This internship offers hands-on experience supporting high fidelity simulation, modelling, and analysis optimization of laser-based quantum enrichment systems to improve isotope selectivity, throughput, reliability, and scalability. Working with guidance from the Quantum Enrichment Simulation Lead and in collaboration with experimental teams, contribute to verification and validation (V&V) efforts and ensuring that simulation outputs are actionable for optical train design, flowfield architecture, separation chamber layout, and process optimization. The role will provide exposure to rarefied gas dynamics, particle/DSMC methods, and laser–matter interaction concepts, and will contribute to verification and validation activities and clear technical documentation. This role is based in Austin, TX / remote and is intended for a current student or recent graduate seeking hands‑on experience in advanced multiphysics simulation in a fast‑paced development environment. - Solid understanding of core fluid mechanics, including compressible flow concepts; exposure to high‑speed or rarefied gas dynamics (e.g., in coursework or projects) is a plus. - Interest in kinetic descriptions of gases (e.g., Boltzmann‑type approaches, DSMC, gas‑kinetic schemes) and willingness to learn how these are applied to rarefied or separation devices. - Exposure to or curiosity about laser–plasma or laser–molecule interaction, internal energy mode excitation, or radiation–matter interaction; prior project or research experience in this area is a plus. - Interest in isotope‑selective laser excitation schemes (e.g., AVLIS, MLIS, condensation repression, multiphoton dissociation, or photoionization); prior familiarity is a plus, not a requirement. - Willingness to learn verification and validation principles for multiphysics simulations, including basic concepts of code and solution verification, validation hierarchies, and uncertainty quantification. - Ability to work collaboratively with experimental teams and to use data to help calibrate and improve models with guidance. - Strong problem‑solving and systems‑thinking mindset, with an interest in using simulations to inform design changes and improve hardware, optical layouts, or process performance. - Clear written and verbal communication skills, including the ability to document modeling work and explain assumptions and limitations to technical audiences. - Experience with Star-CCM+ or other relevant codes - Currently pursuing or recently completed an M.S., or Ph.D. in Mechanical Engineering, Aerospace Engineering, Nuclear Engineering, Applied Physics, or a closely related discipline with a focus on fluid mechanics, high-speed aerodynamics, or related fields. - Prior experience (course projects, research, internships, or personal projects) involving CFD, gas‑kinetic simulations, rarefied flows, or high‑speed compressible flows is strongly preferred but not strictly required. - Experience working with experimental programs (e.g., wind tunnels, lab test rigs, plasma or laser experiments, or separation devices) is a plus. - Demonstrated interest in advanced simulation, laser‑based technologies, or nuclear/isotope separation applications through coursework, clubs, research, or internships is a plus. - Under the guidance of senior team member, support high-fidelity simulation campaigns for laser-based quantum enrichment systems, including continuum and rarefied gas flows, DSMC-based molecular beams, and coupled excitation/ionization physics in the separation region. - Assist with geometry definition, mesh or particle-resolution setup, basic model selection, numerical setup, and post-processing workflows for laser enrichment unit simulations, following established procedures and review. - Help implement and test hybrid continuum–kinetic frameworks (e.g., CFD–DSMC coupling) under guidance, including straightforward domain decomposition and selecting appropriate models across different Knudsen number regimes. - Contribute to the setup and analysis of simplified laser–matter interaction and excitation models (with mentoring), including isotope-selective excitation concepts and basic relaxation/ionization pathways as appropriate to the system. - Work with experimental teams to support targeted tests (nozzle expansions, beamlines, laser interaction zones, optical diagnostics), providing pre-test modelling support and helping compare simulation outputs to post-test data. - Assist in simulation-based sweeps and parametric studies to help identify sensitivities and trends in enrichment factor, efficiency, and process robustness. - Support basic uncertainty quantification, sensitivity analysis, and optimization activities under the supervision of senior engineers, contributing inputs and analyses that strengthen simulation-based decisions. - Prepare clear plots, summaries, and short technical notes that document modelling assumptions, setups, and key findings for internal stakeholders, incorporating feedback from the team. - Collaborate with process engineering and product teams, with support from the simulation lead, to connect simulation outputs to system-level metrics (e.g., separation factor trends, qualitative impacts on capacity or efficiency). - Perform literature reviews and basic benchmarking of relevant CFD, DSMC, and laser-interaction methods, summarizing findings to support ongoing model development.

Role Description Templar is looking for a Research Engineer to work across the post-training and pre-training stacks in a decentralized, community-driven training environment. You will contribute to state-of-the-art post-training pipelines running on real-world decentralized infrastructure, implement and evaluate ideas relevant to scaling large-scale post-training, and help push the frontier of what distributed LLM training can do. This is a research engineering role — you will be both building systems and contributing to the ideas that shape them. The environment is fast-moving, highly technical, and fully remote. What You'll Do - Contribute to the development of decentralized training of large language models - Work across the post-training and pre-training stacks - Implement and evaluate ideas relevant to scaling large-scale post-training on decentralized infrastructure - Contribute to training runs and writing technical reports Qualifications - Strong programming skills with experience training models across multiple devices - Solid foundations in machine learning - Clear written and verbal communication skills - Ability to work independently in a fast-moving, remote environment Requirements - Experience with LLM RL post-training or large-scale pre-training - Publications or research experience in relevant areas such as distributed learning, reinforcement learning from human feedback, or scalable training infrastructure

• Design and implement advanced computer vision and machine learning models tailored to real-world challenges. • Build and optimize pipelines for object detection, classification, and occlusion prediction using frameworks like TensorFlow and PyTorch. • Research and apply cutting-edge techniques such as Convolutional Neural Networks (CNNs), Neural Radiance Fields (NeRF), and 3D reconstruction. • Handle image processing, segmentation, and feature extraction for large datasets. • Collaborate with cross-functional teams to integrate computer vision models into scalable applications. • Continuously improve models through iterative testing and refinement based on user feedback and performance metrics. • Document and present findings, methodologies, and recommendations effectively to team members and stakeholders.

Research Engineer (Focused on Search/IR) You'll own and advance the search and information retrieval systems at the core of Firecrawl — the infrastructure that determines how we find, rank, index, and serve web content at scale. This is a hands-on, full-stack search role where you'll build and operate everything from ingestion pipelines to serving layers. If you've built search indexes at massive scale and care deeply about ranking quality, freshness, and retrieval speed, this is the role. Salary Range: $180,000–$270,000/year (Range shown is for U.S.-based employees. Compensation outside the U.S. is adjusted fairly based on your country's cost of living. You can explore how we calculate this here: https://www.firecrawl.dev/careers/compensation.) Equity Range: Up to 0.15% Location: San Francisco, CA (Preferred) OR Remote (Americas) Job Type: Full-Time Experience: 3+ years building search/IR systems at scale Visa: US Citizenship/Visa required for SF; N/A for Remote About Firecrawl Firecrawl is the easiest way to extract data from the web. Developers use us to reliably convert URLs into LLM-ready markdown or structured data with a single API call. In just a year, we've hit 8 figures in ARR and 90k+ GitHub stars by building the fastest way for developers to get LLM-ready data. We're a small, fast-moving, technical team building essential infrastructure super-intelligence will use to gather data on the web. We ship fast and deep. What You'll Do - Build and operate search indexes at massive scale: Design, build, and maintain the indexing infrastructure that powers Firecrawl's core product. You'll handle billions of documents and care about every millisecond of latency and every byte of storage. - Own the full stack from ingestion to serving: You don't just build one piece — you own the entire pipeline. Ingestion, processing, indexing, ranking, query understanding, and serving. When something breaks at 3am, you know where to look because you built it. - Solve ranking, relevance, and query understanding: Make sure the right content surfaces for the right queries. You'll build and iterate on ranking models, relevance scoring, and query parsing systems that directly impact product quality. - Tackle freshness, dedup, and incremental indexing: The web changes constantly. You'll build systems that keep our index fresh without re-crawling everything, deduplicate content intelligently, and handle incremental updates at scale without rebuilding from scratch. - Run experiments and ship results to production: You design experiments, measure results rigorously, and ship winners to production fast. You don't need someone to tell you what to try next — you have a backlog of ideas and the judgment to prioritize them. - Collaborate with the research team: Work closely with the Head of Research and the RL-focused Research Engineer to connect search/IR improvements with model training and broader product strategy. What We're Looking For Someone who has built search indexes at massive scale. Not a tutorial project — real indexes serving real traffic with real latency requirements. You've dealt with the hard problems: sharding strategies, index compaction, schema evolution, and the operational complexity of keeping billions of documents queryable and fast. Hands-on with ranking, relevance, and query understanding. You've built or meaningfully improved ranking systems. You understand BM25, learned ranking, embedding-based retrieval, and when to use which. You can reason about relevance tradeoffs and you've shipped ranking changes that moved metrics in production. Owns the full stack: ingestion → index → serving. You're not a specialist who only touches one layer. You've built and operated the entire search pipeline — from how documents enter the system to how results get served. You understand the dependencies between layers and you make good architectural decisions because you see the whole picture. Has solved freshness, dedup, and incremental indexing problems. You know that building the initial index is the easy part. Keeping it accurate, fresh, and deduplicated at scale is where the real engineering lives. You've built systems that handle continuous updates without full rebuilds and you've debugged the subtle correctness issues that come with incremental processing. Self-directed experimenter who ships without handholding. You generate your own hypotheses, design your own experiments, and ship your own code. You don't wait for a roadmap or a sprint planning meeting. You see what needs to improve, you try something, you measure it, and you ship it if it works. Backgrounds that tend to do well: Search engineers at companies with large-scale indexes (web search, e-commerce search, document search). IR researchers who've shipped their work to production. Infrastructure engineers who've built and operated real-time indexing pipelines. Engineers from Elasticsearch, Algolia, Vespa, or similar search infrastructure teams. What We're NOT Looking For Search users, not search builders. If your experience is configuring Elasticsearch or tuning Solr queries but you haven't built search infrastructure from scratch, this isn't the right role. We need someone who builds the engine, not someone who tunes the knobs. Researchers who don't ship. If your best search/IR work lives in a paper and you've never deployed a ranking model to production, this isn't it. Every experiment here ends with code running in prod. Engineers who only work on one layer. If you only do indexing, or only do ranking, or only do serving — and you're not interested in owning the full stack — you'll be frustrated here. We need someone who sees the whole pipeline and can work anywhere in it. People who need clean infrastructure to be productive. The systems you'll work on are evolving fast. If you need everything to be perfectly abstracted and well-documented before you can contribute, you'll stall. We need someone who can build and improve infrastructure while shipping on it. A Note On Pace We operate at an absurd level of urgency because the window for what we're building won't stay open forever. If that excites you, keep reading. If it doesn't, no hard feelings — but this role probably isn't for you. Benefits & Perks Available to all employees - Salary that makes sense — $180,000–$270,000/year, based on impact, not tenure - Own a piece — Up to 0.15% equity in what you're helping build - Generous PTO — 15 days mandatory, anything after 24 days, just ask (holidays excluded); take the time you need to recharge - Parental leave — 12 weeks fully paid, for moms and dads - Wellness stipend — $100/month for the gym, therapy, massages, or whatever keeps you human - Learning & Development — Expense up to $1,000/year toward anything that helps you grow professionally - Team offsites — A change of scenery, minus the trust falls - Sabbatical — 3 paid months off after 4 years, do something fun and new Available to US-based full-time employees - Full coverage, no red tape — Medical, dental, and vision (100% for employees, 50% for spouse/kids) — no weird loopholes, just care that works - Life & Disability insurance — Employer-paid short-term disability, long-term disability, and life insurance — coverage for life's curveballs - Supplemental options — Optional accident, critical illness, hospital indemnity, and voluntary life insurance for extra peace of mind - Doctegrity telehealth — Talk to a doctor from your couch - 401(k) plan — Retirement might be a ways off, but future-you will thank you - Pre-tax benefits — Access to FSAs and commuter benefits (US-only) to help your wallet out a bit - Pet insurance — Because fur babies are family too Available to SF-based employees - SF HQ perks — Snacks, drinks, team lunches, intense ping pong, and peak startup energy - E-Bike transportation — A loaner electric bike to get you around the city, on us Interview Process - Application Review — Send us your stuff + a quick note on why this excites you. Show us what you've built — search systems, indexing pipelines, ranking improvements. We care about what you've shipped, not where you went to school. - Technical Deep Dive (~60 min) — Go deep on search/IR systems you've built: architecture decisions, scale challenges, ranking approaches, and production tradeoffs. We'll explore a live problem — how you'd approach a real search/indexing challenge at Firecrawl's scale. We're looking for depth across the full stack, production instincts, and the ability to reason about tradeoffs under constraints. - Founder Chat (~30 min) — Culture, pace, ownership, and how you like to work. Time for your questions too. - Paid Work Trial (1–2 weeks) — Test drive the real thing: tackle a real search/IR problem with production implications. We'll evaluate on technical depth, experimentation rigor, and how fast you ship something meaningful. - Decision — We move fast after the trial. If you've built search systems at scale and you want to work on one of the most interesting web data problems in AI infrastructure — this is your shot. 👉 Apply now and let's build the index. 🔍