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About This Role
Instrument is a digitally native design and technology company built to help brands unlock their full potential. Since 2005, our team of makers, thinkers, and storytellers has partnered with leading brands like Google, Nike, Uber, ŌURA, and Eventbrite to craft digital experiences that create impact and drive results.
Unlike traditional agencies, we don’t just design—we build. Our work lives at the intersection of taste and technology, powered by curiosity, thoughtful curation, and a commitment to delivering the most fitting solution for every brief. We bring this to life across three core offerings: Brand, Marketing, and Product.
As a member of our freelance network, you’ll collaborate with our teams to bring bold ideas to life—whether launching new brands, building digital products, or shaping experiences that move people. We welcome collaborators from all backgrounds and experiences who share our curiosity, creativity, and care for craft.
We believe great work comes from diverse perspectives and shared purpose. If you’re passionate about learning, experimenting, and making work that matters, we’d love to hear from you.
The Project: We are developing a highly complex, stateful, multi\-agent simulation engine driven by generative AI. This is a greenfield project exploring new forms of non\-linear, narrative\-based user interaction at scale. Unlike typical stateless web apps, this system requires persistent "memory" for thousands of unique, evolving user sessions. We are building an architecture where multiple AI agents orchestrate complex logic, maintain long\-term context, and react to user inputs in real\-time.
The Role: You will serve as the Sr. AI Engineer, focusing on the core intelligence and agentic logic that drives the application. You will be responsible for designing the multi\-agent architecture that manages our core interactive loops, dynamic scenario generation, and global system state aggregation. You will be the team’s primary authority on how we talk to large language models, ensuring that our agents are fast, reliable, and strictly scoped to their specific domains to minimize latency and hallucination.
This is a part\-time contract role (20 hrs/week) from 4/13–5/29, with a strong likelihood of extension through October at full\-time hours.
What You'll Do
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- + Agent Design \& Orchestration: Build and manage the logic for complex multi\-agent workflows. You will design the systems that handle user onboarding (profile generation), dynamic scenario creation, and real\-time interactive simulation loops.
+ Context Engineering: Architect state management for the LLMs to prevent "context rot" and hallucination. You will strictly govern what each agent knows, structuring context dynamically to maximize token caching and minimize latency.
+ Advanced Prompting \& Evals Infrastructure: Write, test, and version\-control robust system instructions for standalone LLMs and multi\-agent workflows. You will design, implement, and own a rigorous evaluations (evals) framework to programmatically score both individual prompt performance and end\-to\-end agent lifecycles. You will establish the CI/CD\-style testing loops required to iterate on model behavior predictably and safely at scale.
+ Moderation and Security Risk Mitigation: Design and implement pipelines in collaboration with our backend team that moderate harmful or offensive user inputs while also mitigating prompt injection attacks and undesired LLM outputs.
+ Full\-Stack Integration: Work closely with backend and frontend teams to seamlessly integrate AI outputs into the user interface, ensuring smooth data flow from the models down to the client.
What You'll Bring
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- + Core Engineering Foundation: Strong traditional programming background. You must understand software architecture and be capable of writing production\-grade code. You cannot rely solely on AI coding assistants or vibe coding.
+ Generative AI Experience: 1–3 years of deep, hands\-on experience building and deploying LLM\-backed applications in production.
+ Language Proficiency: Strong proficiency in Python. Strong proficiency in TypeScript and familiarity with modern reactive frontend frameworks (preferably Angular v21\).
+ Agent Frameworks: Hands\-on experience with modern agent harnesses (e.g., LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK, Google ADK). Strong preference for candidates with experience using ADK*.*
+ Context \& Latency Optimization: Deep understanding of how LLMs process information. You must have proven experience optimizing token usage, leveraging caching, prompt and context engineering, and designing systems that fetch only the exact context an agent needs at any given moment.
+ Risk Mitigation: Hands\-on experience with designing and employing guardrails for agents’ actions and outputs while also mitigating prompt injection attacks.
Ideally You Are
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- + A Pioneer: You thrive in an emerging tech landscape where best practices are still being written, and you are excited to help define them.
+ A Precision Communicator: You understand that a single ambiguous word in a system prompt can derail an entire multi\-agent workflow.
+ Latency\-Obsessed: You don't just care that the model gets the right answer; you care about how many milliseconds it took to generate it, and you actively design to reduce that overhead, especially when combined with content moderation and prompt injection mitigation strategies.
Core Tech Skills
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- + Android
+ Augmented Reality / Virtual Reality (AR/VR)
+ AWS
+ Back\-end
+ Creative Technologist
+ Database
+ Dev\-Ops
+ Django
+ E\-Commerce
+ Front\-end
+ Full\-stack \*
+ GCP \*
+ iOS
+ Java
+ Javascript \*
+ Leader
+ Marketing
+ Media
+ Mobile
+ Node
+ Objective\-C
+ Product Development
+ Product Manager
+ Prototyping \*
+ Python \*
+ QA
+ React
+ Swift
+ Systems Architecture \*
+ Tech Producer
+ Typescript \*
+ Unity
+ UX \*
+ Wagtail
- Additional Hard Skills/Knowledge:
- + Prompt Engineering
+ Context Engineering
+ LLM APIs
+ LLM Agents
+ Agent Orchestration Harness (e.g. OpenAI Agents SDK / Claude Agent SDK / Google ADK / LangGraph / CrewAI)
+ AI Evals
+ AI security, guardrails, and risk management
Pay Range
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- + The expected pay range for this role is $61 \-$78 per hour based on the US 3 pay range for a W\-2 temporary engagement
+ Our company has three regional pay bands that it adheres to depending on your location, we reference them as US 1, US 2, and US 3
+ US 3 is our base pay. Examples of cities in US 3 are Portland, Houston and Miami.
+ US 2 pay is 7\.5% higher than US 3 to meet the market rates. Examples of cities in US 2 are Los Angeles, Chicago and Seattle
+ US 1 pay is 15% higher than US 3 to meet market rates. Examples of cities in US 1 are Brooklyn and San Francisco
+ If you are curious which region you are in, please apply and get connected with our recruiting team!
Salary Context
This $126K-$162K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At instrument, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($144K) sits 13% below the category median. Disclosed range: $126K to $162K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
instrument AI Hiring
instrument has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $162K - $162K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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