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About This Role
Company Overview
Orbis is a technology company that delivers sovereign intelligence and decision capabilities to the United States Government and its allies. We convert complex information and operational systems into decisive outcomes, giving public institutions and critical enterprises the tools to move faster, see clearer, and act first in an increasingly contested world.
We are not a consulting firm and not a systems integrator. Every Orbis team is built around practitioners who have operated at the mission edge, and every engagement is judged on one thing: whether it produces working capability, not slide decks. Solutions forged from experience; that is how we build, and it is how we hire.
Job Summary
Lead applied research in one or more Orbis AI focus areas — agentic AI, knowledge representation, dynamic ontologies, autonomous analysis, and high-performance analytics — and carry the results into the product. You will frame the problem, survey what already exists, build prototypes that test a real hypothesis, and measure honestly enough to act on the answer. The role exists because our advantage comes from turning advanced research into dependable mission capability.
Travel: Up to 20% for conferences, customer engagement, and team collaboration.
Key Responsibilities
- Frame research problems drawn from real product and mission gaps, define what a result must show to be worth productizing, and survey existing work before building.
- Build prototypes that isolate the question being asked, at the fidelity the question actually requires.
- Run rigorous experiments — baselines, ablations, held-out evaluation, and honest error analysis — with methodology another engineer can reproduce.
- Build and maintain the datasets and evaluation harnesses your area depends on, including cases that capture known failure modes.
- Report results clearly, negative results included, and make a defensible recommendation to continue, pivot, or stop.
- Transition validated results into production alongside product engineers, staying involved through the handoff rather than delivering a notebook.
- Contribute to the scientific community through publications, open-source releases, and academic collaborations, and advise Orbis on what in the field is worth adopting.
- Travel up to 20% for conferences, customer engagement, and team collaboration.
Required Qualifications
- 3+ years of applied research or research-engineering experience, or an M.S./Ph.D. in Computer Science, Machine Learning, or a related field with equivalent applied work
- Strong Python engineering skills, producing research code another engineer can read, run, and build on
- Demonstrated experience designing and running experiments that produced an actionable conclusion, with methodology you can defend
- Depth in at least one Orbis AI focus area: agentic AI, knowledge representation, dynamic ontologies, autonomous analysis, or high-performance analytics
- Working knowledge of modern LLM and retrieval methods and their practical limitations
- Clear technical writing that makes a result legible to engineers and non-specialist stakeholders alike
- Willingness to travel up to 20%
Desired Qualifications
- Publication record, open-source contributions, or academic collaborations in a relevant area
- Experience carrying a research result all the way into a production system
- Experience with knowledge graphs, ontology engineering, reasoning systems, or information extraction
- Experience with agent architectures, planning, or multi-agent coordination
- Experience with model training, fine-tuning, or evaluation at scale
- Experience with performance engineering on data-intensive or analytical workloads
- Familiarity with OSINT, intelligence, or investigative research workflows
Physical Requirements
- Prolonged periods of sitting at a desk and working on a computer
- Participation in virtual and in-person meetings
- Ability to attend planned meetings and/or work in classified spaces for extended periods within the specified work regions
- Travel: Up to 20% for conferences, customer engagement, and team collaboration.
Orbis Benefits
Beyond the opportunity to work alongside practitioners who have operated at the mission edge, joining Orbis means real ownership over outcomes that matter; the chance to build technology that gives the United States Government and its allies a genuine decision advantage, not just another dashboard. From competitive retirement matching to a PTO policy that encourages real time off, we're committed to creating an environment where our team can do their best work and still have a life outside of it.
Our comprehensive benefits package is designed to meet the diverse needs of our employees and their families. A full list of benefits is shared with candidates following an initial conversation with our HR team, so you'll have a clear picture of the support and resources available to you as part of the Orbis team.
Orbis Locations
Orbis works in whatever arrangement the mission requires. Depending on the role, that might mean five days a week on a customer site, full-time in one of our offices, a hybrid schedule, or fully remote work; our team is spread across 25 states and four countries to match. We make sure every team member has the tools and access needed to do the work, wherever that work happens.
Travel is common across many Orbis roles, particularly those supporting customer missions in the field.
Orbis is headquartered in McLean, VA, with additional office locations in Taipei, Taiwan, and Canberra, Australia.
Orbis is an Equal Employment Opportunity employer. We are committed to providing a work environment free of discrimination and harassment, and we make employment decisions based on merit,
qualifications, and business need. Orbis does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, protected veteran status, genetic information, or any other status protected by applicable law.
Orbis complies with all applicable equal opportunity and non-discrimination laws in every jurisdiction in which we operate. Reasonable accommodation is available to applicants and employees with disabilities upon request.
Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 11,520 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Orbis, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $252,000 based on 476 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Orbis AI Hiring
Orbis has 4 open AI roles right now. They're hiring across Research Engineer, AI Agent Developer. Based in Washington, DC, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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.
Frequently Asked Questions
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