---
title: 'Machine Learning Engineer at Sunset'
canonical: 'https://feeny.ai/job/machine-learning-engineer-sunset-new-york-4hpwv6ybfsk5'
type: 'job'
last_seen: '2026-09-04'
---

# Machine Learning Engineer at Sunset

- **Company:** Sunset
- **Location:** New York, NY
- **Employment:** full-time
- **Posted:** 2026-08-13
- **Last confirmed live:** 2026-09-04
- **Apply:** https://jobs.ashbyhq.com/sunset/48131ef6-4971-403a-8127-cfdb5446fc07

## Job description

## ABOUT SUNSET

At its core, Sunset was founded to help founders. We started by supporting startups through shutting down, but we have since expanded into unlocking a new revenue stream for all types of businesses.

In 2025, we had a unique insight: the data every company generates each day through collaboration, communication, and building is some of the most valuable training data in the world. Public and synthetic data can only get frontier models so far, so the next generation of model progress depends on real, proprietary data grounded in how actual businesses operate. We are a primary source of it, partnering directly with the frontier AI labs building what comes next.

## WHY JOIN SUNSET NOW

- We have scaled from $0 to a multi-eight-figure run rate in a matter of months
- We have raised from top-tier investors, including Floodgate, Afore, Ludlow, and Hustle Fund
- We are small enough that you will carry outsized responsibility and grow as quickly as the company does
- You will partner with and build for some of the fastest and most important companies in the world
- You will help build a massive, category-defining business from the ground floor

## THE ROLE

Sunset turns sensitive internal enterprise data into de-identified datasets without destroying the structure and meaning that make the data valuable. The data does not arrive in one clean modality. It spans messages, documents, tables, files, images, metadata, and provider-specific structures, with important context distributed across all of them.

You will improve how well our system understands and protects that data. Your initial scope will be a prioritized subset of named-entity recognition, entity and identity resolution, structured extraction, classification, semantic review, or other model-backed parts of the de-identification pipeline. We do not expect one person to be an expert in every modality. The goal is measurable improvement in the areas you own: better precision, recall, F1, high-risk coverage, and preserved data utility across the failure modes that matter.

This is an applied, production-facing ML role. You will study errors, form hypotheses, build datasets and experiments, improve or replace models, and ship the result into a live pipeline. Evaluation, reproducibility, observability, and safe releases matter because they let us identify, ship, and verify meaningful model improvements in production.

## WHAT YOU'LL DO

- Own and improve NER, entity resolution, structured or tabular detection, document understanding, semantic review, or related de-identification systems
- Transform model failures and capability ceilings into a prioritized improvement roadmap
- Design active-learning loops that combine model sweeps, LLM-assisted review, clustering, and uncertainty signals to identify the examples most worth hand-labeling
- Build representative datasets and benchmarks, and use decision-relevant metrics to reveal strengths, weaknesses, uncertainty, and failure costs
- Choose and combine deterministic rules, classical ML, fine-tuning, embeddings, multimodal models, and LLM-based approaches based on the problem and evidence
- Design experiments, tune thresholds, analyze precision-recall and utility tradeoffs, and explain which changes are real, uncertain, or limited to particular conditions
- Productionize improvements with reproducible artifacts, evaluation evidence, runtime instrumentation, and safe rollout
- Optimize inference cost, latency, and throughput without hiding regressions in quality or high-risk recall
- Build high-fidelity evaluation environments with seeded failure modes and programmatic verifiers that expose subtle regressions
- Build reliable model- or agent-based harnesses with bounded behavior and explicit output verification when the problem calls for them
- Partner with Applied Science on measurement and calibration, Data and Product Engineering on pipeline and review systems, and Security and Quality on acceptable risk
- Use AI engineering tools deeply to accelerate research, implementation, error analysis, and evaluation while verifying their output

## WHAT SUCCESS LOOKS LIKE

- Model improvements generalize beyond the examples used to develop them and hold up in replay, shadow, and production evidence
- Priority modalities and entity classes show credible improvements in precision, recall, F1, or other decision-relevant quality measures
- High-risk misses decline without unacceptable over-redaction or loss of useful structure
- New formats and modalities can be covered without relying on brittle one-off fixes
- Improvements reduce meaningful delivery risk, review or rework burden, or loss of data utility rather than moving only an isolated benchmark
- The team can explain why a model changed, where it improved or regressed across consequential failure modes and data segments, and whether the change should ship
- The path from error discovery to a trustworthy production improvement becomes faster and more repeatable
- Quality gains remain inside acceptable inference-cost, latency, and operational constraints

## YOU MIGHT THRIVE HERE IF

- You have 3+ years of professional machine learning or software engineering experience, including improving models in production
- You have startup experience, enjoy broad ownership, and thrive when requirements are evolving or incomplete
- You use modern AI tools fluently and verify their output
- You have personally moved model quality through error analysis, data work, experimentation, implementation, deployment, and iteration
- You have a strong grasp of precision, recall, F1, calibration, thresholding, class imbalance, imperfect labels, distribution shift, and representative evaluation
- You are an applied engineer first: a strong Python and software engineer who can work inside data pipelines and production systems, not only notebooks
- You have a bias toward action while maintaining scientific and engineering rigor
- You are curious and stay current with relevant state-of-the-art methods
- You choose techniques based on the shape of the problem and can combine deterministic, statistical, neural, and LLM-based approaches
- You communicate uncertainty and tradeoffs clearly to scientists, engineers, and people making delivery or risk decisions

THIS ROLE MAY NOT BE FOR YOU IF

- You want to focus on research novelty without owning measurable production improvement
- You prefer optimizing one aggregate benchmark without investigating consequential failure modes, data segments, and failure costs
- You want data preparation, evaluation, deployment, and production diagnosis to belong entirely to other teams
- You reach for a larger model before understanding the errors, constraints, and simpler alternatives
- You do not want AI tools to be part of your daily engineering and research workflow

## BONUS

- Experience with NER, entity resolution, information extraction, document understanding, multimodal systems, or privacy-preserving ML
- Experience with hyperparameter tuning, data augmentation, model merging, ensembles, knowledge distillation, or multimodal model training
- Experience fine-tuning or adapting transformer, GLiNER, embedding, vision-language, or small specialized models
- Experience with active learning, uncertainty sampling, weak supervision, human-in-the-loop review, or LLM-assisted evaluation pipelines
- Experience building goldens, adversarial corpora, replay systems, model bakeoffs, agentic harnesses, or programmatic evaluation environments
- Experience with difficult ML or labeling problems
- Experience with ONNX Runtime, TensorRT, model pruning, quantization, or other CPU/GPU inference optimization
- Experience with sensitive enterprise data or other high-trust production systems
- Experience with synthetic data generation and managing the synth-to-real gap

## About Sunset

## Company Overview
- **One-liner**: Sunset is the first purpose-built platform that handles all legal, tax, and operational work to wind down venture-backed startups cleanly and quickly.
- **Entity Type**: Private (Pre-Seed & Venture funded)
- **Headquarters**: New York, New York, United States
- **Founded**: 2023
- **Founders**: Brendan Mahony (Founder & CEO)

## Core Business
- **Primary Industry**: Startup wind-downs / Dissolution services (legal, tax, and operational)
- **Target Customers**: B2B; founders and investors of venture-backed startups that are shutting down
- **Mission/Purpose**: To give founders the freedom to find what's next by removing the burden of the shutdown process.

## Products & Services
- **End-to-End Wind-Down Platform**: A fully managed service combining a purpose-built software platform with a dedicated team (attorneys, CPAs, Client Success leads) to handle legal, tax, and operational tasks. This includes state withdrawals, asset monetization (selling, auctioning, licensing IP), investor distributions (building waterfalls, collecting banking info, executing capital redistribution), and filing all final federal and state tax returns. [sunsethq.com](https://sunsethq.com/)

## Market Standing
- **Valuation/Market Cap**: Not publicly available
- **Key Metric (Funding)**: **$7.9M** total funding across two rounds:
    - Pre-Seed (Jan 2024): **$1.5M**
    - Venture Round (May 2025): **$6.4M** [linkedin.com](https://www.linkedin.com/company/sunsethq)
- **Key Metric (Scale)**: Has helped **over 175+** venture-backed startups shut down; has redistributed **hundreds of millions of dollars** back to investors. [builtin.com](https://builtin.com/company/sunset-0), [sunsethq.com](https://sunsethq.com/)
- **Notable Investors/Partners**: Backed by Y Combinator and some of the world’s best entrepreneurs and investors. [sunsethq.com](https://sunsethq.com/about)
- **Growth Signals**: 11 employees with +13.9% YoY headcount growth; active job postings for Data Engineer, ML Engineer, and Client Success Manager. [linkedin.com](https://www.linkedin.com/company/sunsethq)

## Competitive Advantages
- **Category-Defining Platform**: Sunset is the first company to build a dedicated, integrated platform (software + expert team) specifically for startup wind-downs, solving a problem that was previously handled ad-hoc by generalist lawyers and accountants.
- **End-to-End, Fully Managed Service**: They handle every aspect of the dissolution—legal, tax, asset monetization, and capital distribution—in one place with one fee, saving founders months of time and significant personal cost.
- **Deep Founder Empathy**: Founded by Brendan Mahony after his own painful, 7-month dissolution experience. This mission-driven origin creates a strong, empathetic culture focused on helping founders during a difficult time. [sunsethq.com](https://sunsethq.com/about)
- **Strong Traction with Top-Tier Clients**: Having served over 175 venture-backed startups and redistributed hundreds of millions to investors, they have built significant credibility and trust in the startup ecosystem.

## Strategic Focus
- **Scaling the Platform**: Investing in engineering (Data Engineer, ML Engineer roles) to further automate and streamline the wind-down process.
- **Deepening Service Offerings**: Continuing to refine the legal, tax, and asset monetization workflows to ensure the fastest, most efficient, and highest-value return for investors.
- **Becoming the Standard**: Establishing Sunset as the default, go-to service for any venture-backed startup facing a shutdown, thereby creating a standard for an industry that previously had none.

## Why Work Here
- **High-Impact Mission**: Employees directly help founders through one of the most difficult moments of their careers, providing closure and allowing them to move on. The work is deeply meaningful and has a tangible, positive impact on people’s lives.
- **Strong Values-Driven Culture**: Core values include "Carry each other," "Be the painkiller," "Make it world-class," "Swing big, learn fast," and "Keep your promises," fostering a supportive, empathetic, and high-performance environment. [sunsethq.com](https://sunsethq.com/careers)
- **Growth Stage Company**: As a small but growing team (11 people), employees have outsized impact and ownership. The company is generating revenue and has raised significant funding, indicating a strong trajectory.
- **Work Environment**: On-site in New York City with an open-door policy, open office floor plan, and an engineering team that utilizes pair programming. [builtin.com](https://builtin.com/company/sunset-0)
- **Benefits**: Offers company equity, generous PTO (unlimited policy), paid holidays, and health/vision/dental insurance. [builtin.com](https://builtin.com/company/sunset-0)
- **Tech Stack**: Modern and robust, including JavaScript, TypeScript, Ruby on Rails, React, Remix, PostgreSQL, Redis, and Azure. [builtin.com](https://builtin.com/company/sunset-0)

## Sources
1. [sunsethq.com](https://sunsethq.com/)
2. [sunsethq.com/careers](https://sunsethq.com/careers)
3. [sunsethq.com/about](https://sunsethq.com/about)
4. [linkedin.com/company/sunsethq](https://www.linkedin.com/company/sunsethq)
5. [builtin.com/company/sunset-0](https://builtin.com/company/sunset-0)

## Other roles at Sunset

- [Security Lead](https://feeny.ai/job/security-lead-sunset-new-york-6vgygry650b6) — New York, NY
- [Platform Engineer](https://feeny.ai/job/platform-engineer-sunset-new-york-tjxkj0jzd1ts) — New York, NY
- [Engineering Manager](https://feeny.ai/job/engineering-manager-sunset-new-york-e0vrwgg97akb) — New York, NY
- [Design Engineer](https://feeny.ai/job/design-engineer-sunset-new-york-nj7k67c09en7) — New York, NY
- [Data Scientist](https://feeny.ai/job/data-scientist-sunset-new-york-9rqm51n50pf9) — New York, NY
- [AI Product Engineer](https://feeny.ai/job/ai-product-engineer-sunset-new-york-n8f4zqtcafxf) — New York, NY
- [Full-Stack Product Engineer](https://feeny.ai/job/full-stack-product-engineer-sunset-new-york-dj4a73d25w0d) — New York, NY
- [GTM Engineer](https://feeny.ai/job/gtm-engineer-sunset-new-york-r3wr42whx92v) — New York, NY
- [Growth Marketer](https://feeny.ai/job/growth-marketer-sunset-new-york-rs2h7qxaev58) — New York, NY
- [Business Development Representative, Data Partnerships](https://feeny.ai/job/business-development-representative-data-partnerships-sunset-new-york-jhpb75y5za0p) — New York, NY