Search for part-time machine learning engineer roles in New York, focusing on contract positions, with 3-5 years of ML engineering experience, working with Python and TensorFlow, ideally in the fashion or e-commerce industry.

Okay, I've searched for part-time machine learning engineer roles in New York that are contract positions, requiring 3-5 years of ML engineering experience, focusing on Python and TensorFlow, and ideally within the fashion or e-commerce industry. I found 27 positions for you.

Part-Time Machine Learning Engineer

New York, NY

Contract

3-5 years

Python

TensorFlow

Fashion

Can you help me to analyze the job description and company culture?

Sure. Here's the analysis:

Job Analysis:

The Part-Time Machine Learning Engineer at Cadenova is fundamentally tasked with architecting and deploying the machine learning infrastructure for their predictive insights engine tailored to the fashion industry. The key responsibilities revolve around developing a neural network pipeline that can seamlessly integrate brand metadata, visual data from fashion imagery, and sales history—essentially bridging the gap between raw data and actionable insights for fashion brands. This role requires a blend of technical acumen and creativity, as candidates will need to design systems that not only perform functions but also enhance decision-making in a highly subjective field. Given the fast-paced nature of startups, candidates should anticipate challenges such as tight deadlines, evolving project scopes, and a requirement for continuous iteration based on stakeholder feedback. Success in this role means being able to deliver robust, scalable ML solutions that the team can depend upon, supporting them as they help brands optimize their product assortments based on nuanced market insights and consumer trends.

Company Analysis:

Cadenova operates in the intersection of artificial intelligence and the fashion industry, positioning itself as an innovative disruptor that leverages data to transform how fashion brands make product decisions. The company's focus on reducing guesswork within assortment planning aligns with a broader trend towards data-driven decision-making in fashion, suggesting that they value agility, innovation, and a strong understanding of both technology and market dynamics. The culture likely emphasizes collaboration, creativity, and a willingness to experiment, as reflected in their need for a part-time Machine Learning Engineer who can quickly adapt and deliver. This role reports on the frontline of AI development, potentially providing significant visibility to leadership as the results of their work directly impact product offerings. For someone in this position, aligning with Cadenova’s mission to empower fashion businesses through intelligent insights will be crucial for both satisfaction and success within the role.

Mock up some interview questions based on the job description.

Absolutely. Here are some mock interview questions that could come up:

Tell me about a time when you deployed a machine learning model in a production environment. What challenges did you face, and how did you overcome them?
Can you describe a project where you integrated a neural network pipeline with existing data infrastructure? What were the key learnings?
Share an experience where you had to work with a cross-functional team to address a complex problem. How did you ensure alignment and communication?
What methods have you employed to calculate KPIs for machine learning recommendations, and how did you adjust your approach based on the results?
How would you approach setting up a NoSQL database for a new machine learning project, particularly for a fashion-related application?
What are the steps you would take to run fashion images through a computer vision model to extract relevant features?
Imagine a scenario where the outputs of your machine learning model did not align with brand expectations. How would you handle the situation?
If you were asked to enhance the predictive capabilities of Cadenova's insights engine, what new features or improvements would you suggest?
Why are you interested in working at Cadenova specifically, and how does our mission resonate with your career goals?
How do you stay current with the latest trends and technologies in machine learning and their applications in fashion?
What excites you most about applying machine learning within the fashion industry?
What unique challenges do you think fashion brands face that machine learning can address effectively?
How might you approach collecting user feedback to refine the recommendation system you would be developing here at Cadenova?

Job Details

Overview Company

Machine Learning Engineer

Cadenova

a day ago

Highlight

HybridPart-time, Contractor3+ yearsMid

Summary

Cadenova is seeking a Part-Time Machine Learning Engineer (Contractor) to join their team in New York, NY. The role involves architecting and deploying the core ML infrastructure for their predictive insights engine.

About the Role

As a Part-Time Machine Learning Engineer, you will be responsible for developing and deploying a neural network pipeline to generate design and assortment recommendations. You will set up and configure NoSQL data infrastructure, run images through a computer vision model to extract fashion features, and integrate outputs with historical data to support backend readiness for future forecasting and revenue optimization.

About You

Required:

3–5+ years of ML engineering or applied data science experience.
Strong Python skills with experience in TensorFlow or PyTorch.
Proficient with NoSQL DBs (e.g., Firestore, MongoDB).
Experience deploying ML pipelines in production (vision models preferred).

Preferred:

Fashion/e-comm experience or familiarity with KPI frameworks in recommendation systems.

Cadenova

Cadenova is building the next generation of AI-powered decision support for fashion brands. Our platform helps merchandisers, designers, and planning teams make smarter product decisions earlier in the development process by providing actionable insights before any apparel is produced. We combine internal brand data with external market signals to generate forecasts, trend insights, and optimization recommendations at the product level. Cadenova’s mission is to reduce uncertainty and guesswork in assortment planning, enabling brands to better anticipate consumer preferences, reduce overbuying, and make more confident design and production bets.

New York, NY