MLOps/ ML Engineer

About Natural Negative: Natural Negative is a premier, internationally recognized materials science innovator, green technology powerhouse, and deep-tech artificial intelligence trailblazer operating on an absolute mission to protect, optimize, and transform biomaterials engineering for industrial manufacturing at scale. Backed by elite venture investors and supported by active Innovate UK technology grants, Natural Negative designs advanced, production-ready compostable formulations that match or surpass conventional plastics while remaining fully drop-in compatible with existing factory assembly lines. Headquartered out of the Plus X Innovation Hub in Brighton, England, and operating a high-trust, globally distributed research collective, the company bridges the gap between unfinished organic science and practical industrial adoption. Driven by a high-performance, developer-friendly corporate philosophy that values kind candor, systems thinking, and deep intellectual curiosity, the firm equips high-agency AI builders with an uncompromised remote canvas to leverage state-of-the-art cloud nodes, manipulate complex biomaterial data arrays, and deploy robust, production-grade MLOps pipelines globally.

Position Overview

We are seeking a highly analytical, detail-obsessed, and systems-minded MLOps/ ML Engineer to join our core centralized R&D team in a full-time remote capacity open to qualified technical builders resident anywhere within the United Kingdom. In this high-ownership, foundational engineering seat, you will step up to claim true individual operational and strategic accountability over our global production cloud ML infrastructure, working in lockstep alongside our CTO to translate raw material science experiments into automated digital platform capabilities. Shifting completely away from routine manual data collection checklists, basic administrative data entry logging, or simple disconnected file copying, you will run an active deep-tech machine learning, automated telemetry ingestion, and containerized cloud deployment laboratory—collaborating hand-in-hand with full-stack engineers and wet-lab materials scientists. This position requires an engineering or machine learning veteran who architects high-throughput model pipelines fluidly natively using Machine Learning paradigms, develops robust automation scripts and data wrappers cleanly natively leveraging Python Scripting tools, and commands containerized compute nodes confidently under GCP cloud environments.

Key Responsibilities

• Production ML Stack Architecture: Architect, deploy, and manage Natural Negative’s global production machine learning infrastructure and compute clusters within Google Cloud Platform (GCP) cleanly natively utilizing Machine Learning methodologies.
• Automated Lab Data Pipelines: Design, implement, and automate data pipeline streams to ingest, clean, and replicate complex visual and chemical experimental data directly out of the physical labs into training environments.
• Rigorous Model Lifecycle Discipline: Infuse structural software engineering best practices into deep learning models, configuring robust continuous integration and continuous deployment (CI/CD) paths, automated testing, and dataset/checkpoint versioning.
• Foundational Modeling Contribution: Partner closely with the CTO and founding team to design, evaluate, and iterate the next generation of predictive materials science models, defining how systems scale as the business expands.
• Containerized Asset Management: Orchestrate, scale, and safe-keep production-grade software assets natively leveraging Python Scripting runtime packages inside containerized frameworks (such as Docker, Kubernetes, or GCP vertex arrays).
• Cross-Disciplinary System Interlock: Interface directly alongside full-stack product developers to integrate model inference capabilities smoothly into customer-facing software touchpoints.
• Technical Documentation and SOPs: Author and maintain comprehensive, scannable documentation for internal ML pipelines, model runbooks, and cloud configuration files to enable seamless collaborative scaling.

Required Skills & Qualifications

• Demonstrated professional history running advanced machine learning engineering, MLOps orchestration, cloud platform development, deep-tech tool optimization, or research systems consulting.
• Expert-tier capability developing neural network architectures, optimizing loss functions, and tracing tensor shapes natively utilizing Machine Learning workflows (with specific hands-on depth in PyTorch or equivalent deep learning libraries).
• Practical operational familiarity organizing automation layers, writing query scripts, and processing microservice wrappers natively using Python Scripting models.
• Proven career track record building, deploying, and maintaining live production-grade ML/data pipelines within an agile, small-team environment where infrastructure must be built from the ground up.
• A deep systems-oriented mindset, highlighting verified capability to decouple components cleanly, reason in modular code structures, and visualize an entire end-to-end data pipeline.
• Domain Interest Alignment: Genuine, uncompromised curiosity regarding the underlying physical materials science data, organic formulations, and biomaterial engineering problems.
• Outstanding written and scannable technical communication attributes in business-fluent English, ensuring absolute confidence when articulating technical strategy or pipeline bottlenecks before the founders.
• Location Context: Position open to qualified ML engineering craftspeople based permanently and resident within the borders of the United Kingdom to operate under a 100% remote work-from-home layout, with optional access to lab and studio workspaces at the Plus X Innovation Hub in Brighton, England.

Preferred Strategic Indicators (Nice to Have)

• Prior technical operations or backend engineering history applying machine learning inside physical scientific domains, advanced materials research, biomedical labs, or complex chemical manufacturing environments.
• Direct hands-on experience structuring infrastructure-as-code (IaC) architectures, configuring serverless endpoints, or managing GPU/TPU resource allocations inside Google Cloud Platform.

What We Offer

• Vetted Deep-Tech Sector Salaried Blueprint: A highly competitive full-time baseline annual corporate salary package calibrated precisely to evaluate your MLOps craftsmanship and PyTorch expertise, paired with a meaningful early-stage corporate equity ownership stake to ensure direct upside sharing.
• The exceptional professional canvas to claim absolute technical system code ownership over the AI models power-routing global biomaterial optimizations.
• Profound work-from-home remote parameters offering a 100% remote virtual home office layout, complete calendar execution trust, and zero physical geographic commuting friction out of any state or territory in the UK.
• Access to elite personal lifestyle and wellness systems, featuring a structured 28 days of paid annual vacation leave plus full paid coverage for all UK bank holidays, alongside an institutional workplace pension scheme.
• A progressive corporate environment that actively rejects presenteeism and micromanagement, prioritizing family, rest, and dedicated time to think to ensure high-quality software craftsmanship.