Description review
Applied AI Engineer
DNA325 · United States · back to the listing
HR standards
71/100
solid
Title ↔ description
82/100
solid
Reads as
Machine Learning Engineer
100% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
How others title the same work
Large employers
- Applied AI Engineer Automattic
- Machine Learning Engineer, CX Intelligence Coinbase
- AI Engineer - FDE (Forward Deployed Engineer) Databricks
- AI Engineer - FDE (Forward Deployed Engineer) - U.S. Federal Sector Databricks
- Senior AI Engineer – Notebooks Datadog
Startups
- Attendi | Medior Machine Learning Engineer | Amsterdam, Netherlands | ONSITE (hybrid) | €6,000 - €7,000 per month | Full-time (80–100%, ~4–5 days/week) | Visa sponsorship + 30% ruling possible Attendi
- Member of Technical Staff (applied) Anthrogen
- Aptura AI | Full-Time | MTS (Applied AI), MTS (SWE / Product) | London | ONSITE / HYBRID Aptura AI
- Arcforma AI (arcforma.ai) | AI Engineer (Marketing / Construction / Arcforma AI (arcforma.ai)
- Staff AI Engineer - Agent Architecture & Behavior Artisan
What the listing never says
- 18 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
About the Company
Our client is an AI-native product company built to replace how billions of people manage their digital lives — starting with email, notes, and task tools that were never designed to be AI-native. It's backed by multi-million-dollar investment and built remote-first from day one, with a clear target: cut the time it takes users to get everyday things done by roughly 90%. That means solving the problems most AI products avoid — long-running workflows, persistent context, and reliable behavior under real-world, non-deterministic conditions. The team is small and high-talent-density by design, not by necessity, and moves at a pace that matches the scale of what it's building.
About the Role
As an Applied AI Engineer, you'll turn model capability into real product behavior. You'll own problems end-to-end — from shaping model behavior, to building the systems around it, to making sure it holds up in production. This role sits at the intersection of machine learning, systems, and product: making AI actually work for users, not just in demos.
What You'll Work On
• Build and ship AI features end-to-end (model system user experience)
• Design and iterate on prompts, tools, memory, and agent workflows
• Turn raw model outputs into structured, reliable, predictable behavior
• Debug issues across the full stack — model, orchestration, infra, UX
• Optimize for latency, cost, and production reliability
• Develop lightweight evaluation frameworks to measure real-world performance
• Work closely with product and engineering to turn ambiguous problems into working systems
Requirements
• Strong foundation in machine learning and modern neural network architectures
• Hands-on experience training, fine-tuning, or deploying ML models
• Ability to write clean, production-quality code
• Comfort working across abstraction layers — model, infra, product
• Strong problem-solving skills in ambiguous, fast-moving environments
• A bias toward shipping, iteration, and continuous improvement
Tech Stack
Python · PyTorch / JAX · LLMs (OpenAI-style APIs, LLaMA, Qwen, etc.) · inference/serving (e.g. vLLM) · vector DB
What Success Looks Like
• ML models in production meet accuracy, latency, and reliability targets
• Production issues are found quickly, debugged effectively, and root-caused
• Data pipelines, training loops, and inference systems stay robust, reproducible, maintainable
• Effective collaboration with engineers, product, and research to ship reliable ML-powered features
• Iteration is driven by real-world signals and measurable improvement
What to Expect
The best products in the world are built by small, world-class teams. Decisions are made collectively, at rapid speed — balancing high-quality shipping with fast learning. You'll be expected to bring structure, exercise judgment, and execute independently.
Compensation and benefits are competitive and vary by location; the package includes base salary and equity, discussed openly with you as part of the process.
If there's a fit, expect 3–4 interviews total, followed by a prompt, transparent decision.
Originally posted on Himalayas
Our client is an AI-native product company built to replace how billions of people manage their digital lives — starting with email, notes, and task tools that were never designed to be AI-native. It's backed by multi-million-dollar investment and built remote-first from day one, with a clear target: cut the time it takes users to get everyday things done by roughly 90%. That means solving the problems most AI products avoid — long-running workflows, persistent context, and reliable behavior under real-world, non-deterministic conditions. The team is small and high-talent-density by design, not by necessity, and moves at a pace that matches the scale of what it's building.
About the Role
As an Applied AI Engineer, you'll turn model capability into real product behavior. You'll own problems end-to-end — from shaping model behavior, to building the systems around it, to making sure it holds up in production. This role sits at the intersection of machine learning, systems, and product: making AI actually work for users, not just in demos.
What You'll Work On
• Build and ship AI features end-to-end (model system user experience)
• Design and iterate on prompts, tools, memory, and agent workflows
• Turn raw model outputs into structured, reliable, predictable behavior
• Debug issues across the full stack — model, orchestration, infra, UX
• Optimize for latency, cost, and production reliability
• Develop lightweight evaluation frameworks to measure real-world performance
• Work closely with product and engineering to turn ambiguous problems into working systems
Requirements
• Strong foundation in machine learning and modern neural network architectures
• Hands-on experience training, fine-tuning, or deploying ML models
• Ability to write clean, production-quality code
• Comfort working across abstraction layers — model, infra, product
• Strong problem-solving skills in ambiguous, fast-moving environments
• A bias toward shipping, iteration, and continuous improvement
Tech Stack
Python · PyTorch / JAX · LLMs (OpenAI-style APIs, LLaMA, Qwen, etc.) · inference/serving (e.g. vLLM) · vector DB
What Success Looks Like
• ML models in production meet accuracy, latency, and reliability targets
• Production issues are found quickly, debugged effectively, and root-caused
• Data pipelines, training loops, and inference systems stay robust, reproducible, maintainable
• Effective collaboration with engineers, product, and research to ship reliable ML-powered features
• Iteration is driven by real-world signals and measurable improvement
What to Expect
The best products in the world are built by small, world-class teams. Decisions are made collectively, at rapid speed — balancing high-quality shipping with fast learning. You'll be expected to bring structure, exercise judgment, and execute independently.
Compensation and benefits are competitive and vary by location; the package includes base salary and equity, discussed openly with you as part of the process.
If there's a fit, expect 3–4 interviews total, followed by a prompt, transparent decision.
Originally posted on Himalayas