AI Engineer, Foundation Model Training, SeekrGEO

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 Engineer you lead the training systems that make ambitious model programs possible: large‑scale distributed training, parallelism strategies, data infrastructure, and the operational rigor that multi‑week runs demand. You will work alongside Research Scientists on modeling and recipe decisions, and you translate ideas from research papers into working code and decide whether they deserve a full training run. What You’ll Do Build and harden training infrastructure on accelerator clusters: data loaders, parallelism strategies, checkpointing, fault tolerance, and the evaluation harness that catches regressions before customers do. Own the parallelism strategy for our training workloads: FSDP, tensor / pipeline / sequence parallelism, ZeRO variants, activation and gradient checkpointing, mixed precision, and the memory and throughput tradeoffs that come with each. Diagnose distributed training failures and turn fixes into reusable platform improvements. Design and operate the data pipeline for large training corpora: sharded formats, streaming loaders, deduplication, mixture tuning, and the versioning discipline that makes runs reproducible. Keep multi‑week training runs healthy through checkpoint management, fault‑tolerant and elastic training, and the operational hygiene needed for long‑horizon runs on shared infrastructure. Do performance work on accelerators: kernel‑level profiling, attention kernel selection and tuning, memory layout optimization, and closing the gap between theoretical and observed throughput. Build the evaluation infrastructure that makes model comparisons trustworthy and reproducible, both during training and after deployment. Support deployed models through their lifecycle: monitor systems behavior in production, diagnose regressions, and close the loop back into the next training cycle. Contribute improvements back to SeekrFlow training so the platform gets stronger with every run. Partner with Research Scientists to pressure‑test ideas: reproduce a paper’s core claim, verify a proposed recipe scales, and turn research prototypes into production runs. Partner with the SeekrGEO product team and customer‑facing teams to align training infrastructure with the workflows the model needs to support. Use AI coding assistants effectively as part of a modern engineering workflow while maintaining strong judgment over training code, systems code, and infrastructure. What We’re Looking For Strong background in modern ML systems, with deep familiarity with transformer architectures, multi‑modal models, and the practical realities of training them at scale. Fluency with PyTorch and the distributed training ecosystem (FSDP, tensor / pipeline / sequence parallelism, ZeRO, checkpointing strategies). Hands‑on experience with at least one large‑scale training framework such as Megatron‑LM, torchtitan, or DeepSpeed. Ability to move comfortably between engineering and research. You can read a paper, reproduce its core idea, and pressure‑test whether it will hold up at scale. Demonstrated experience contributing to a large model run through pretraining or continued pretraining, not just fine‑tuning a frontier checkpoint. Comfort designing experiments and evaluating ambiguous technical tradeoffs. Strong Python and software engineering fundamentals, with comfort in testing, code review, CI/CD, debugging, and performance analysis. 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 Experience operating distributed training at scale across accelerator clusters, with comfort in collective communication and the failure modes specific to large‑scale runs. Hands‑on experience with Megatron‑LM, torchtitan, and other distributed training frameworks. Performance work on accelerators: kernel‑level profiling, mixed precision, activation and gradient checkpointing, attention kernels, memory layout optimization. Experience with AMD ROCm is a strong plus; CUDA / NVIDIA experience translates directly and is welcome. Experience with data infrastructure for large training corpora: sharded formats, deduplication, streaming pipelines, mixture tuning. Experience with checkpoint management, fault‑tolerant and elastic training, and the operational hygiene needed for multi‑week runs. Experience with experiment tracking, model and data versioning, evaluation pipelines, and diagnosing production issues in trained models. Track record of owning ambiguous, long‑horizon technical problems, whether through graduate research, multi‑year infrastructure builds, or sustained open‑source or research programs. Nice to Have Experience with remote sensing data pipelines and the storage or access patterns each modality demands (SAR, hyperspectral, multispectral, high‑cadence EO). Experience with infrastructure for agentic systems or tool‑using models: rollouts, evaluation harnesses, RL loops at scale. Familiarity with government and defense data handling, classification regimes, or air‑gapped deployment. Experience deploying or distilling large models for inference under real latency and cost constraints. Open‑source contributions to training stacks 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. #J-18808-Ljbffr Seekr