Lead RF Data Scientist

About Forterra
At Forterra, we are unleashing autonomy at scale to transform the battlefield. Our mission is to build the foundational platforms that enable an intelligent ecosystem to coordinate, adapt, and execute with speed and precision even in the uncertainty and disruption of modern conflict. In an era marked by rapid technological change and evolving threats, we design for flexibility, survivability, and operational dominance.

Forterra delivers weapons, sensors, and battlefield effects through integrated autonomous networks reaching operational areas faster, safer, and without placing human lives at risk. Our systems operate with distributed control, dynamic routing, and real-time responsiveness, enabling sustained advantage across complex mission environments.

About the role

Forterra is building RF statistical-learning techniques for difficult, real-world sensing environments. The work combines signal processing, statistical learning, and carefully designed experiments to recognize and understand signals in conditions where clean textbook data is not enough.

We are hiring a technical research lead who wants to make a visible impact. You will take ambitious ideas from mathematical formulation through working techniques, testing, and operationally relevant evaluation. You will work closely with RF and DSP engineers, platform engineers, and analysts who use the results. The goal is not research for its own sake: it is to create capabilities that are useful, understandable, and dependable when they matter.

This is a hands-on, mid-career applied-research role for someone who enjoys moving between signal processing, statistical learning, numerical methods, and experimental design. You should be excited by hard, imperfect data; eager to learn from failures; and motivated to see your work shape real systems.

What you'll do

• Lead RF technique development for detection, classification, parameter estimation, and understanding complex signal environments. Choose methods that fit the problem rather than treating a neural network as the default answer.
• Define technique inputs, targets, loss functions, uncertainty outputs, and decision rules for real RF collections. Account for sample rate, bandwidth, channel and receiver effects, incomplete labels, interference, and overlapping or confusable signals.
• Build a disciplined data practice: versioned datasets and splits, label quality rules, leakage checks, and reproducible training and evaluation recipes. Make synthetic, simulated, and collected data distinguishable in every result.
• Design synthetic and hybrid data generation that exercises the technique's intended operating envelope, then characterize where synthetic performance does and does not transfer to collected data.
• Run training and ablation studies; diagnose failures as data, labeling, representation, optimization, technique, or evaluation problems; and make the next experiment smaller and more informative.
• Establish meaningful technique qualification: held-out and stress evaluation, calibration, confusion analysis, uncertainty behavior, tail failures, and clear criteria for whether a technique is ready to move forward.
• Own the mapping from learned signal behavior to consistent, structured event and object descriptions, with explicit evidence rules, so that technique outputs can be understood and acted upon.
• Work with runtime engineers to specify technique artifacts, features, preprocessing, numerical tolerances, test vectors, and fallback behavior. Review C++/CUDA implementation changes for fidelity to the technique specification.
• Work directly with analysts and field teams to turn collection observations into prioritized labeling, replay, and evaluation work. Use those findings to improve the next technique or to narrow its approved operating envelope.
• Produce durable technical artifacts: derivations, experiment plans, technique documentation, data and label specifications, evaluation reports, and concise design reviews that distinguish measured evidence from assumptions.
• Travel up to 10% to collection sites and test events, with the ability to commute to Durham (RTP), NC or Arlington, VA as needed.

Minimum Qualifications

• M.S. or Ph.D. in electrical engineering, statistics, applied mathematics, computer science, physics, or a related quantitative discipline, or equivalent experience.
• 3+ years of applied research or product development using measured data, including ownership of experiments from hypothesis through evaluation and release recommendation.
• Strong foundations in statistical learning, estimation, optimization, linear algebra, numerical methods, and experimental design. Able to reason about uncertainty, identifiability, calibration, and failure modes rather than only optimize a leaderboard metric.
• Demonstrated experience building reproducible training and evaluation pipelines, including dataset versioning, data-quality controls, held-out evaluation, and experiment tracking.
• Proficiency in Python and the scientific Python ecosystem (for example NumPy, SciPy, PyTorch or JAX). Able to read C++ and CUDA sufficiently to verify that the deployed numerical behavior matches the intended technique contract.
• Able to author a technique specification, an evaluation plan, and a clear evidence-backed recommendation that another technical reviewer can audit.
• Strong written and verbal communication; able to work directly with engineers, analysts, and operational users without diluting technical rigor.
• Must be a U.S. person eligible to obtain and maintain a U.S. security clearance.

Preferred Qualifications

• RF signal-processing experience with complex IQ, spectrograms, modulation, radar waveform parameters, receiver/channel impairments, and multi-signal interference; or a strong signal-processing background with the ability to learn RF domain specifics quickly.
• Experience with source separation, multi-target tracking, structured prediction, time-frequency analysis, inverse problems, Bayesian methods, or uncertainty-aware deep learning.
• Applied research experience on national-security, intelligence-analysis, autonomy, communications, sensing, or other high-consequence operational problems.
• Experience building GPU-backed techniques or numerical pipelines in PyTorch, JAX, CUDA, or equivalent tooling.
• Human-in-the-loop labeling, active learning, data-centric methods, or evaluation of partially labeled real-world corpora.
• Ontology engineering, knowledge representation, or mapping technique outputs to structured semantic or event systems.
• Experience with technique release governance, model cards, safety cases, reproducible science, or formal verification of numerical software.

US Salary Range
 $175,000—$220,000

The salary range for this role is an estimate and is based on a wide variety of compensation factors. The salary offered to candidates will vary based on a variety of factors including (but not limited to) relevant work experience, education, specialized training, critical expertise, training, and more. Equity in Forterra is included in most of our full-time, high-demand roles and is therefore considered part of Forterra’s overall compensation package. In addition to base salary and equity, Forterra offers competitive benefits for full-time employees including:

• Premium Healthcare Benefits: Three plan options, including an HSA-eligible plan, with Forterra covering 80% of the plan premium for you and your dependents.  
• Basic Life/AD&D, short and long-term disability insurance plans 100% covered by Forterra, plus the option to purchase additional life insurance for you and your dependents.
• Extremely generous company holiday calendar including a winter break in December.
• Competitive paid time off (PTO) offering 20 days accrued per year.
• A minimum of 7 weeks fully paid parental leave for birth/adoption. 
• A $9k annual tuition reimbursement or professional development stipend.
• Fully stocked beverage refrigerators with all the Celsius your little heart desires. 
• 401(k) retirement savings plan, including traditional, Roth 401(k), and after-tax deferral with company match up to 4%.

Your recruiter will be able to share more information about our salary and benefits offering during the hiring process.

CLEARANCE ELIGIBILITY - This position may require eligibility to obtain and maintain a U.S. security clearance. For more information about U.S. Security Clearances: click here.   

Forterra is an equal-opportunity employer, providing and promoting equal employment opportunity in all personnel practices. Forterrans are unique, talented individuals who are united through a shared passion to deliver autonomous systems that enable national resilience and a robust supply chain.  

All qualified applicants will receive consideration for employment without regard to any factor, including veteran and disability status, and criminal history, in accordance with applicable local, state, and federal laws.

Forterra is also committed to working with and providing reasonable accommodations to individuals with disabilities. Please let your recruiter know if you need an accommodation at any point during the interview process.

To see Forterra’s candidate data privacy policy, please visit Forterra Legal | Job Applicant Privacy Notice.