidays, a birthday day off, and paid time for volunteering in your community. + Comprehensive parental leave – Enjoy 100% paid parental leave, giving you the time and support you need during life's most important moments. + Retirement savings support – Plan for your future with our 401(k) retirement plan and company match. + Flexible ways of working – Benefit from our company-wide agile work policy, designed to support flexibility, trust, and work-life balance. + Growth and development opportunities – Access ongoing learning, professional development programs, and opportunities to expand your skills and career. + A collaborative and inclusive culture – Join a team of talented, supportive colleagues in a global environment that values diverse perspectives, innovation, and continuous learning.
Location
US, East coast prefered
Base Pay
$90,000 - $100,000 / year
Actual rate of pay may vary based on factors including, but not limited to: position offered, location, education, training, and/or experience. Total compensation may also include variable incentives, bonuses, benefits, and/or other perks, as outlined in any formal offer made.
Life at RWS: Life at RWS - If you like the idea of working with smart people who are passionate about growing the value of ideas, data and content by making sure organizations are understood, then you’ll love life at RWS.
Our purpose is to unlock global understanding. This means our work fundamentally recognizes the value of every language and culture. So, we celebrate difference, we are inclusive and believe that diversity makes us strong. We want every employee to grow as an individual and excel in their career.
In return, we expect all our people to live by the values that unite us: to partner, putting clients fist and winning together, to pioneer, innovating fearlessly and leading with vision and courage, to progress, aiming high and growing through actions and to deliver, owning the outcome and building trust with our colleagues and clients. RWS embraces DEI and promotes equal opportunity, we are an Equal Opportunity Employer and prohibit discrimination and harassment of any kind. RWS is committed to the principle of equal employment opportunity for all employees and to providing employees with a work environment free of discrimination and harassment. All employment decisions at RWS are based on business needs, job requirements and individual qualifications, without regard to race, religion, nationality, ethnicity, sex, age, disability, or sexual orientation. RWS will not tolerate discrimination based on any of these characteristics.
RWS Values
Get the 3Ps right – Partner, Pioneer, Progress – and we´ll Deliver together as RWS. Recruitment Agencies: RWS Holdings PLC does not accept agency resumes. Please do not forward any unsolicited resumes to any RWS employees. Any unsolicited resume received will be treated as the property of RWS and Terms & Conditions associated with the use of such resume will be considered null and void.
#LI-JD1
Salary Context
This $90K-$100K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At RWS, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($95K) sits 55% below the category median. Disclosed range: $90K to $100K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
RWS AI Hiring
RWS has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $100K - $100K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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
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