London, UK We're building the future of Revenue Management. AI agents that give short-term rental operators and independent hotels more money for less effort. We integrate directly with Booking.com, Airbnb, and PMS platforms via deep private API connections. The role: This is a flexible Hybrid role in London. You'll be the second engineering hire, working directly alongside Martin. Everything is on the table: backend systems, React frontend, Chrome extension, data pipelines, agent orchestration, and prompt engineering. You'll ship full features end-to-end, not tickets. We ship fast using Claude Code and AI-assisted development daily. If you're already running multiple PRs in parallel with AI tools, you'll feel right at home. What we offer: £80K–£90K base salary plus up to 1% equity (DOE). This is a founding role, you'll have real ownership of the product and the company. You'll shape technical direction, product decisions, and company culture from the ground floor. If you want to build something real with a small team that's already winning customers and growing fast, where your code ships to production the same day and your decisions shape the company then let's talk. What we're looking for: Previous startup or founder experience, you've built something from zero and know what that demands. Very strong Python (Django/Celery) and React. Comfortable across the full stack, backend, frontend, infra, data. AI agent development, prompt engineering, or data pipeline experience is a strong plus. You need to be based in London or able to come to our Canary Wharf office 2 days a week. We can't sponsor visas. Stack: Django, Celery, PostgreSQL, React, Chrome Extensions, Claude Code.

Suitland, MD, United States LightFront is seeking a Senior Python & SAS Developer (Data Modernization) to support a long-term Census contract. This role will focus on modernizing legacy federal statistical and survey processing workflows by translating existing logic into scalable Python-based solutions using tools such as pandas, PySpark, and modern data architecture patterns. This is a backend/data modernization role, not a traditional web application role. The developer will support legacy system analysis, backend data processing, database logic optimization, ETL and batch workflows, and cloud-based processing patterns. The work requires someone who is comfortable stepping into established systems, understanding how existing workflows operate, and carefully translating that logic into cleaner, more maintainable solutions without disrupting current operations. This role will be hired initially as a 1099 contractor under LightFront, a joint venture between Illumen Group and Spatial Front, Inc. (SFI), with the potential to convert to full-time employment in the new fiscal year. The role is remote after onboarding, but the selected candidate must report to the Census office in Maryland to retrieve equipment and complete PIV-related onboarding. If the candidate is not local, reimbursable travel will be coordinated after onboarding begins. Clearance / eligibility: Candidates must be U.S. Citizens due to federal contract requirements and must be able to obtain a Public Trust clearance. The clearance process typically takes 6–8 weeks, and candidates must be comfortable with that process and timeline before moving forward. The selected candidate will also need to report to the Census office in Maryland for equipment and PIV pickup after onboarding has started. Travel costs will be reimbursed for non-local candidates. The ideal candidate will have a strong Python background, with at least 4 years of experience in data processing, backend systems, or similar technical environments. This role requires someone who has worked with legacy systems and can translate existing business or data logic into Python-based solutions using tools such as pandas, PySpark, or similar libraries. Candidates should be comfortable working through unclear, messy, or long-standing workflows where the first step is understanding the current logic before rewriting anything. Candidates should also have at least 4 years of database experience, including writing, refactoring, or optimizing stored procedures, backend logic, and complex SQL. Experience with PostgreSQL, PL/pgSQL, Oracle PL/SQL, or similar database environments is important. The role also requires experience with data pipelines, batch processing, ETL workflows, job orchestration, troubleshooting, and performance tuning. AWS experience is also needed, ideally with services such as Lambda, SQS, SNS, IAM, or similar cloud services used in backend or data workflow environments. Linux experience is required, as the role will involve working in Linux-based development and processing environments. Experience with serverless architecture, Docker, Kubernetes, PySpark, R, or hands-on Base SAS production workflows is helpful, but not required. The strongest fit will be someone who has worked with legacy data systems, can translate existing logic rather than only build new features, is comfortable operating in less structured environments, and can balance support for older systems with modern Python, database, and cloud-based workflows.

Remote, NY, USA OPF is hiring a contract engineer to run the data collection phase of an emissions measurement study comparing edge versus cloud LLM inference across multi-modal AI workloads. The methodology is being designed by external advisors with prior published work on AI carbon measurement; the engineer's role is to implement that methodology rigorously, capture clean instrumented data, and hand it off cleanly to OPF's internal team for analysis and modeling. Findings will appear in a co-branded industry publication backed by a defensible methodology — meaning the public artifact is industry-format, but the underlying data has to withstand potential scrutiny. Engagement scope You will design, build, and run a Python-based testing harness that captures inference runtime, energy consumption, and component-level power across three edge devices and one cloud GPU instance, then deliver clean, documented outputs to the OPF analytical team for downstream modeling. The work spans Windows and Linux, three distinct hardware platforms (Intel Core Ultra with NPU, AMD Ryzen AI with XDNA NPU, NVIDIA L4 cloud GPU), multiple deployments (OpenVINO and Lemonade Server for local; vLLM, Triton, and/or FastAPI for cloud as appropriate per task); and two layers of measurement (software-based estimation and on-die telemetry). The engagement begins with a proof-of-concept phase on the AMD Ryzen 7 Pro laptop, focused on a single task (Basic Queries), to build the harness, telemetry, and methodology end-to-end before scaling to the rest of the platforms and tasks. Following this, a cloud GPU instance must be provisioned for cloud data collection and harness validation. Once the harness has been validated on one laptop and in the cloud environment, the approach will be scaled to the remaining laptops and tasks. Proof-of-concept outcome serves as an interim milestone before proceeding with the remainder of the engagement. See https://onepointfive.notion.site/job-spec-al-ml-engineer for the full job description. Required experience Strong Python proficiency, experimental discipline, and reproducibility practices Hands-on hardware telemetry — Intel RAPL, NVIDIA NVML, plus at least one of Intel SoC Watch / VTune, AMD μProf, or HWiNFO64 Working knowledge of at least one in-scope deployment (OpenVINO, Lemonade Server, vLLM, or Triton) Linux and Windows systems experience, including hardware performance counter telemetry Demonstrated reproducibility discipline: pinned environments, version-controlled configs, documented assumptions Strongly preferred Prior MLPerf Power or MLCommons Power working group submissions Published or contributed to AI energy / carbon measurement research or related sustainability topics Experience with NPU-class accelerators (Intel AI Boost, AMD XDNA, or comparable) Experience with GPU benchmarking methodology — DCGM telemetry, clock-pinning for reproducibility, or comparable practices Comfort working alongside external advisors and delivering well-documented handoffs to a downstream analytical team