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About the Role

As our Senior Machine Learning Engineer, you will own the intelligence layer of Thalo’s platform, transforming hundreds of gigabytes of unique HVAC sensor data into detection algorithms, physics-based models, and product features. This hands-on, end-to-end role involves building and tuning the issue-detection engine, deploying physics-based, ML, and LLM-powered models, establishing evaluation practices, and collaborating with cross-functional teams to ensure shipped intelligence is accurate, trustworthy, and useful in the field. You will be a senior voice on a small, high-performing team.

Responsibilities

Own, extend, and improve Thalo’s issue-detection engine spanning electrical, refrigerant, and equipment-performance diagnostics.
Research, develop, and implement ML, statistical, and LLM-based models in production using streaming sensor time-series data.
Own the AI-evaluation practice, including building labeled fault sets, defining accuracy metrics, and establishing an eval harness for regression testing.
Translate model outputs into clear, actionable insights and reports for field, CS, and BD teams.
Continuously improve the data pipeline for large-scale ingestion, storage, transformation, and analysis.
Partner closely with hardware, software, and business teams to connect field and customer insights back into the product.
Document work to facilitate team knowledge sharing and development.

Requirements

5+ years building and deploying ML or statistical models on production data, ideally in an early-stage startup.
Strong applied experience with time-series or streaming sensor data, including anomaly detection, forecasting, or signal processing.
Hands-on experience shipping production features on frontier LLMs (e.g., prompt engineering, structured output, tool-use/agents, RAG).
Experience evaluating AI systems: building eval sets, measuring precision/recall, using LLM-as-judge, and guarding against regressions.
Fluency in Python and the modern data stack, with software-engineering skills to ship production-grade code.
A customer instinct: ability to translate model output into plain-English insights for technicians or building operators.
Curiosity about the physical world and a drive to understand the product's underlying principles.
A self-directed, ownership mindset and a habit of documenting and sharing context.

Skills

Machine Learning
Statistical Modeling
LLM-based Models
Time-series Data Analysis
Streaming Sensor Data Analysis
Anomaly Detection
Forecasting
Signal Processing
Prompt Engineering
Structured Output
Tool-use/Agents
Retrieval-Augmented Generation (RAG)
AI System Evaluation
Precision/Recall Metrics
LLM-as-judge
Python
Modern Data Stack
Production-grade Code Development
React/TypeScript
InfluxDB
TimescaleDB
Grafana
AWS Bedrock

Location

Midtown Manhattan office

Work Type

In-person
Collaborative culture

Experience Level

Senior
5+ years building and deploying ML or statistical models

Education Level

M.S. or higher in a quantitative discipline such as math, physics, statistics, or data science (or equivalent applied experience)

Salary/Compensations

$150,000-$180,000

Benefits

Immediate opportunity to make an impact fighting climate change
Mission-driven team
Stocked pantry
Weekly happy hours
Quarterly offsites
National subsidized healthcare plans (medical, dental, vision)
401(k) program
12 weeks paid parental leave
Paid time off
Free mental health and professional coaching appointments through Lyra
High equity emphasis

About the Company

The world is electrifying, and HVAC is at the center of it. Over the next decade, 100 to 200 million new heat pumps and HVAC units will become the backbone of a decarbonized world, but the industry has no way to keep them running well.
The technician workforce has barely grown while the equipment base has multiplied, reactive repairs eat most of a tech's time, and half the installed base gets no real maintenance at all, wasting energy and driving billions in emergency costs.
Thalo is fixing this by building the physical AI layer for HVAC: sensors plus physics-based intelligence that turn static equipment into self-monitoring systems and shift service from guesswork to data.
Every sensor deployed makes the platform smarter and builds a unique dataset on equipment performance.
The team has experience building self-driving cars at Waymo, working on satellite imagery at Google, designing systems for John Deere, developing space missions for NASA, and leading manufacturing design for Boom Supersonic jets.
The company is bringing that rigor to a generational climate challenge.

Equal Opportunity

Thalo Labs is committed to diversity and building an equitable and inclusive environment for people of all backgrounds and experiences.
A diverse team is critical to Thalo's success.
Especially encourage members of traditionally underrepresented communities to apply, including women, people of color, LGBTQ+ people, veterans, and people with disabilities.