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An immediate opportunity to make an impact fighting climate change with a mission-driven team.
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An in-person, collaborative culture. In our midtown Manhattan office, we not only have a stocked pantry but we also dedicate time to connect with each other during weekly happy hours and quarterly offsites.
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National subsidized healthcare plans for medical, dental, and vision insurance.
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Additional benefits include a 401(k) program, 12 weeks paid parental leave, and paid time off.
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Free mental health and professional coaching appointments through Lyra .
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At our ground-floor stage, our compensation structure places a strong emphasis on the value of high equity, with an annual compensation ranging from $150,000-$180,000.
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Own, extend, and improve Thalo’s issue-detection engine spanning the electrical, refrigerant, and equipment-performance diagnostics at the core of our product
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Research, develop, and implement ML, statistical, and LLM-based models in production, working directly with first-of-its-kind streaming sensor time-series data
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Own our AI-evaluation practice: build labeled fault sets (from service outcomes, physics-vs-LLM disagreements, and field cross-checks), define accuracy metrics, and stand up an eval harness that regression-tests every prompt change, new detector, and model upgrade before it ships
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Turn model outputs into clear, actionable insights and reports our field, CS, and BD teams can confidently put in front of customers
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Continuously improve the data pipeline for large-scale ingestion, storage, transformation, and analysis so detection runs reliably and cost effectively as we scale
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Partner closely with hardware, software, and business teams to connect field and customer insights back into the product and document your work so the whole team can build on it
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5+ years building and deploying ML or statistical models on production data, ideally in an early-stage startup environment
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M.S. or higher in a quantitative discipline such as math, physics, statistics, or data science (or equivalent applied experience)
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Strong applied experience with time-series or streaming sensor data, including anomaly detection, forecasting, signal processing, or similar
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Hands-on experience shipping production features on frontier LLMs (e.g., prompt engineering, structured output, tool-use/agents, and RAG) with the judgment to know when an LLM is the right tool versus a deterministic rule or a statistical model
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Experience evaluating AI systems: building eval sets, measuring precision/recall, using LLM-as-judge, and guarding against regressions as prompts and models change
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Fluency in Python and the modern data stack, with the software-engineering chops to ship production-grade code (not just notebooks)
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A real customer instinct: the ability to translate a model output into a plain-English insight a technician or building operator will trust and act on
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Curiosity about the physical world and the drive to understand the “why” behind the product, not just how to implement it
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A self-directed, ownership mindset and a habit of documenting and sharing context
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A passion for tackling climate change and promoting sustainability
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HVAC, refrigeration, combustion, building-systems, or energy-domain experience (a strong plus, but something we’re happy to help the right person learn)
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Experience with agentic / tool-use systems, RAG over technical documentation, or LLM vision
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Familiarity with LLM cost/latency optimization (prompt caching, batch inference) and model governance (managing upgrades, monitoring output/score drift, A/B-testing context changes)
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Frontier-class LLM, open source LLM, and/or AWS Bedrock in production
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Full-stack comfort to take a feature to the UI (React/TypeScript); time-series databases (InfluxDB, TimescaleDB) and tools like Grafana; a degree in a quantitative or engineering discipline