{"id":3928677,"url":"https://alion.io/job/foresight-applied-ai-engineer-2","title":"Applied AI Engineer","company":{"id":2123186,"name":"Foresight","domain":"foresight.works","url":"https://alion.io/company/foresight-works","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":true,"listed_via":null,"ats_vendor":"Workable","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States","San Francisco, United States","Montreal, Canada"],"countries":["US","CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":180000,"max":230000,"currency":"USD","period":"year","gross":null,"usd_annual":230000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"Django","optional":false},{"name":"FastAPI","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Replit","optional":false},{"name":"SQLAlchemy","optional":false},{"name":"VLM","optional":false},{"name":"AWS Step Functions","optional":true},{"name":"SOC 2","optional":true}],"status":"live","first_seen_at":"2026-11-06T00:00:00Z","employer_posted_date":"2026-11-06","last_verified_at":"2026-11-08T23:22:01Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"About Foresight\nForesight's mission is to bring the world's critical infrastructure into service faster by better enabling teams to deliver complex capital projects.\nThe AI buildout runs on physical things: data centers, power, and the supply chains behind them. A single hyperscale campus involves an owner, a general contractor, dozens of trades, long-lead equipment suppliers, lenders and commissioning teams. Each works from its own schedule and its own spreadsheets. When a project slips, the people who could fix it usually find out last. For an AI data center, a month late can mean tens of millions in lost revenue.\nForesight is the control tower for these projects. We connect schedules, field progress and execution data across every party, forecast where delivery is heading, and help teams act before a delay becomes a missed date. On one 15-project portfolio, Foresight found a project 245 days behind, cut reporting overhead by 90%, and helped prevent more than $100M in lost revenue.\nWe work with top data-center owners, developers and general contractors across the world, including Crusoe, Applied Digital, and NTT. We raised a $25M Series A led by Macquarie Capital and are headquartered in New York.\nThe role\nWe're looking for an Applied AI Engineer to help build the core ML engine of the Foresight platform.\nYou will build the systems that enhance the ability for efficient delivery of large scale construction projects. You will architect, train and deploy the models that perform Foresight's risk predictions and AI recommendations, to be used in interfaces that make 100k-activity projects understandable at a glance. This is a hands-on role on a new team, for someone who owns models and backend features from training to deployment and monitoring to build systems that are accurate, scalable and observable.\nAt Foresight, machine learning is an integral part of the platform. You will be using complex, multimodal high stakes data to make predictions that will be used by teams to deliver major projects on time. This could translate to work on:\nRisk prediction: Build, train and deploy models that predict if a project will be at risk and by how much. \nCriticality prediction: Build models that use project plans to identify which of the tasks are the most critical to complete the project on time before the project even starts. \nProject planning: Build models that accept blueprints and 3D Replit models to create project timelines and scaffolding as the starting point for human planners to make project planning a breeze. \nProgress trackers: Build models that take in multi-model data from static cameras or videos from humans/drones on-site, voice notes, etc about a task to predict what percent of the task is complete. \nRisk mitigation: Build, train and deploy models that can predict the best path forward for risk mitigation or timeline amelioration for major projects. \nContinuous improvement: Evaluate models repeatedly and prototype methods and new models to improve the accuracy of current features, inspired by latest research publications. \nWhat you'll do\nOwn features from technical design through release and beyond: measure adoption, learn and keep improving. \nWork alongside Full Stack engineers to help them turn new models into production features customers rely on. \nPartner with product, design and customers to find the problems most worth solving. \nTune performance across the modelling stack, from accuracy to latency and reliability. \nRaise the bar on architecture, testing, observability and how the team ships. \nHelp shape the product and technical roadmap. \nRequirements\nYou might be a fit if you\nHave 3+ years of experience building and shipping Machine Learning models in production, with real ownership of the backend. \nHave 1+ years of experience building and shipping VLM/LLM based features to production. \nAre strong in Python on the backend (FastAPI, Django or similar). \nAre solid at relational data modelling in Postgres: schema design, indexes, query plans, migrations, and an ORM such as SQLAlchemy. \nHave shipped agentic workflows, RAG systems and high-volume data pipelines. \nHave experience with async work: queues, background jobs, retries, idempotency, and what happens when a worker dies halfway. \nAre hands-on with AWS (S3, RDS, a managed queue or event service, containers or serverless). \nWrite tests and debug production issues from logs, metrics and traces, not guesses. \nAre clear in written communication and comfortable making pragmatic trade-offs in a small team that moves fast. \nBonus points\nExperience with workflow orchestration for multi-step jobs (Temporal, Step Functions, Airflow or similar). \nYou've worked on a legacy-to-modern migration, or on products for regulated enterprise or public-sector customers (SOC 2, data residency). \nEarly-stage or founding-team experience. \nNone of these are required. If the core of this role excites you, apply.\nBenefits\nWhy join now\nReal stakes, real customers. Real stakes, real customers. You won't be building for a hypothetical buyer. Owners and contractors are building now, on deadlines measured in revenue per day. \nBlank canvas, solid ground. You get first-engineer level scope on a product that already has paying customers, real data and proven outcomes to tell. \nDefining a category.Agentic software for physical infrastructure is a new category. What you build will become the standard. \nSmall, senior team. Flat, direct, low on process. We debate openly, decide quickly and ship. \nCompensation and location\nTotal Comp: $180,000-230,000, based on experience, level, and location.\n Benefits: Health, Dental, Vision, 401(k), PTO . \n Location: New York, San Francisco, and Montreal are preferred. Occasional travel to may be required.\nForesight is an equal opportunity employer. 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