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About This Role
Viridien ( www.viridiengroup.com ) is an advanced technology, digital and Earth data company that pushes the boundaries of science for a more prosperous and sustainable future. With our ingenuity, drive and deep curiosity we discover new insights, innovations, and solutions that efficiently and responsibly resolve complex natural resource, digital, energy transition and infrastructure challenges.
Job Summary
We are hiring an ML Engineer to build production agentic applications that automate complex geoscience workflows. You will design agent-based systems, integrate tools and external APIs, implement persistent state management, provide agents with the right context, and deliver well-tested services with lightweight web interfaces for operators and end users.
Beyond implementation, you will help shape what these applications do: which workflows to automate, where people stay in the loop, and how results are presented and verified. You will also be responsible for designing and executing LLM fine-tuning strategies when model adaptation is required.
About the Team
We work together to Build high-impact tools and platforms that drive innovation – from scientific seismic algorithms to data visualization tools to intuitive web apps. combining our expertise across regions to drive innovation and impactful results. Our team values collaboration, continuous learning, and innovation, and we take pride in supporting each other to achieve shared goals.
Key Responsibilities
- Shape the application logic and user experience: define what a good result looks like, remove what does not help users decide, and make outputs easy to verify.
- Design agent workflows with branches, loops, sub-workflows, and explicit routing, choosing the simplest structure that solves the problem.
- Implement human-in-the-loop approvals, and decide which actions an agent may take on its own.
- Give agents the right context: tool-based search over files, logs, code, and databases, using text search, SQL, and semantic retrieval where appropriate.
- Ground answers in cited evidence, enforce tool permissions, and guard against prompt injection and unsafe actions.
- Integrate tools and external APIs. Handle concurrency, retries, and error paths.
- Add database-backed persistence. Ensure workflows can pause and resume reliably.
- Design and execute LLM fine-tuning strategies when needed: data curation, supervised fine-tuning with parameter-efficient methods such as LoRA, preference optimization (e.g., DPO), reinforcement learning methods such as GRPO with verifiable rewards, evaluation sets, and rollback criteria.
- Instrument agent runs with tracing and continuous evaluation, making decisions and actions auditable and detecting quality regressions early.
- Deliver production-ready services with automated tests and CI/CD, and expose APIs where other teams or systems need them.
- Collaborate with domain experts, product, and engineering. Present clear design docs and trade-offs.
Leverage AI coding agents such as Claude Code or Codex effectively and responsibly: plan and direct their work, review and test what they produce, and stay accountable for every change that ships. *
Qualifications:
Required:
- Bachelor’s degree or higher in Computer Science, Engineering, Physics, Mathematics, Geophysics, or a related field.
- Strong academic performance, including a minimum cumulative GPA of 3.5 on a 4.0 scale for all earned degrees. Equivalent academic grading systems will be considered for degrees obtained outside the U.S.
- Strong programming and software engineering fundamentals, including asynchronous and concurrent programming.
- Sound design and product judgment: you can explain why a system is built the way it is, what was left out, and how its quality is measured.
- Hands-on experience building multi-agent or graph-based LLM workflows, using established frameworks or custom implementations.
- Experience designing how agents find information: tool-based search over files, logs, and databases, and semantic retrieval with reranking when needed.
- Practical LLM fine-tuning experience: parameter-efficient methods such as LoRA, dataset preparation and quality assurance, offline and online evals; a working understanding of RL-based fine-tuning and reward design.
- Experience with tracing and evaluation of LLM applications.
- Experience building web services and APIs (e.g., FastAPI), and working with PostgreSQL, Docker, and Git.
- Ability to harness AI coding agents such as Claude Code or Codex: understand how they work, break down tasks, guide their execution, critically review their output, recognize failures, and take ownership of the resulting code.
Preferred:
- Experience designing complex agent systems, including task decomposition, parallel execution, result aggregation, and human-in-the-loop orchestration.
- Hands-on experience with LLM post-training techniques (e.g., DPO, GRPO), including reward design for verifiable outcomes and multi-step tool use.
- Experience running enterprise-scale applications in production, including retries, idempotency, timeouts, and rate limits.
- Prior work in workflow automation; exposure to geoscience or scientific computing is a plus.
- Frontend experience building lightweight operator dashboards.
Houston Benefits:
- 401k Retirement Plan - when you contribute up to 5%, not only will we match that, but we will also add an additional 2%
- Free on-site fitness centre offering group exercise classes
- Short-term disability and long-term disability coverage at no cost to you
- Parental Leave Program allowing mothers and fathers to take time off from work for childbirth, adoption and to bond with your new child
- Pet Insurance – options to meet every budget
Wellness Program – resources provided to support your personal wellness and participation in activities can earn you $65 quarterly. *
Our Hiring Process
At Viridien, we are committed to delivering a respectful, inclusive, and transparent recruitment experience.
Due to the high volume of applications we receive, we may not be able to provide individual feedback to every applicant. Only candidates whose qualifications closely match the role criteria will be contacted for an interview. We do, however, aim to share personalized feedback with those who progress to the first round of interviews and beyond.
We are also dedicated to ensuring that our hiring process accessible to all. If you require any reasonable adjustments to fully participate in the application or interview stages, please don’t hesitate to contact your recruiter directly.
We see things differently. Diversity fuels our innovation, we value the unique ways in which we differ, and we are committed to equal employment opportunities for all professionals.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Viridien, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Viridien AI Hiring
Viridien has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Houston, TX, US, Massy, Île-de-France, France, Crawley, England, UK.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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