Interested in this AI/ML Engineer role at terra?
Apply Now →About This Role
We’re looking for a senior strategist who lives and breathes SEO and can’t wait to push it into the next era. You’re fascinated by how AI is changing the way people find information and want to lead the charge in Generative Engine Optimization (GEO).
If you’re passionate about helping brands grow through organic visibility, data\-driven insights, and next\-generation search, we’d love to meet you. This role is part of our expanding Digital Strategy team and is ideal for someone who’s ready to lead, teach, and shape the future of organic marketing.
### Who You Are
- You have 5\+ years of SEO experience, with a proven record driving measurable growth through technical, on\-page, and content\-based strategies.
- You stay ahead of how AI and generative search are transforming discovery and are eager to experiment, learn, and lead in this new landscape.
- You understand how to structure content, data, and site architecture to help both search engines and generative engines identify, cite, and surface brand information accurately.
- You thrive at the intersection of SEO, content strategy, analytics, and emerging technology, and you know how to translate complexity into clarity for clients and teammates.
- You’re a strong and adaptable writer who can turn complex concepts into clear, engaging, and search\-optimized content that resonates with both humans and algorithms.
- You’re an inspiring mentor who enjoys leveling up others’ skills and guiding clients through sophisticated organic strategies.
- You’re data\-driven, comfortable with experimentation, and excited about the evolving relationship between search, content, and AI.
- Agency experience and client\-facing communication skills are strongly preferred.
### What You'll Do
- Lead SEO and GEO strategy for key clients, combining technical expertise with creative and analytical thinking.
- Audit and optimize websites for search visibility, crawlability, and AI discoverability — including structured data, schema markup, and content frameworks.
- Guide content teams on how to produce authoritative, AI\-friendly content that ranks well and is surfaced or cited by generative models.
- Contribute to and review content development, ensuring it reflects strategic keyword intent while maintaining brand voice, clarity, and editorial quality across markets.
- Build and maintain data\-driven dashboards that visualize organic and AI\-based visibility metrics.
- Collaborate with Paid Media, Content, and Development teams to deliver integrated, insight\-driven strategies.
- Educate clients and internal teams about GEO principles, best practices, and the shifting dynamics of AI\-powered search.
- Research new tools and platforms shaping the future of SEO, GEO, and digital analytics — and bring that innovation to our clients.
- Support company leadership in refining and expanding our organic marketing offerings as the landscape evolves.
### Parks \& Benefits
- In addition to 11 observed holidays, salaried team members have unlimited paid time off, with an additional 4 mental wellness days per year
- 100% company funded health insurance, with dental and vision options
- 401(k) plan to help save for your future
- Permanent remote work option
- Summer Fridays (office closes at 3:00 PM) and Fall/Winter/Spring Fridays (office closes at 5:00 PM)
- Monthly wellness stipend and quarterly employee appreciation gift
- One\-time reimbursement for work from home equipment
- Monthly team bonding sessions / happy hours
- Pre\-tax commuter benefits
- The opportunity to join a dynamic, close\-knit team that loves going to work with and for each other every day
Salary is commensurate with experience and geographic location.About Terra
Terra is a digital marketing and creative agency built by an integrated and international team of content creators, strategists, designers, and developers. We help organizations reimagine and deliver their most ambitious digital projects and initiatives.
Acting as an extension of your team, we craft exceptional online experiences, content, and marketing strategies for brands around the world. We take our clients’ challenges personally and do the hard, human work required to produce creative solutions that get results.
Terra is also an equal opportunity employer.
We recruit, employ, train, compensate, and promote team members regardless of their race, religion, color, national origin, sex, disability, age, veteran status, or any other protected status (as required by applicable law).
Our top goal as an employer is to bring together a diverse mix of talented people who are excited to join our company, stay with Terra for a long time, and do their best work while they’re here. We take pride in the quality of the services and work we provide to our clients, and we know none of it is possible without the hard work and commitment of our passionate and creative employees.
Salary Context
This $100K-$115K range is in the lower quartile 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 terra, 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 in Demand for This Role
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 ($107K) sits 50% below the category median. Disclosed range: $100K to $115K.
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.
terra AI Hiring
terra has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $115K - $115K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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
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