Senior AI Engineer, Foundation Model Training, SeekrGEO

AI Engineer, Foundation Model Training, SeekrGEO

Austin, Texas, United States;
Reston, Virginia, United States

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.

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  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.

• 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 Seekr Flow 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.

• 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, torch titan, or Deep Speed.
• 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.

• 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, torch titan, 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…