safi.co
Safi's mission is to make circular economy firms more profitable through the deployment of AI Technology. We do that by developing foundational models, software and data connectors. In this role you will deploy our technology with customers and help them use it to improve their plants. Why Safi? Our customers are industrial recyclers of plastic and metals - manufacturers, processors, smelters. These firms are held back by limited, legacy technology. Our current customers include one of the world's largest recycling plants and a group that processes the entire plastic waste stream of a major developed nation. We have product-market fit and strong customer traction in an under-served market. We want to expand on that traction to manage the entire end to end lifecycle of plants in multiple sectors. We're backed by leading climate-focused VCs, including LowerCarbon Capital, Nosara Capital and Transition Ventures. If our mission resonates with you, we encourage you to apply, even if your experience doesn't match every requirement. Who we're looking for: We're at an early stage with a small team. We need an experienced Staff Software Engineer / Tech Lead to deeply understand these plants and help us automate and improve them. You: Own technical direction. You make the call on how core systems are designed and built, not just on the code in front of you - and you can justify those calls in terms of pace, reliability, and what the business needs next. Raise the bar of strong engineers around you. Not via management, but via shipped work, the architecture you set, and the questions you ask in PRs and design reviews. Have experience working with a high degree of autonomy, quickly and with a large set of competing problems to tackle (and enjoy it!). Have experience taking products from 0 to 1 (and then from 1 to 100!) - and have made the architectural decisions that let a system survive that transition. Are excited about deploying technology at real-world industrial sites. Can take a problem from "I saw something that could be improved at a plant" to a shipped feature in our app deploying at the plant, end-to-end, within a sprint or two. Can work closely with customers - assessing (not just blindly following) what they say, observing their operations and understanding what is the critical thing to impact. Are interested in commercial questions (how much do we charge for this?), deployment (what's the right UI for forklift engineers wearing gloves?) and AI/ML (what can we build that is 10x better than their current ERP process?). Can work shoulder-to-shoulder with our ML team. You don't train models but turn their outputs into something a forklift driver and a procurement manager both want to use. Requirements: You have led multiple user-facing projects end-to-end, and can scope and define projects for others to work on alongside you. You can own system design and architecture for a production product, and have examples of architectural decisions you made and the trade-offs behind them. Deep Experience with the majority of our stack and have examples of filling experience gaps quickly Python Django and DRF React and React Native PostgresQL Google Cloud Experience monitoring reliable systems and recovering from outages You know how to see an alert in production and dig in to deliver a resolution. At least 5 years in an environment where shipping fast is important You are comfortable delivering at pace but with strong monitoring and testing fundamentals so that software is robust and easily fixable Happy to travel to a customer site or our London HQ regularly (i.e. avg. once per month) expenses covered by Safi, of course If remote, timezone +/-2 hrs of London The process: Screening Call (15 mins) Product Challenge (1 hour with our CPO) Working session with our Engineers - Using your own development tools (60 to 90 mins) Technical Challenge - System Design and Architecture (1 hour) Final Chat, Q+A (30 mins) Compensation and Benefits: Competitive salary and share option plan Salary varies based on location and level of experience. For a candidate in London, UK we expect to pay between £140,000 - £160,000, dependent on seniority 26 days annual leave (+ all UK bank holidays) - the bank holidays are flexible, so you can take them whenever it suits you HQ in Spitalfields, East London, UK Personal wellness & development budget of £75 per month Home office kit-out budget of £500 Regular team socials and meals Private health insurance (UK) Salary sacrifice pension scheme (UK) Cycle to work scheme (UK) We welcome applicants from all backgrounds and do not discriminate on the basis of age, disability, gender reassignment, marriage and civil partnership, pregnancy and maternity, race or ethnicity, religion or belief, sex, or sexual orientation. If you require reasonable adjustments at any stage, please let us know.
Safi's mission is to make circular economy firms more profitable through the deployment of AI Technology. We do that by developing foundational models, software and data connectors. In this role you will deploy our technology with customers and help them use it to improve their plants. Why Safi? Our customers are industrial recyclers of plastic and metals - manufacturers, processors, smelters. These firms are held back by limited, legacy technology. Our current customers include one of the world's largest recycling plants and a group that processes the entire plastic waste stream of a major developed nation. We have product-market fit and strong customer traction in an under-served market. We want to expand on that traction to manage the entire end to end lifecycle of plants in multiple sectors. We're backed by leading climate-focused VCs, including LowerCarbon Capital, Nosara Capital and Transition Ventures. If our mission resonates with you, we encourage you to apply, even if your experience doesn't match every requirement. Who we're looking for: We're at an early stage with a small team. We need an experienced Staff Software Engineer / Tech Lead to deeply understand these plants and help us automate and improve them. You: Own technical direction. You make the call on how core systems are designed and built, not just on the code in front of you - and you can justify those calls in terms of pace, reliability, and what the business needs next. Raise the bar of strong engineers around you. Not via management, but via shipped work, the architecture you set, and the questions you ask in PRs and design reviews. Have experience working with a high degree of autonomy, quickly and with a large set of competing problems to tackle (and enjoy it!). Have experience taking products from 0 to 1 (and then from 1 to 100!) - and have made the architectural decisions that let a system survive that transition. Are excited about deploying technology at real-world industrial sites. Can take a problem from "I saw something that could be improved at a plant" to a shipped feature in our app deploying at the plant, end-to-end, within a sprint or two. Can work closely with customers - assessing (not just blindly following) what they say, observing their operations and understanding what is the critical thing to impact. Are interested in commercial questions (how much do we charge for this?), deployment (what's the right UI for forklift engineers wearing gloves?) and AI/ML (what can we build that is 10x better than their current ERP process?). Can work shoulder-to-shoulder with our ML team. You don't train models but turn their outputs into something a forklift driver and a procurement manager both want to use. Requirements: You have led multiple user-facing projects end-to-end, and can scope and define projects for others to work on alongside you. You can own system design and architecture for a production product, and have examples of architectural decisions you made and the trade-offs behind them. Deep Experience with the majority of our stack and have examples of filling experience gaps quickly Python Django and DRF React and React Native PostgresQL Google Cloud Experience monitoring reliable systems and recovering from outages You know how to see an alert in production and dig in to deliver a resolution. At least 5 years in an environment where shipping fast is important You are comfortable delivering at pace but with strong monitoring and testing fundamentals so that software is robust and easily fixable Happy to travel to a customer site or our London HQ regularly (i.e. avg. once per month) expenses covered by Safi, of course If remote, timezone +/-2 hrs of London The process: Screening Call (15 mins) Product Challenge (1 hour with our CPO) Working session with our Engineers - Using your own development tools (60 to 90 mins) Technical Challenge - System Design and Architecture (1 hour) Final Chat, Q+A (30 mins) Compensation and Benefits: Competitive salary and share option plan Salary varies based on location and level of experience. For a candidate in London, UK we expect to pay between £140,000 - £160,000, dependent on seniority 26 days annual leave (+ all UK bank holidays) - the bank holidays are flexible, so you can take them whenever it suits you HQ in Spitalfields, East London, UK Personal wellness & development budget of £75 per month Home office kit-out budget of £500 Regular team socials and meals Private health insurance (UK) Salary sacrifice pension scheme (UK) Cycle to work scheme (UK) We welcome applicants from all backgrounds and do not discriminate on the basis of age, disability, gender reassignment, marriage and civil partnership, pregnancy and maternity, race or ethnicity, religion or belief, sex, or sexual orientation. If you require reasonable adjustments at any stage, please let us know.
safi.co
Location: London (Hybrid), Safi HQ in Spitalfields Salary: £60,000 - £80,000 + equity Travel: Regular site visits across Europe, North America and Latin America Safi's mission is to make circular economy firms more profitable through the deployment of AI technology. We do that by developing foundational AI models, software, and data connectors. Role As an AI Product Manager for Vision Models, you are the critical layer that connects on-the-ground AI performance with our AI/ML engineering. You will: Capture and triage data + feedback from customer deployments Use your judgement and understanding of our customer's industrial processes + priorities, recycled material, and our vision AI capabilities to prepare and prioritise work for our AI/ML engineers Lead the domain discovery required to bring new materials and markets online Own the data pipeline and labelling teams that we use to teach our models You will spend time at recycling sites watching how operators actually assess quality - and translate what you see into precise AI model development, labelling and training sets. You must be comfortable planning with ML engineers, reading model-performance data, and reasoning about trade-offs with people. We think much of this role can be learnt on the job - high energy, commitment, quick thinking and willingness to get your hands dirty (quite literally, we work with waste materials) will go far. This is a junior-mid level role for someone who is excited about deploying AI that impacts real-life industrial plants. You do not need prior experience as a product manager in a tech company. Why Safi? Our customers are industrial recyclers of plastic and metals - manufacturers, processors, smelters. These firms are held back by limited, legacy technology and fragmented data. Within our first year of operations, we have signed customers such as one of the world's largest recycling plants, a top 5 global aluminium smelter and a group that processes the entire plastic waste stream of a major nation. We have product-market fit and strong customer traction in an under-served market. We want to expand on that traction to help manage the entire end-to-end lifecycle of plants in multiple sectors. We're backed by leading climate-focused VCs, including LowerCarbon Capital, Nosara Capital, and Transition Ventures. If our mission resonates with you, we encourage you to apply, even if your experience doesn't match every requirement. What You Will Do Own the ML backlog and prioritisation. Triage incoming data and feedback from customers and colleagues. Assess with sales, deployment and customers to understand importance. Execute the work - training preparation, data collection, labelling - that our ML engineers need to solve the customer problems Review model feedback and spot trends. Regularly review incoming AI-model feedback, identify recurring issues or trends, and feed the significant ones into the ML triage process. Maintain and curate the labelling guidelines (taxonomy). Keep the guidelines accurate and up to date as understanding improves and new edge cases emerge. Decide when to split out a new class and when to fold classes together - decisions that directly determine final model quality. Lead new-material and new-partner model development. Understand deeply with our customers how different objects, contamination and material affects their process. Understand what matters when assessing its quality: what can be judged visually, how accurate detection needs to be, and where the hard cases are. Gather detailed specifications, convey them to the ML team, sanity-check feasibility, create the labelling guidelines, and get annotators onto the work. Run weekly reviews with partners. Act as the interface between the business and the ML team. Run the weekly cadence with ML engineers, deployment, and sales so that priorities, guidelines, and progress stay aligned. What We're Looking For Required ML literacy: you have a basic understanding of how computer vision AI works Data analysis: you have experience analysing data and taking accurate conclusions Great people skills and confidence: You will need to walk onto an industrial site and be able to connect with forklift operators and plant managers, some of whom will not be able to speak the same language as you Information absorption: you will need to be able to quickly absorb information about materials, business needs, sales requirements and make a plan Able to write and maintain detailed documentation for your team. This is the core of taxonomy management. Genuine curiosity about the physical material and how it's handled Resilience: willing to travel extensively to recycling and processing sites in un-glamorous parts of the world Nice To Have Experience in a ML/data product role - ideally working directly with a machine-learning, data-science, or computer-vision team Experience in recycling, waste, materials, manufacturing, or another physical/industrial domain. Working knowledge of Spanish How We Work We're a small team and we all automate our own work - most people here use LLM and agentic tools to streamline triage, reporting, and recurring processes. We use Linear, Notion, Claude, GitHub and GCP. If you have a builder's instinct for making your own job easier, you'll fit in well. We're in our office near Spitalfields / Brick Lane a few days a week for the energy of building together in person. You will also be travelling 1-2 times per month. Compensation + Benefits Salary between £60,000 and £80,000 Share option plan - every employee owns what we're building 26 days annual leave (+ all UK bank holidays) - the bank holidays are flexible, so you can take them whenever it suits you Personal wellness & development budget of £75 per month Home office kit-out budget of £500 Private health insurance (opt in) Business and leisure travel insurance Salary sacrifice pension scheme Cycle to work scheme Safi is an equal opportunity employer. We welcome applicants from all backgrounds and do not discriminate on the basis of race, colour, religion, sex, sexual orientation, gender identity, national origin, age, disability, or any other status protected by applicable law. We're committed to building a diverse team and making our hiring process fair and accessible. Please let us know if you need any reasonable adjustments during the recruitment process.
Location: London (Hybrid), Safi HQ in Spitalfields Salary: £60,000 - £80,000 + equity Travel: Regular site visits across Europe, North America and Latin America Safi's mission is to make circular economy firms more profitable through the deployment of AI technology. We do that by developing foundational AI models, software, and data connectors. Role As an AI Product Manager for Vision Models, you are the critical layer that connects on-the-ground AI performance with our AI/ML engineering. You will: Capture and triage data + feedback from customer deployments Use your judgement and understanding of our customer's industrial processes + priorities, recycled material, and our vision AI capabilities to prepare and prioritise work for our AI/ML engineers Lead the domain discovery required to bring new materials and markets online Own the data pipeline and labelling teams that we use to teach our models You will spend time at recycling sites watching how operators actually assess quality - and translate what you see into precise AI model development, labelling and training sets. You must be comfortable planning with ML engineers, reading model-performance data, and reasoning about trade-offs with people. We think much of this role can be learnt on the job - high energy, commitment, quick thinking and willingness to get your hands dirty (quite literally, we work with waste materials) will go far. This is a junior-mid level role for someone who is excited about deploying AI that impacts real-life industrial plants. You do not need prior experience as a product manager in a tech company. Why Safi? Our customers are industrial recyclers of plastic and metals - manufacturers, processors, smelters. These firms are held back by limited, legacy technology and fragmented data. Within our first year of operations, we have signed customers such as one of the world's largest recycling plants, a top 5 global aluminium smelter and a group that processes the entire plastic waste stream of a major nation. We have product-market fit and strong customer traction in an under-served market. We want to expand on that traction to help manage the entire end-to-end lifecycle of plants in multiple sectors. We're backed by leading climate-focused VCs, including LowerCarbon Capital, Nosara Capital, and Transition Ventures. If our mission resonates with you, we encourage you to apply, even if your experience doesn't match every requirement. What You Will Do Own the ML backlog and prioritisation. Triage incoming data and feedback from customers and colleagues. Assess with sales, deployment and customers to understand importance. Execute the work - training preparation, data collection, labelling - that our ML engineers need to solve the customer problems Review model feedback and spot trends. Regularly review incoming AI-model feedback, identify recurring issues or trends, and feed the significant ones into the ML triage process. Maintain and curate the labelling guidelines (taxonomy). Keep the guidelines accurate and up to date as understanding improves and new edge cases emerge. Decide when to split out a new class and when to fold classes together - decisions that directly determine final model quality. Lead new-material and new-partner model development. Understand deeply with our customers how different objects, contamination and material affects their process. Understand what matters when assessing its quality: what can be judged visually, how accurate detection needs to be, and where the hard cases are. Gather detailed specifications, convey them to the ML team, sanity-check feasibility, create the labelling guidelines, and get annotators onto the work. Run weekly reviews with partners. Act as the interface between the business and the ML team. Run the weekly cadence with ML engineers, deployment, and sales so that priorities, guidelines, and progress stay aligned. What We're Looking For Required ML literacy: you have a basic understanding of how computer vision AI works Data analysis: you have experience analysing data and taking accurate conclusions Great people skills and confidence: You will need to walk onto an industrial site and be able to connect with forklift operators and plant managers, some of whom will not be able to speak the same language as you Information absorption: you will need to be able to quickly absorb information about materials, business needs, sales requirements and make a plan Able to write and maintain detailed documentation for your team. This is the core of taxonomy management. Genuine curiosity about the physical material and how it's handled Resilience: willing to travel extensively to recycling and processing sites in un-glamorous parts of the world Nice To Have Experience in a ML/data product role - ideally working directly with a machine-learning, data-science, or computer-vision team Experience in recycling, waste, materials, manufacturing, or another physical/industrial domain. Working knowledge of Spanish How We Work We're a small team and we all automate our own work - most people here use LLM and agentic tools to streamline triage, reporting, and recurring processes. We use Linear, Notion, Claude, GitHub and GCP. If you have a builder's instinct for making your own job easier, you'll fit in well. We're in our office near Spitalfields / Brick Lane a few days a week for the energy of building together in person. You will also be travelling 1-2 times per month. Compensation + Benefits Salary between £60,000 and £80,000 Share option plan - every employee owns what we're building 26 days annual leave (+ all UK bank holidays) - the bank holidays are flexible, so you can take them whenever it suits you Personal wellness & development budget of £75 per month Home office kit-out budget of £500 Private health insurance (opt in) Business and leisure travel insurance Salary sacrifice pension scheme Cycle to work scheme Safi is an equal opportunity employer. We welcome applicants from all backgrounds and do not discriminate on the basis of race, colour, religion, sex, sexual orientation, gender identity, national origin, age, disability, or any other status protected by applicable law. We're committed to building a diverse team and making our hiring process fair and accessible. Please let us know if you need any reasonable adjustments during the recruitment process.