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Research Scientist, Foundation Model Training, SeekrGEO

Seekr
Full Timemid
Reston, Virginia, USPosted 6 days ago

Role Overview

Seekr is hiring a mid-level Research Scientist, Foundation Model Training, SeekrGEO. This is a full-time role in Reston. Part of Seekr's Lifecycle hiring, posted 6 days ago. Full responsibilities, required qualifications, and the apply link are listed in the description below.

Salary Context

Salary is not disclosed in this posting. Market median for Mid-level Lifecycle roles is $105k-$140k (based on 203 comparable listings). Many employers share specifics during the interview process or after an initial screen.

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Job description

Seekr’s Mission

Seekr builds trusted AI for mission-critical decisions. Our platform helps organizations build, govern, and deploy secure, explainable AI rooted in their own data across cloud, on-premises, edge, and air-gapped environments. We care deeply about transparency, auditability, and defensibility because high-stakes AI is only useful when people can understand and trust how it behaves.

About The Opportunity

SeekrGEO is Seekr’s geospatial AI product. This role contributes to the foundation model program behind it: pretraining and post-training of large multi-modal models on geospatial data, together with the distributed training systems that make that work possible at scale. The focus is training, but the role supports the full model lifecycle through deployment.

As a Research Scientist you lead the modeling side of that program: architecture choices, data mixture, pretraining and post-training recipes, and the evaluations that tell us whether the model works. You will work alongside Research Engineers on the systems that make these workloads run at scale, and you prototype recipes in code rather than only on paper.

What You’ll Do

  • Own the pretraining and continued pretraining strategy for our multi-modal geospatial foundation models: model architecture choices, data mixture, objective design, and the ablations that justify the choice.
  • Design and run post-training pipelines spanning SFT, preference optimization, RL, and reasoning-focused recipes for multi-modal models.
  • Design evaluations that are meaningful, reproducible, and trusted both by us and by customers. Build the benchmarks and probes that surface real capabilities and real failures.
  • Diagnose training-dynamics problems (loss spikes, gradient pathologies, optimizer instabilities, capacity issues, data mixture drift) and partner with Research Engineers to turn fixes into durable platform improvements.
  • Curate and version training corpora with the rigor required for repeatable runs and meaningful ablations. Decide what data belongs in each mixture and why.
  • Read current literature and translate promising ideas into shipped capabilities. Reproduce a paper’s core claim, stress-test it on our workloads, and decide whether it earns a place in the next training cycle.
  • Support deployed models through their lifecycle: monitor behavior in production, diagnose regressions in the field, and close the loop back into the next training cycle.
  • Partner with the SeekrGEO product team and customer-facing teams to align training objectives with the workflows the model needs to support.
  • Write production-grade training code alongside Research Engineers, staying hands-on through the training and evaluation loop.
  • Use AI coding assistants effectively as part of a modern research workflow while maintaining strong judgment over modeling choices, training code, and evaluation logic.

What We’re Looking For

  • Strong background in modern ML, with deep familiarity with transformer architectures, multi-modal models, and the practical realities of training them at scale.
  • Demonstrated experience taking a large model through pretraining or continued pretraining, not just fine-tuning a frontier checkpoint.
  • Experience designing and running post-training pipelines (SFT, preference optimization, RL) and reasoning clearly about their failure modes.
  • Comfort designing experiments, evaluating ambiguous technical tradeoffs, and building evals that hold up under scrutiny.
  • Ability to move comfortably between research and engineering. You write production-grade training code, not only experiment scripts, and you can collaborate credibly with engineers on scaling decisions.
  • Expert-level PyTorch fluency, with a deep understanding of distributed training strategies (FSDP, tensor / pipeline / sequence parallelism, ZeRO, checkpointing) and how each interacts with optimization dynamics, effective batch size, and model behavior.
  • Strong Python and software engineering fundamentals, with comfort in testing, code review, debugging, and iteration under production pressure.
  • Fluency with AI coding assistants and the modern developer workflows they enable.
  • Clear communication and strong collaboration across technical and non-technical partners.
  • Reside near Austin, TX or Reston, VA and able to work 3 days per week in office.

Preferred Qualifications

  • Master’s or PhD in computer science, machine learning, AI, statistics, electrical engineering, geospatial science, or a related field.
  • Track record of original technical work: publications, patents, open-source contributions, or research that materially shaped shipped systems.
  • Experience with multi-modal pretraining or vision-language alignment.
  • Experience with RL post-training, including familiarity with common failure modes and the recent literature addressing them.
  • Familiarity with reasoning models, long-chain-of-thought training, or related elicitation methods.
  • Background in remote sensing, computer vision for Earth observation, or physics-informed modeling is a strong plus.

Nice to Have

  • Experience with remote sensing modalities (SAR, hyperspectral, multispectral, high-cadence EO) and the modeling quirks of each.
  • Experience training or post-training agentic systems, tool-using models, or planner / executor architectures.
  • Familiarity with government and defense data handling, classification regimes, or air-gapped deployment.
  • Experience with model distillation, quantization, or other techniques for meeting real latency and cost constraints in deployed systems.
  • Open-source contributions to ML research code or geospatial ML libraries.

Why This Role Matters

SeekrGEO is built for customers whose decisions carry weight. The foundation models behind that product are trained in-house, on our own infrastructure, from data we curate. This role sits at the heart of that work.

About the Company:

Seekr is a leader in explainable and trustworthy artificial intelligence designed to power mission-critical decisions in enterprises, government, and regulated industries. SeekrFlow™, our end-to-end AI platform, provides secure, auditable AI solutions tailored to sectors where transparency, accuracy, and compliance are paramount. Available across cloud, on-premises, and edge environments, SeekrFlow reduces bias, strengthens data integrity, and simplifies model oversight so organizations can rely on trusted AI decisions in high-stakes settings that impact society’s most sensitive and vital systems. Trusted by leading enterprises and government agencies, we partner with defense, finance, telecom, and critical infrastructure leaders to enable AI solutions that drive real-world results with unmatched transparency and control. We are a team of strategic thinkers and problem-solvers tackling the toughest challenges facing critical infrastructure and global enterprises through best-in-class AI models and customer deployment. Our team operates with unwavering commitment to our core values and mission:

  • We are driven by outcomes—our customers' success is what we strive for every day.
  • We believe trust is earned, which is why we build explainability and transparency into the entire AI lifecycle.
  • We take our responsibility to deliver secure AI seriously.
  • We believe innovation drives progress—we are building the technologies that power the systems our society depends on.

Company Benefits:

  • Meaningful Mission & Impact - Work with a deeply talented, collaborative team solving some of the toughest AI challenges that matter.
  • Equity Ownership – RSUs that let you share directly in Seekr’s long‑term success and growth.
  • Time Off That Respects Real Life – Unlimited PTO plus 14 paid company holidays to truly recharge.
  • Work Your Way – A flexible hybrid work environment with offices in Reston, VA and Austin, TX.
  • Competitive Total Rewards – A role‑appropriate compensation structure that supports long‑term growth, including base salary, bonuses, or commission plans depending on role.
  • 401(k) with Company Match – Build your future with a retirement plan that includes employer matching.
  • Comprehensive Health & Wellness – Medical, dental, vision, and life insurance coverage starting day one—for you and your family.
  • Parental Leave – Paid parental leave to support employees as they welcome a new child through birth, adoption, or foster placement.

About Seekr

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Seekr

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1 other open role at Seekr on TryApplyNow.

Frequently Asked Questions

How do I apply for the Research Scientist, Foundation Model Training, SeekrGEO position at Seekr?

Use the Apply button above to submit your application directly to Seekr. Most applications take less than 5 minutes if your resume and contact details are ready, and you'll be routed to the employer's official application system to finish.

Where is the Research Scientist, Foundation Model Training, SeekrGEO position at Seekr located?

This position is based in Reston. Seekr has not indicated remote or hybrid options for this role, so candidates should plan for on-site work.

What does a Research Scientist, Foundation Model Training, SeekrGEO at Seekr earn?

Seekr has not disclosed a salary range in this posting. Many employers share specifics later in the interview process; you can also ask during a recruiter screen if compensation transparency is important to you.

When was the Research Scientist, Foundation Model Training, SeekrGEO role at Seekr posted?

This role was posted on July 9, 2026 (6 days ago). It's still listed as actively hiring; we re-confirm openings against the source system multiple times per day and remove closed roles.

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