Interested in this AI/ML Engineer role at Lilt- Lega Italiana per la Lotta contro i Tumori?
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About This Role
About LILT
AI is changing how the world communicates — and LILT is leading that transformation.
We're on a mission to make the world's information accessible to everyone, regardless of the language they speak. We use cutting-edge AI, machine translation, and human-in-the-loop expertise to translate content faster, more accurately, and more cost-effectively without compromising on brand, voice, or quality.
At LILT, we empower our teammates with leading tools, global collaboration, and growth opportunities to do their best work. Our company virtues—Work together, win together; Find a way or make one; Dance in the customer's shoes; Quicker than they expect; Quality is Job 1—guide everything we do. We are trusted by Intel Corporation, Canva, the United States Department of Defense, the United States Air Force, ASICS, and hundreds of global Enterprises. Backed by Sequoia, Intel Capital, and Redpoint, we’re building a category-defining company in a $50B+ global translation market being redefined by AI.
About the Role:
As a Senior Technical Program Manager of Applied AI, you will sit at the intersection of cutting-edge research and industrial-scale execution. You will operate with an entrepreneurial mindset, effectively acting as the CEO of your customer's business to proactively drive success. You are responsible for leading complex benchmark transformations and data deliverables. Rather than just managing timelines, you are architecting workflows to ensure the next generation of LLMs and multimodal models are trained on the most accurate, diverse, and complex data available, while championing innovative solutions alongside our research lab partners.
- Entrepreneurial Benchmark Leadership: Act as the CEO of your customer’s business by proactively proposing and co-developing benchmark transcreations directly with research lab partners, ensuring our deliverables are innovative, tailored, and aligned with their core research objectives.
- Frontier Lab Program Management: Lead the end-to-end execution of high-priority technical programs for frontier AI labs, managing the delivery of massive-scale datasets and bespoke engineering milestones.
- High-Volume Data Operations: Architect and oversee pipelines for delivering high-volume data samples across multiple modalities (text, image, video, audio), ensuring strict adherence to delivery cadences without sacrificing precision.
- End-to-End Quality Control: Partner with production to implement rigorous, automated, and human-in-the-loop QC frameworks to ensure data integrity across various domains and languages.
- Cross-Functional Leadership: Bridge the gap between forward-deployed engineering resources, research, and production teams to ensure LILT can serve the needs of the frontier AI market.
Qualifications
- Education: B.S or M.S. in Computer Science, AI, or a related technical field with 10+ years of industry experience. Equivalently, a Ph.D. with 5+ years of experience in a high-growth AI/Engineering environment.
- Entrepreneurial Mindset: Experience founding or scaling a venture, or operating in an early-stage startup environment where you drove business growth and strategic initiatives from the ground up.
- Strategic Execution: Strong business acumen and the ability to manage high-stakes stakeholder relationships with frontier AI labs.
- Frontier AI Expertise: Proven track record of managing data pipelines for LLMs or multimodal models. Deep understanding of training, inference, and the nuances of data diversity (multilingual and multi-domain).
- Scale & Quality: Demonstrated ability to manage "high-throughput" programs where quality control is mission-critical. You should have experience with automated validation at scale.
Preferred Skills
- Fluency in multiple human languages.
- Proficiency in Python and experience working with modern AI frameworks (PyTorch, LangChain, etc.).
What We Offer:
- Competitive salary and benefits package.
- Opportunity to work with cutting-edge AI technology in a rapidly growing industry.
- A dynamic and collaborative work environment.
- Significant opportunities for professional growth and development.
- The chance to make a real impact on global communication and business.
Where You’ll Work
This position is based in our London, UK office and will be expected to work in the office in a hybrid capacity. Boston and Indianapolis are also acceptable locations. LILT is hybrid with hubs in SF, NY, Indianapolis, Boston, London, and Berlin.
Authorization to work in the U.S. is a precondition of employment.
- London, UK (highly preferred)
- Others: Berlin, Boston, New York, Indianapolis
Our Story
Our founders, Spence and John met at Google working on Google Translate. As researchers at Stanford and Berkeley, they both worked on language technology to make information accessible to everyone. While together at Google, they were amazed to learn that Google Translate wasn’t used for enterprise products and services inside the company.The quality just wasn’t there. So they set out to build something better. LILT was born.
LILT has been a machine learning company since its founding in 2015. At the time, machine translation didn’t meet the quality standard for enterprise translations, so LILT assembled a cutting-edge research team tasked with closing that gap. While meeting customer demand for translation services, LILT has prioritized investments in Large Language Models, human-in-the-loop systems, and now agentic AI.
With AI innovation accelerating and enterprise demand growing, the next phase of LILT’s journey is just beginning.
Our Tech
What sets our platform apart:
- Brand-aware AI that learns your voice, tone, and terminology to ensure every translation is accurate and consistent
- Agentic AI workflows that automate the entire translation process from content ingestion to quality review to publishing
- 100+ native integrations with systems like Adobe Experience Manager, Webflow, Salesforce, GitHub, and Google Drive to simplify content translation
- Human-in-the-loop reviews via our global network of professional linguists, for high-impact content that requires expert review
LILT in the News
- Featured in The Software Report’s Top 100 Software Companies!
- LILT makes it onto the Inc. 5000 List.
- LILT’s continues to be an intellectual powerhouse, holding numerous patents that help power the most efficient and sophisticated AI and language models in the industry.
- Check out all our news on our website.
Information collected and processed as part of your application process, including any job applications you choose to submit, is subject to LILT's Privacy Policy at https://lilt.com/legal/privacy*.*
At LILT, we are committed to a fair, inclusive, and transparent hiring process. As part of our recruitment efforts, we may use artificial intelligence (AI) and automated tools to assist in the evaluation of applications, including résumé screening, assessment scoring, and interview analysis. These tools are designed to support human decision-making and help us identify qualified candidates efficiently and objectively. All final hiring decisions are made by people. If you have any concerns, require accommodations, or would like to opt-out of the use of AI in our hiring process, please let us know at [email protected].
LILT is an equal opportunity employer. We extend equal opportunity to all individuals without regard to an individual’s race, religion, color, national origin, ancestry, sex, sexual orientation, gender identity, age, physical or mental disability, medical condition, genetic characteristics, veteran or marital status, pregnancy, or any other classification protected by applicable local, state or federal laws. We are committed to the principles of fair employment and the elimination of all discriminatory practices.
Compensation Range: £88.8K - £111K
Salary Context
This $88K-$111K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Lilt- Lega Italiana per la Lotta contro i Tumori, 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($99K) sits 53% below the category median. Disclosed range: $88K to $111K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Lilt- Lega Italiana per la Lotta contro i Tumori AI Hiring
Lilt- Lega Italiana per la Lotta contro i Tumori has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Seattle, WA, US, Indianapolis, IN, US. Compensation range: $111K - $161K.
Location Context
AI roles in Seattle pay a median of $226,100 across 1,095 tracked positions. That's 6% above the national 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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
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