Interested in this AI/ML Engineer role at Alchemy Cloud?
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### Why this role exists
Scientists spend a staggering amount of time wrangling data instead of running experiments. We build the AI\-native platform that changes that: the system of record and the intelligence layer for R\&D labs formulating everything from next\-gen batteries to sustainable materials to alternative proteins. Our software doesn't just store lab data, it designs experiments, recommends formulations, surfaces insights, and automates the busywork that stands between a researcher and their next discovery.
This role is the bridge between Sales and Customer Success. You own the moment that makes or breaks every customer relationship: the initial deployment. When a new customer says yes, you're the person who takes them from a signed deal to a live, adopted, referenceable account \- running discovery, designing the solution, and executing it hands\-on. You're the connector that ensures what Sales promised is exactly what the customer gets, and that Customer Success inherits an account that's already succeeding.
### What you'll do
You'll own the full arc of a new deployment, end to end:
- Discovery. Sit shoulder\-to\-shoulder with scientists at some of the most demanding labs in the world. Go deep on their chemistry, their workflows, and their actual problems, asking the first\-principles questions that pin down what success really looks like before a single thing gets built.
- Solution design. Translate the messy reality of a customer's science into a concrete architecture: configurations, data models, integrations, migrations, and the AI agents and skills that will automate the work they used to do by hand.
- Execution. You'll ship real software into customers' hands in days and weeks, not quarters or years (!), standing up a tenant, co\-creating with subject matter experts at the customer, and wiring up agentic workflows using the most capable AI tooling available.
You'll also be a tight feedback loop for the Product team. When you hit a gap, you'll characterize it precisely, weigh a custom config against a real product improvement, and push the platform to absorb the pattern so the next ten customers never feel it, then hand the account to Customer Success already set up to thrive.
### Who you are
This is a rare combination, and that's the point. You bring deep scientific domain expertise and pair it expertly with the latest technology.
- You have real depth in a scientific field \- chemistry, materials science, food science, or an adjacent R\&D discipline \- ideally at the bench. You understand how scientists actually work because you've been one.
- You intimately know the legacy software scientists have been stuck with \- paper lab notebooks, legacy ELN and LIMS, XLS, homegrown software \- and all the weaknesses, gotchas and frustrations that have historically come with them.
- You're genuinely technical and build with the frontier: you've worked hands\-on with LLMs and agentic frameworks \- prompt/skill design, tool use, evals, feedback loops \- and you have opinions about where they shine and where they don't. You generate production\-quality software solutions and are comfortable across the stack.
- You thrive in front of customers. You can run a technical discovery session, pin down real requirements, and make a scientist feel deeply understood.
- You're energized by ambiguity and speed, and would rather ship something real and iterate than wait for the perfect spec.
The magic is in the intersection: someone who can talk experimental design with a materials scientist in the morning and architect an AI agent to automate their workflow in the afternoon.
### Why this is the frontier
This is genuinely as close to the leading edge of the technology industry as you can get. AI is rewriting what software can do, and applying it to real scientific R\&D \- where the stakes are discovery itself \- is one of the most consequential places to be doing that work. You won't be reading about what's coming; you'll be building it, using tooling that didn't exist last year to move at a pace that compounds every week.
Your work is visible and high\-impact from day one. You'll see a customer go from hopeful to happy and genuinely referenceable because of something you designed and built. You'll help define what "forward deployed" even means at a company inventing the category. And you'll be surrounded by people \- Sales, Customer Success, Product and Engineering \- rowing hard in the same direction toward the same goal: making researchers dramatically more productive.
If you want to sit at the intersection of deep science and frontier AI, close to the people your work helps, we'd love to talk.
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 Alchemy Cloud, 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. Mid-level AI roles across all categories have a median of $194,400.
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.
Alchemy Cloud AI Hiring
Alchemy Cloud has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Brazil, IN, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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.
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