Senior AI Engineer
Scout AI is hiring a Senior AI Engineer for a remote role open to applicants in United States. It pays $200k-$400k a year and Level rates it ; you can apply on Scout AI.
Level's read on this job
- On Level's scale, this AI engineer role is Builds AI ●●●●: the job is building AI systems.
- Pay on this posting starts 8% higher than the typical AI engineer posting on Level.
- This is listed as a remote role.
- The posting mentions PyTorch, AI agents and fine tuning among its tools and skills.
- Scout AI has 24 other open roles on Level.
AI in this role
The future of defense will be decided by those who field intelligent machines at scale. At Scout AI, we’re developing Fury, the first robotic foundation model for defense, to give U.S. forces overwhelming, adaptable, and autonomous power across every domain. Fury enables human operators to command fleets of robots through natural language, and empowers those machines to sense, decide, and act together as one. This mission will ask everything of us: urgency, precision, and relentless work.
The Role
We're looking for a Senior or Staff AI Engineer to join the Fury Orchestration Team with a deep passion for developing and deploying multimodal foundation models that power autonomous teams of robots. Your work will directly influence how our system reasons, plans, remembers and coordinates across multiple robots to accomplish long-horizon missions. Depending on your background and interests, you’ll contribute across the stack: model training and evaluation, reinforcement learning, evaluation and simulation, reasoning and memory architectures, multi-agent coordination, and edge inference optimization. Expect to rapidly prototype ideas against rigorous evaluations, test ideas on real robotic mission rollouts, and break new ground as we create the first robotic foundation model for defense.
We’re a startup. You’ll be moving fast, context-switching daily, and helping define the culture and process as we go. You’ll have substantial ownership, influence over technical direction, and the opportunity to help build the future of autonomous defense systems from the ground up.
Responsibilities
Design, train, and evaluate multimodal foundation models that enable multi-robot mission execution
Drive model development decisions including model architectures, data mixtures, curriculum design, and training recipes informed by rigorous experimentation
Curate and generate large-scale training datasets from both synthetic and real-world data engines
Develop memory, communication, planning, and tool-use capabilities for AI agents operating across teams of robots
Be obsessed with evaluation: design benchmarks, metrics, and testing methodologies that enable rapid iteration across the team
Build and improve simulation environments targeted for synthetic data generation, evaluation, and reinforcement learning
Translate foundational research into deployable, real-time perception and decision-making systems
Collaborate across engineering, robotics, and mission teams to integrate AI systems with onboard autonomy stacks
Conduct field trials and mission operations to validate system performance e under real-world constraints
Qualifications
6+ years of hands-on experience building and deploying AI models, including 2+ years working with multimodal models or agentic AI systems
Proficiency in Python, PyTorch, and modern machine learning infrastructure
Experience training, finetuning, and evaluating large-scale deep learning models using distributed compute infrastructure
Demonstrated experience improving model performance through data curation, training strategy, evaluation design, and rigorous experimentation
Solid grasp of modern model training techniques including SFT, DPO, RLVR
Demonstrated ability to take research from prototype to product in fast-moving environments
BS, MS, or PhD in Computer Science, Engineering, Mathematics, Physics, or related technical field, or equivalent practical experience. Advanced graduate research in AI, robotics, or machine learning is a plus
Bonus: Experience building closed-loop simulation, evaluation, or reinforcement learning environments for agentic or physical AI systems
Must be a U.S. Person due to required access to U.S. export controlled information or facilities
Passion about defense of the United States and its allies
May be expected to obtain and hold a U.S. Top Secret security clearance
Why Join Scout
Work on the world’s most important frontier, ensuring U.S. and allied dominance in the age of intelligent machines
Be a core part of a team building the first defense-specific robotic foundation model
Collaborate with some of the top engineers in autonomy, AI, and national security
See your work deployed on real systems
Help define the future of intelligent defense systems
Backed by Draper Associates, Booz Allen Ventures, and other top investors
Benefits
Competitive Cash Compensation
Meaningful equity
Premium medical, dental, and vision plans with $0 paycheck contribution
Competitive PTO and company holiday calendar
Catered lunch daily and fully stocked kitchen
EV charging
Relocation assistance (depending on role eligibility)
How we rate this
Senior AI Engineer at Scout AI rates 99 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in AI engineer jobs at this level
Pay
- This job starts at
- $200k
- Typical AI engineer job
- $185k
This posting starts 8% above the typical AI engineer job. See AI engineer jobs
Questions you could be asked
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- What are the limits of PyTorch that you've run into, and how did you work around them?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: AI agents, Fine-tuning, and PyTorch. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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