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    "requisitionDescription": "<div><p id=\"isPasted\"><strong>Vision Systems Researcher</strong></p><p><strong>Division:</strong> DATUM, Impac Exploration Services<br><strong>Location:</strong> Oklahoma City (OK), Houston (TX), San Jose (CA)<br><strong>Type:</strong> Full-Time</p><p>We&#39;re building computer vision systems for environments where most cameras fear to tread. While everyone else is teaching AI to recognize cats and faces, we&#39;re teaching it to see patterns in the physical world that determine million-dollar outcomes.</p><p>DATUM needs a computer vision researcher who gets excited about novel imaging challenges. You&#39;ll work with data streams no one&#39;s properly analyzed before, from sensors and imaging systems that weren&#39;t designed to play nicely with neural networks. The patterns you&#39;ll teach our models to recognize have real-world consequences&mdash;this isn&#39;t about optimizing click-through rates.</p><p><strong>What You&#39;ll Build</strong></p><ul><li>Vision systems that extract insights from unconventional imaging modalities</li><li>Real-time analysis pipelines for streaming visual data in challenging conditions</li><li>Integration frameworks for specialized imaging hardware</li><li>Computer vision that works where GPS doesn&#39;t and WiFi won&#39;t</li></ul><p><strong>The Research Reality</strong></p><ul><li id=\"isPasted\">Your models might need to work with imaging modalities that barely exist yet</li><li>You&#39;ll push the boundaries of what&#39;s possible with computational photography</li><li>Sometimes you&#39;ll work with data from sensors originally designed for astronomy or medical imaging</li><li>You might need to extract signal from what others consider noise</li></ul><p><strong>You&#39;re Our Person If</strong></p><ul><li>You&#39;ve pushed computer vision beyond standard benchmarks</li><li>Unconventional imaging excites rather than intimidates you</li><li>You can adapt academic approaches to messy real-world constraints</li><li>You understand when to use classical CV vs. deep learning</li><li>You&#39;ve made models work with limited or unusual training data</li><li>You see physical-world applications as the real challenge</li></ul><p id=\"isPasted\"><strong>Especially If</strong></p><ul><li>You&#39;ve worked with quantum imaging or single-photon detection</li><li>You have experience with interferometric or holographic reconstruction</li><li>You&#39;ve built vision systems for extreme environments (deep sea, space, particle physics)</li><li>You understand computational imaging beyond traditional cameras</li><li>You&#39;ve explored neuromorphic vision sensors or event-based cameras</li><li>You&#39;re comfortable with terahertz imaging or other exotic wavelengths</li></ul><p><strong>Technical Foundations</strong></p><p>We expect fluency in:</p><ul><li>Modern CV frameworks (PyTorch, TensorFlow, or similar)</li><li>Both classical and deep learning approaches</li><li>Real-time optimization and edge deployment</li><li>Working with non-standard image formats and sensor data</li><li>Building robust systems for production environments</li></ul><p><strong>Why This Hits Different</strong></p><ul><li>Your models will analyze things no one&#39;s properly looked at before</li><li>Real impact&mdash;your work directly affects critical operations</li><li>Access to imaging hardware and datasets that don&#39;t exist elsewhere</li><li>Freedom to pursue novel approaches, not just incremental improvements</li><li>Collaboration with researchers pushing boundaries in other domains</li><li>Problems that require genuine innovation, not just applying known solutions</li></ul><p><strong>Growth Trajectory</strong></p><p>Start by solving specific vision challenges. Soon you&#39;ll be defining how entire industries should approach visual analysis. Publish papers on approaches others haven&#39;t imagined. Build IP that reshapes what&#39;s possible with computer vision.</p><p>When Meta or Apple tries to poach you for their AR/VR teams, they&#39;ll be impressed by problems you&#39;ve solved that they haven&#39;t even encountered yet.</p><p><strong>The Reality</strong></p><p>You&#39;ll work with data that&#39;s messy, hardware that&#39;s temperamental, and constraints that academic papers ignore. You&#39;ll need to understand enough about our domain to know what patterns matter. You&#39;ll optimize for inference speed as much as accuracy.</p><p>But you&#39;ll also pioneer computer vision applications that don&#39;t exist anywhere else. You&#39;ll see your models make decisions that matter. You&#39;ll prove that the most interesting vision problems aren&#39;t on Instagram&mdash;they&#39;re in the physical world.</p><p><strong>Ready to See Differently?</strong></p><p>Show us computer vision work that went beyond standard datasets. Tell us about a time you made vision work in a challenging environment. Share your thoughts on where industrial computer vision is heading.</p><p>We&#39;re looking for someone who sees unconventional imaging challenges and thinks &quot;finally, something interesting.&quot;</p></div>\n",
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