Staff Software Engineer (Forward Deployed)
invisibletech
1 year ago
Full-time
On-site
New York - Hybrid; San Francisco Bay Area - Hybrid
$210,000 - $300,000 USD yearly
Engineering
Moneyboard classification: Published range reaches threshold
Reported USD annual base: $210,000–$300,000
Original source: Greenhouse
About Invisible
Invisible is an Applied AI research company closing the gap between frontier models and the enterprise. We provide the platform infrastructure that makes AI work in production at scale: unify fragmented data, map complex processes, orchestrate and evaluate agents, and stand up hyper-personalized workflow applications fast for companies like Grubhub, Alliance Global, and Thomson Reuters. Where models fall short, we build RL environments and data to fix them. The result: a virtuous cycle between enterprise deployment and model training.
Recognized as Best Predictive Analytics Platform of the Year at the 2026 AI Breakthrough Awards, Invisible makes AI work.
About The Role
As a Staff Forward Deployed Engineer (FDE) at Invisible, you'll lead high-impact, AI-powered solutions that reshape how our clients operate their most critical workflows. You won't just build and deploy — you'll drive the strategy, architecture, and execution of end-to-end systems, working directly with clients.
This is a hybrid role: equal parts AI engineer, software builder, and technical consultant. It's perfect for someone who wants to be hands-on with models and close to the impact they generate. The team is increasingly focused on applied AI work — building and deploying LLM-powered and agentic systems in production — so strong ML engineering instincts can be a valuable addition alongside your architecture and leadership experience.
What You’ll Do
Partner with delivery and executive stakeholders to scope, design, and lead implementation of AI-driven solutions, including LLM and agentic systems
Identify transformational opportunities in messy, ambiguous workflows and turn them into repeatable systems
Lead architecture design and trade-off discussions across performance, scalability, cost, and reliability — including for ML/AI-specific systems like RAG pipelines and production-grade AI applications
Own projects from first discovery call through full deployment — including client-facing delivery, internal coordination, and post-launch iteration
Build shared infrastructure, reusable components, and internal playbooks to level-up the team, including MLOps practices for model versioning, monitoring, and lifecycle management
Coach and mentor mid-level engineers and help shape the culture of forward-deployed AI engineering at Invisible
What We Need
8+ years of software engineering experience, including significant time spent building data, ML, or backend systems
Python & ML/LLM Frameworks: Deep proficiency in Python with hands-on experience using Hugging Face, LangChain, OpenAI, Pinecone, and related ecosystems, including LLMs and AI Agents
Deployment & Infrastructure: Skilled in full-stack and API-based deployment patterns, including Docker, FastAPI, Kubernetes, and cloud environments (GCP, AWS, Azure)
Platform Orchestration: Experienced with workflow orchestration libraries, pub/sub systems (Kafka), and schema governance
Data Management: Expertise in data governance and operations, including Unity Catalog and policy management, cluster/job orchestration, data contracts and quality enforcement, Delta/ETL pipelines, and replay processes
ML Operations (for teams with an applied AI/ML focus): Experience with MLOps frameworks and best practices — model versioning, monitoring, and lifecycle management — and designing scalable ML systems such as RAG pipelines
Strong product and system design instincts — you understand business needs and how to translate them into technical architecture
Experience building usable systems from messy data and ambiguous requirements
Excellent communication and client-facing skills; you've led conversations with technical and non-technical stakeholders alike
Proven experience owning projects from scoping through deployment in ambiguous, high-stakes environments
Be willing to be on-call for our customers when situations arise
Travel: Willingness to travel for on-site client engagements as needed
Onsite: Available to work from the office a few days per week, depending on team needs — some NYC-based teams require 3 days per week onsite; your specific requirement will depend on the team you're placed with
What’s In It For You
Invisible is committed to fair and competitive pay, ensuring that compensation reflects both market conditions and the value each team member brings. Our salary structure accounts for regional differences in cost of living while maintaining internal equity.
The annual salary range for this position, depending on level, is: $210,000 - $300,000 USD
Bonus and equity is included in all full-time, non commission offers. Final compensation is determined by a combination of factors, including location, job-related experience, skills, knowledge, internal pay equity, and overall market conditions. Because of this, every offer is unique. Additional details on total compensation and benefits will be discussed during the hiring process.
You can find more information about our geographic pay tiers here. During the interview process, your Invisible Talent Acquisition Partner will confirm which tier applies to your location. For candidates outside the U.S., compensation is adjusted to reflect local market conditions and cost of living.
What It's Like to Work at Invisible:
At Invisible, we’re not just redefining work—we’re reinventing it. We operate at the intersection of advanced AI and human ingenuity, pushing the boundaries of what’s possible to unlock productivity and scale. Ownership is at the core of everything we do. Here, you won’t just execute tasks—you’ll build, innovate, and shape the future alongside world-class clients pushing the boundaries of AI.
We expect bold ideas, relentless drive, and the ability to turn ambiguity into opportunity. The pace is fast, the challenges are big, and the growth is unmatched. We’re not for everyone, and we’re okay with that. If you’re looking for predictable routines, this isn’t the place for you. But if y
Reported USD annual base: $210,000–$300,000
Original source: Greenhouse
About Invisible
Invisible is an Applied AI research company closing the gap between frontier models and the enterprise. We provide the platform infrastructure that makes AI work in production at scale: unify fragmented data, map complex processes, orchestrate and evaluate agents, and stand up hyper-personalized workflow applications fast for companies like Grubhub, Alliance Global, and Thomson Reuters. Where models fall short, we build RL environments and data to fix them. The result: a virtuous cycle between enterprise deployment and model training.
Recognized as Best Predictive Analytics Platform of the Year at the 2026 AI Breakthrough Awards, Invisible makes AI work.
About The Role
As a Staff Forward Deployed Engineer (FDE) at Invisible, you'll lead high-impact, AI-powered solutions that reshape how our clients operate their most critical workflows. You won't just build and deploy — you'll drive the strategy, architecture, and execution of end-to-end systems, working directly with clients.
This is a hybrid role: equal parts AI engineer, software builder, and technical consultant. It's perfect for someone who wants to be hands-on with models and close to the impact they generate. The team is increasingly focused on applied AI work — building and deploying LLM-powered and agentic systems in production — so strong ML engineering instincts can be a valuable addition alongside your architecture and leadership experience.
What You’ll Do
Partner with delivery and executive stakeholders to scope, design, and lead implementation of AI-driven solutions, including LLM and agentic systems
Identify transformational opportunities in messy, ambiguous workflows and turn them into repeatable systems
Lead architecture design and trade-off discussions across performance, scalability, cost, and reliability — including for ML/AI-specific systems like RAG pipelines and production-grade AI applications
Own projects from first discovery call through full deployment — including client-facing delivery, internal coordination, and post-launch iteration
Build shared infrastructure, reusable components, and internal playbooks to level-up the team, including MLOps practices for model versioning, monitoring, and lifecycle management
Coach and mentor mid-level engineers and help shape the culture of forward-deployed AI engineering at Invisible
What We Need
8+ years of software engineering experience, including significant time spent building data, ML, or backend systems
Python & ML/LLM Frameworks: Deep proficiency in Python with hands-on experience using Hugging Face, LangChain, OpenAI, Pinecone, and related ecosystems, including LLMs and AI Agents
Deployment & Infrastructure: Skilled in full-stack and API-based deployment patterns, including Docker, FastAPI, Kubernetes, and cloud environments (GCP, AWS, Azure)
Platform Orchestration: Experienced with workflow orchestration libraries, pub/sub systems (Kafka), and schema governance
Data Management: Expertise in data governance and operations, including Unity Catalog and policy management, cluster/job orchestration, data contracts and quality enforcement, Delta/ETL pipelines, and replay processes
ML Operations (for teams with an applied AI/ML focus): Experience with MLOps frameworks and best practices — model versioning, monitoring, and lifecycle management — and designing scalable ML systems such as RAG pipelines
Strong product and system design instincts — you understand business needs and how to translate them into technical architecture
Experience building usable systems from messy data and ambiguous requirements
Excellent communication and client-facing skills; you've led conversations with technical and non-technical stakeholders alike
Proven experience owning projects from scoping through deployment in ambiguous, high-stakes environments
Be willing to be on-call for our customers when situations arise
Travel: Willingness to travel for on-site client engagements as needed
Onsite: Available to work from the office a few days per week, depending on team needs — some NYC-based teams require 3 days per week onsite; your specific requirement will depend on the team you're placed with
What’s In It For You
Invisible is committed to fair and competitive pay, ensuring that compensation reflects both market conditions and the value each team member brings. Our salary structure accounts for regional differences in cost of living while maintaining internal equity.
The annual salary range for this position, depending on level, is: $210,000 - $300,000 USD
Bonus and equity is included in all full-time, non commission offers. Final compensation is determined by a combination of factors, including location, job-related experience, skills, knowledge, internal pay equity, and overall market conditions. Because of this, every offer is unique. Additional details on total compensation and benefits will be discussed during the hiring process.
You can find more information about our geographic pay tiers here. During the interview process, your Invisible Talent Acquisition Partner will confirm which tier applies to your location. For candidates outside the U.S., compensation is adjusted to reflect local market conditions and cost of living.
What It's Like to Work at Invisible:
At Invisible, we’re not just redefining work—we’re reinventing it. We operate at the intersection of advanced AI and human ingenuity, pushing the boundaries of what’s possible to unlock productivity and scale. Ownership is at the core of everything we do. Here, you won’t just execute tasks—you’ll build, innovate, and shape the future alongside world-class clients pushing the boundaries of AI.
We expect bold ideas, relentless drive, and the ability to turn ambiguity into opportunity. The pace is fast, the challenges are big, and the growth is unmatched. We’re not for everyone, and we’re okay with that. If you’re looking for predictable routines, this isn’t the place for you. But if y