Research Engineer, Foundation Model Training, SeekrGEO

Seekr • Austin, TX

1d • Hybrid

About The Position

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.

Requirements

• 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.

Nice To Haves

• 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.

Responsibilities

• 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.

Benefits

• Equity Ownership – RSUs that let you share directly in Seekr’s long‑term success and growth.
• Unlimited PTO plus 14 paid company holidays to truly recharge.
• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed