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About This Role
Davidson is distinguished in the aerospace and missile defense industry, in part, because of its excellence in hiring exceptional experts, engineers and software developers dedicated to delivering advanced, intelligent technology solutions in defense of our nation.
Davison is seeking an Applied Scientist in Colorado Springs to develop algorithms and computational solutions for complex real-world problems involving optimization, uncertainty, simulation, and sequential decision-making.
This role combines applied mathematics, algorithm development, programming, and experimental analysis. You will work on problems that require translating ambiguous requirements into practical models, evaluating alternative approaches, and adapting methods to fit real-world constraints.
Working closely with the technical lead, you will have the autonomy to investigate problems, challenge assumptions, propose improvements, and implement solutions. Your work will span improving existing applications, developing simulation-based optimization methods, and advancing real-time decision systems that use information as it becomes available.
Job Responsibilities:
- Formulate complex problems using appropriate mathematical and computational models, making and evaluating assumptions where necessary.
- Develop, adapt, and combine algorithms from optimization, probability, statistics, machine learning, and sequential decision-making.
- Identify opportunities to improve decisions and computational efficiency by exploiting available information and information revealed over time.
- Design computational experiments, analyze results, and use evidence to evaluate approaches and guide improvements.
- Implement algorithms and contribute to maintainable software in Python and C++, including improving existing applications, testing, and code quality.
- Develop and optimize discrete-event simulations and contribute to real-time systems for sequential decision-making under uncertainty.
- Collaborate with the technical lead to assess existing approaches, identify limitations, and develop practical alternatives.
Job Requirements:
- Bachelor's degree in mathematics, operations research, statistics, computer science, engineering, or a related field, with 5+ years of relevant experience; or a Master's or PhD in a related field with 3+ years of relevant experience.
- Demonstrated experience applying quantitative, statistical, or algorithmic methods to complex real-world problems.
- Practical programming ability in Python and C++.
- Experience applying relevant methods such as optimization, probability, statistics, simulation, stochastic optimization, reinforcement learning, bandit algorithms, or active learning. Candidates need experience in some relevant areas, not all.
- Ability to critically evaluate assumptions and approaches, reason through ambiguity, and develop and test practical solutions.
- Familiarity with Git and GitHub or comparable version-control tools.
- Ability to communicate technical reasoning, results, and trade-offs clearly.
Desired (not required) competencies:
- Experience with any of the following: sequential decision-making, reinforcement learning, stochastic optimization, bandit algorithms, or active learning.
- Experience with simulation-based optimization.
- Familiarity with supervised and unsupervised learning.
- Experience with scientific computing, simulation, or computational experimentation.
- Experience improving software from prototype to maintainable application.
- Familiarity with automated testing or CI/CD practices.
- Graduate-level study or research in a relevant quantitative or computational discipline.
Relevant experience may come from professional work, research, graduate projects, or other substantial technical projects. Candidates are not expected to have expertise in every listed method.
Clearance:
Ability to obtain a DoD secret clearance
The Opportunity
This is a self-directed technical role for someone who enjoys applying their knowledge to interesting, open-ended problems. You will have room to explore approaches, question assumptions, and implement improvements while collaborating with the technical lead on problem formulation and overall direction.
The emphasis is on sound technical reasoning, practical algorithm development, and evidence-based problem-solving-not on any single academic discipline, algorithm, or software framework.
Salary Context
This $120K-$143K range is in the lower quartile for Research Scientist roles in our dataset (median: $196K across 100 roles with salary data).
Role Details
About This Role
Research Scientists push the boundaries of what AI can do. They design experiments, develop novel architectures, publish papers, and translate research breakthroughs into production capabilities. This is where the fundamental advances happen, from attention mechanisms to diffusion models to reasoning chains.
The work is intellectually demanding and often ambiguous. You might spend months on an approach that doesn't pan out. The best research scientists combine deep mathematical intuition with engineering pragmatism. They know when to go deep on theory and when to run experiments. They read papers voraciously and can spot incremental contributions from genuine breakthroughs.
Across the 11,520 AI roles we're tracking, Research Scientist positions make up 2% of the market. At Davidson Technologies, Inc., this role fits into their broader AI and engineering organization.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
What the Work Looks Like
A typical week includes: reading and discussing recent papers with your team, designing and running experiments on multi-GPU clusters, analyzing results and iterating on hypotheses, writing up findings for internal review or publication, and collaborating with engineering teams to productionize promising results. The ratio of thinking to coding is higher than in engineering roles.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
Skills Required
PhD strongly preferred for most roles. Deep expertise in a specific area (NLP, computer vision, reinforcement learning, multimodal) is expected. PyTorch is the standard. Publication track record matters. Strong mathematical foundations in linear algebra, probability, optimization, and information theory are assumed.
Beyond the fundamentals, companies value experience with large-scale distributed training, novel architecture design, and the ability to bridge theory and practice. Understanding of current frontier topics (reasoning, multimodal, long-context, alignment) is essential. Code quality matters more than many researchers expect. Labs want researchers who can implement their ideas cleanly.
Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
Compensation Benchmarks
Research Scientist roles pay a median of $223,400 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($131K) sits 41% below the category median. Disclosed range: $120K to $143K.
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.
Davidson Technologies, Inc. AI Hiring
Davidson Technologies, Inc. has 1 open AI role right now. They're hiring across Research Scientist. Based in Colorado Springs, CO, US. Compensation range: $143K - $143K.
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 Scientist roles include PhD Student, Research Engineer, Postdoc.
From here, career progression typically leads toward Research Lead, Distinguished Scientist, VP of Research.
The PhD is the entry point for most paths. Choose your advisor and research area carefully since they'll define your first industry position. Publish consistently, contribute to open-source projects in your area, and build relationships at conferences. Industry research offers better compensation and compute resources than academia, but the pressure to show product impact is real.
What to Expect in Interviews
Research interviews are multi-stage: a research talk (present your best paper), technical deep-dives on your methodology, and often a 'research proposal' exercise where you design an experiment to test a hypothesis. Coding rounds test implementation ability alongside theoretical knowledge. Be prepared to implement a paper from scratch and discuss the design choices the authors made. Strong candidates can critique papers constructively and identify gaps in experimental methodology.
When evaluating opportunities: Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
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 Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
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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