Principal Software Engineer - Identity Graph

PhysicsX

ABOUT US PhysicsX is a deep-tech company with roots in numerical physics and Formula One, dedicated to accelerating hardware innovation at the speed of software. We are building an AI-driven simulation software stack for engineering and manufacturing across advanced industries. By enabling high-fidelity, multi-physics simulation through AI inference across the entire engineering lifecycle, PhysicsX unlocks new levels of optimization and automation in design, manufacturing, and operations — empowering engineers to push the boundaries of possibility. Our customers include leading innovators in Aerospace & Defense, Materials, Energy, Semiconductors, and Automotive. NOTE: WE ARE CURRENTLY RECRUITING FOR MULTIPLE POSITIONS, HOWEVER PLEASE ONLY APPLY FOR THE ROLE THAT BEST ALIGNS WITH YOUR SKILLSET AND CAREER GOALS. THE ROLE The Principal ML Infrastructure Engineer will extend and operate the infrastructure that powers our research model training, fine-tuning, and serving pipelines. You will be embedded within our Research function, partnering directly with ML engineers and research scientists to ensure they can train Large Physics Models efficiently and reliably at scale. TEAM CONTEXT In this role, you will be vertically embedded in Research, working daily with: * Research Scientists who determine the model architectures and methods * ML Engineers who implement and develop the models * Simulation Data Engineers who are accountable for upstream data pipelines You will have end-to-end responsibilities over the research infrastructure, with the autonomy to make architectural decisions and the responsibility to keep data flowing reliably. Horizontally, you will be part of an infrastructure engineering group responsible for infrastructure across the company. WHAT YOU WILL DO TRAINING INFRASTRUCTURE * Design and operate distributed training infrastructure for neural operator architectures (Transolver, Point Cloud Transformer, etc.) on our large NVIDIA DGX B200 platform. * Optimize training pipelines for throughput, fault tolerance, and cost efficiency, including checkpointing strategies, gradient accumulation, and multi-node synchronization. * Build and maintain experiment tracking and observability systems that give researchers clear visibility into training runs, hyperparameter sweeps, and model performance. DATA I/O AND PERFORMANCE * Solve data loading bottlenecks for large-scale mesh datasets. * Optimize data pipelines for efficient I/O from cloud storage, including prefetching, caching, and format optimization. * Work with heterogeneous data sources of varying formats and resolutions. MODEL SERVING AND DEPLOYMENT * Build serving infrastructure for pre-trained LPMs, supporting both zero-shot inference and uncertainty quantification (Monte Carlo Dropout). * Design and implement model packaging pipelines for customer deployment. Models must run reliably in customer environments with fine-tuning capabilities. * Ensure reproducibility: any model checkpoint should be deployable with consistent behaviour. PLATFORM AND TOOLING * Improve developer experience for the Research team with fast iteration cycles, reliable CI/CD, clear debugging tools. * Collaborate with the broader Infrastructure team on shared patterns and standards. WHAT YOU BRING TO THE TABLE * Ability to scope and effectively deliver projects, prioritising activity as needed. * Problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly. * Excellent collaboration and communication skills, especially in a research setting. You can translate "the model isn't converging" into infrastructure hypotheses and solutions, and can bridge technical abstractions with implementations. * 5+ years of experience building and operating ML infrastructure at scale: * Deep expertise in distributed training: you've debugged NCCL hangs, optimized collective communication, and know when to use FSDP vs. DDP vs. pipeline parallelism * Strong systems fundamentals: Linux, networking (including domain specific NVLink and InfiniBand), storage I/O, profiling and performance optimization * Production experience with Kubernetes and SLURM for job orchestration on GPU clusters * Proficiency in Python and ML frameworks (PyTorch strongly preferred) * Experience with cloud GPU infrastructure; ideally CoreWeave or similar GPU/HPC-focused clouds IDEALLY * Experience with geometric deep learning or neural operators, ****architectures that operate on meshes, point clouds, or graphs * Background in HPC for simulation engineering, familiarity with how CFD/FEA workflows generate and consume data * Experience building model serving infrastructure with latency and throughput requirements * Familiarity with experiment tracking tools (Weights & Biases, MLflow) and observability stacks (Prometheus, Grafana) * Experience packaging models for deployment into customer environments (containers, model registries, versioning)   What we offer Build what actually matters Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind. Learn alongside exceptional people Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home. Influence over hierarchy We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected. Sustainable pace, long-term ambition Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our Shoreditch office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person. And it doesn’t stop there … 🚀 Equity options - share meaningfully in the company you’re helping to build. 🏦 10% employer pension contribution - because investing in future matters. 🍽️ Free office lunches - to keep you energised and focused. 👶 Enhanced parental leave - 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most. 🍼 YellowNest nursery scheme - to help working parents manage childcare costs. ☀️ 25 days of Annual Leave (+ Public Holidays) - because taking time to rest matters. 🏥 Private medical insurance - 100% employee cover, giving you complete peace of mind. 💪 Wellhub Subscription - gain access to thousands of gyms, classes and wellness apps, supporting both physical and mental wellbeing. 👀 Eye tests - because good work depends on good health. 📈 Personal development - dedicated support for learning, development, and leveling up over time. 💛 Employee Assistance Programme (EAP) - confidential wellbeing support, available whenever you need it. 🚲 Bike2Work scheme and 🚆 Season ticket loan - to make getting to work easier and greener. 🚗 Octopus EV salary sacrifice - for a simpler, more sustainable way to drive electric. 🔎 Watch this space, we’re continuing to build this as we grow…   We value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics.    We collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with UK employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.

Kallikor

At Kallikor, we're building the future of supply chain intelligence through AI-powered simulation digital twins. We create living digital representations of real-world operations (warehouses, distribution networks, global logistics) that help organisations make better decisions faster. We're at an inflection point: moving from AI-assisted tools to domain-specific AI that understands supply chains as deeply as our best engineers do. You'll be instrumental in building our first domain-specific language model (DSLM) and the foundation for Project Genome, an ambitious initiative to capture and synthesise the world's supply chain knowledge into actionable intelligence. This is a production engineering role first. You'll build robust Python systems that happen to train and serve LLMs, not the other way around. We need someone who writes production-quality code, debugs complex distributed systems, and thinks about reliability, who has learned ML/LLMs as powerful tools in their engineering arsenal. You'll work across our entire AI stack: building FastAPI services that serve models, creating training pipelines that process production data, deploying inference endpoints with proper monitoring, and integrating all of this into our existing Python backend. The ML is important, but the engineering discipline is what makes it production-ready. Learn more at kallikor.ai. YOUR OPPORTUNITY * Build production AI systems: Design and implement the full stack, from FastAPI endpoints that handle requests, to training pipelines that process data, to inference services that serve predictions. You'll own the architecture, not just the model weights. * Train and deploy our DSLM: Fine-tune models using Unsloth/Axolotl, but more importantly, build the robust infrastructure around it - data pipelines that feed training, evaluation frameworks that catch regressions, deployment systems that handle failover. Make it production-grade. * Integrate ML into our backend: We use FastAPI, PydanticAI, FastMCP, Memgraph. You'll extend these systems with ML capabilities, not as a separate "ML service" but as a natural part of our backend architecture. Clean abstractions, proper error handling, observability. * Own inference performance: Get models running fast, whether that's vLLM deployment, quantization strategies, batching optimizations, or caching. Hit our <200ms latency targets through engineering, not just throwing bigger GPUs at it. * Shape Project Genome's foundation: Work with our Principal Engineer to architect how we ingest, process, and learn from global supply chain data. This is systems design as much as ML with data pipelines, graph databases, incremental learning strategies being just as important. * Mentor through code review and pairing: Raise the bar on code quality, testing, and production practices across the team. Teach mid and junior engineers how to build ML systems that don't fall over. WHY YOU'RE MADE FOR THIS * You're a strong production Python engineer: You write clean, maintainable, tested code. You understand async/await, know when to use generators vs lists, can profile performance bottlenecks. You've built FastAPI services (or similar) that handle production traffic. Your code passes review without drama. * You've built with LLMs in production: You've integrated GPT-4/Claude into real applications, handled streaming responses, dealt with rate limits and retries, cached intelligently. You know the practical challenges: prompt engineering, context management, error handling, cost control. * You've trained or fine-tuned models: Whether it's fine-tuning LLMs, training classifiers, or running experiments, you understand the workflow. You've dealt with training data quality, evaluation metrics, and overfitting. You can debug why a model isn't learning what you expected. * You think like a systems engineer: You design for failure, add instrumentation, consider edge cases. You know that "the model works on my laptop" isn't shipping. You care about monitoring, logging, alerting, and graceful degradation. * You can navigate the ML landscape pragmatically: You know enough about transformers, attention mechanisms, and training dynamics to make informed decisions. But you're not precious about it. If a simple heuristic beats a complex model, you ship the heuristic. * You balance velocity with quality: You ship incrementally and iterate based on production data. But you don't accumulate tech debt, you refactor proactively, write tests that matter, and leave the codebase better than you found it. * You communicate trade-offs clearly: You can explain to the team why we're choosing LoRA over full fine-tuning, why we're deploying on Fireworks instead of self-hosting, or why a 7B model might beat a 70B model. You help everyone make informed decisions. WHAT WE'RE LOOKING FOR SPECIFICALLY Must have: * 5+ years building production Python systems (backend services, APIs, data processing) * Strong software engineering fundamentals: design patterns, testing, debugging, profiling * Experience integrating LLMs into applications (OpenAI/Anthropic APIs, prompt engineering, streaming, PydanticAI) * Understanding of ML training workflows (even if you're not an expert. You need to know enough to build the infrastructure) * Docker, CI/CD, production deployment experience * Can read and understand PyTorch code (you don't need to write novel architectures) Nice to have: * Fine-tuning experience (LoRA, full fine-tuning, QLoRA) * Distributed training basics (DeepSpeed, FSDP) * Graph databases (Memgraph, Neo4j) * Supply chain or logistics domain knowledge * Experience with agent frameworks (LangChain, PydanticAI, etc.) WHAT YOU'LL WORK WITH * Backend Stack: Python, FastAPI, PydanticAI, FastMCP, Memgraph, Postgres * ML Stack: PyTorch, Unsloth/Axolotl for training, vLLM for inference, Weights & Biases * Models: Qwen 2.5, Llama 3.1, GPT-4, Claude (for now) * Infrastructure: AWS (flexible), Docker, Kubernetes, GPUs when needed * Team: Principal Engineer (your partner on architecture), Mid Data/ML Engineer (your data pipeline partner), Junior AI Engineer (your mentee) EXAMPLE PROJECTS YOU'LL OWN * Build a FastAPI service that handles streaming LLM responses with correct error handling and retry logic * Create a training pipeline that processes production logs, validates data quality, and triggers fine-tuning runs * Deploy a fine-tuned 7B model with vLLM that beats GPT-4 latency while maintaining quality on our domain * Design the data ingestion architecture for Project Genome, how we process papers, documentation, and operational data at scale * Implement evaluation frameworks that catch model regressions before they reach production About Us Kallikor is determined to foster an environment where people can do their best work and feel like they belong. We believe a healthy culture, strong values and contribution from a diverse range of individuals will help us to achieve success. We do not discriminate based on race, ethnicity, gender, ancestry, national origin, religion, sex, sexual orientation, gender identity, age, disability, veteran status, genetic information, marital status or any other legally protected status.

Tripadvisor

About Tripadvisor  The Tripadvisor Group connects people to experiences worth sharing, and aims to be the world’s most trusted source for travel and experiences. We leverage our brands, technology, and capabilities to connect our global audience with partners through rich content, travel guidance, and two-sided marketplaces for experiences, accommodations, restaurants, and other travel categories. The subsidiaries of Tripadvisor, Inc. (Nasdaq: TRIP), include a portfolio of travel brands and businesses, including Tripadvisor, Viator, and TheFork. The Tripadvisor Experiences Engineering team is distributed across Europe and is responsible for the platform, mobile apps and all their supporting infrastructure. We run the systems that help operators build their businesses and those which enable third parties to utilise our inventory. We provide the tools which help our customer services team provide world class service to travellers and operators internationally. About the role We're looking for a Principal Product Engineer who pairs deep engineering craft with strong product instincts. You'll work across the stack - mobile, web, backend - wherever the highest-leverage problem happens to live that quarter. You don't wait for a perfectly scoped ticket; you partner with product, design, and data to figure out what's worth building, then ship it end-to-end. We expect breadth. We also expect real depth in the mobile app ecosystem - our customer-facing experiences live there. But we're not hiring a mobile only specialist. We're hiring a generalist with mobile chops who can lead an iOS architecture review on Tuesday and untangle a backend latency regression on Wednesday.   What you'll do * Identify, scope, and ship the changes that move business metrics - across mobile, web, services, and data layers * Architect long-lasting systems that hold up under real production conditions: performance, reliability, scalability, offline behavior, consistency * Lead technical design reviews across teams, weighing trade-offs not just in code but in product impact, time-to-ship, and operational cost * Drive operational maturity wherever it's weakest - release management, observability, incident response, performance monitoring - including in the mobile apps * Partner with PMs, designers, and engineering leaders to shape what we build, why, and in what order; you're a peer in those conversations, not a downstream implementer * Set the technical bar for the org by example: write the prototype, prove the pattern, then teach it * Communicate trade-offs clearly to engineers, product partners, and senior stakeholders   What we're looking for * 10+ years of software experience, with significant time still spent hands-on in code - track record of shipping product-impacting work end-to-end, not just owning a layer * Real depth in the mobile app ecosystem (iOS and/or Android, with strong fluency in Swift and/or Kotlin and the surrounding ecosystem - offline sync, push, auth, persistence, networking, REST/GraphQL) - and credible breadth beyond it * Demonstrated breadth: you've worked seriously in at least one of {web frontend, backend services, data/infra, platform tooling} alongside mobile, and can hold your own in code review there * Strong product judgment: you've made calls about what not to build, and can defend them with evidence * Comfort troubleshooting in production across stacks - crash analysis, latency tracing, release-health debugging * Excellent cross-functional collaboration; you make the people around you better   Nice to have * Background in marketplaces, bookings, or other transactional consumer products * Time spent close to data - experimentation, analytics instrumentation, or ML-adjacent work   Our Cultural Pillars: Traveler first We exist to create value for our customer, the traveler. We enable our suppliers and partners to unlock this value. Their collective behaviors and insights are what drives us.  Execution is our edge We act fast, experiment, learn from failure, iterate, and improve the solutions of tomorrow across every aspect of our business. Our execution is agile, data-driven, prioritised, and built to scale. We assume no problem is someone else’s problem and finish what can be done today, knowing tomorrow will bring fresh challenges.  We succeed together The best outcomes are driven by empathic, humble, and diverse subject matter experts working toward shared goals. We collaborate relentlessly, challenge assumptions, give actionable feedback, and set each other up for success through empowered teams with a clear charter. We transparently take ownership of our growth, individually and as a team. We celebrate the quality of our effort, our learnings, and our collective achievements.     We strive to create an accessible and inclusive experience for all candidates. If you need a reasonable accommodation during the application or the recruiting process, please make sure to reach out to your individual recruiter or our team at AccessibleRecruiting@tripadvisor.com. If you have any additional questions about careers at Tripadvisor you can email us at recruitment@tripadvisor.com. We have all the answers!   #LI-SM1 #LI-Remote #LI-Hybrid