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NoGood
NoGood

Machine Learning Engineer

Location

Remote restrictions apply
See all remote locations

Salary Estimate

N/AIconOpenNewWindows

Seniority

N/A

Tech stacks

Machine learning
Data
Growth
+47

Visa

U.S. visa required

Permanent role
6 days ago
Apply now

We're Hiring: Machine Learning Engineer (1–2 Years Experience)

We Are:

NoGood is a leading growth, performance, and creator marketing agency at the intersection of performance science and creative storytelling. We empower impactful brands to achieve sustainable growth through innovative strategies, cutting-edge analytics, and creative experimentation. Our dynamic team combines top-tier talent, technology, and proprietary AI-driven solutions to deliver unparalleled marketing results for industry-leading brands.

Description:

We’re looking for a highly motivated Machine Learning Engineer with 1–2 years of hands-on experience in ML/AI application development. This role is perfect for early-career professionals who thrive in a fast-paced, innovation-driven environment and want to work on real-world applications of machine learning and generative AI.

You Have:

  • End-to-End ML Pipeline Development: Design, implement, and maintain scalable ML pipelines — from data preprocessing to model training and deployment.
  • LLM Integration: Collaborate on fine-tuning and deploying large language models (LLMs) like GPT, BERT, or open-source alternatives (e.g., LLaMA, Mistral) for NLP-driven applications.
  • Data Engineering & Analysis: Work with structured and unstructured data — perform wrangling, cleaning, and feature engineering using tools like Pandas, PySpark, or Dask.
  • Model Monitoring & Optimization: Use MLOps tools (e.g., MLflow, Weights & Biases) for experiment tracking, model versioning, and continuous performance monitoring.
  • Interactive Visualizations: Develop dashboards and data visualizations using Plotly, Dash, or Streamlit to communicate findings effectively.
  • Cloud-native Deployment: Support model deployment using FastAPI or Flask, containerized via Docker, and deployed on cloud platforms (AWS/GCP/Azure).
  • Research & Innovation: Stay current with emerging trends in ML and generative AI; evaluate and prototype new models, algorithms, and frameworks.

You Will Do:

  • Bachelor’s degree in Computer Science, Machine Learning, Data Science, Engineering, or related field.

  • 1–2 years of hands-on experience in ML engineering, data science, or full-stack development involving ML components.

  • Proficiency in Python and core ML/data libraries (NumPy, Pandas, Scikit-learn, etc.).

  • Working knowledge of TensorFlow, PyTorch for model development.

  • Experience with Natural Language Processing and foundational NLP libraries (spaCy, Hugging Face Transformers, NLTK).

  • Exposure to modern LLM stacks (e.g., LangChain, LlamaIndex) and prompt engineering.

  • Familiarity with version control (Git) and collaborative development practices.

  • Experience working with SQL and NoSQL databases.

  • [Bonus] Experience with:

  • Cloud platforms (AWS , GCP , or Azure )

  • CI/CD pipelines and containerization (Docker, Kubernetes)

  • Experiment tracking tools (MLflow, W&B)

  • Vector databases (Pinecone, Chroma Db)

About the company

NoGood is an award-winning, tech-enabled growth consultancy that has fueled the success of some of the most iconic brands.

We are a team of growth leads, creatives, engineers and data scientists who help unlock rapid measurable growth for some of the world’s category-defining brands. We bring together the art and science of strategy, creative, content and growth expertise into a single cohesive team, powered by robust data analytics and proprietary AI tech.

Based in NYC, we support partners globally, with a client partner roster that includes VC-backed startups, scale-ups, and Fortune 500 companies such as Nike, Oura, Spring Health, TikTok, Intuit, P&G, and more.

Since 2016, we’ve been delivering what others only promise. Why settle for good enough if you can be up to NoGood?

About NoGood

👥11-50
📍New York
🔗Website
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