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Director Machine Learning Jobs in Millstone, NJ (NOW HIRING)

Nelo Mobile

Data Scientist (Machine Learning)

New York, NY · On-site

$180K - $230K/yr

This involves high-stakes constrained optimization problems where "good enough" mathematics will result in direct financial loss. We are looking for the type of person who is frustrated by the "black ...

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Director Machine Learning information

See Millstone, NJ salary details

$35.9K

$91.8K

$140.7K

How much do director machine learning jobs pay per year?

As of Aug 6, 2026, the average yearly pay for director machine learning in Millstone, NJ is $91,757.00, according to ZipRecruiter salary data. Most workers in this role earn between $71,400.00 and $105,800.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the director machine learning position, and why are they important?

To thrive as a Director Machine Learning, you need advanced expertise in machine learning, statistics, data science, and leadership, typically supported by a master's or Ph.D. in a related field and several years of relevant industry experience. Familiarity with tools such as Python, TensorFlow or PyTorch, cloud platforms, and data management systems, as well as certifications like AWS Certified Machine Learning or Google Professional Machine Learning Engineer, are commonly required. Exceptional communication, strategic thinking, and team management skills distinguish top candidates in this role. These capabilities are essential for driving organizational AI initiatives, fostering high-performing teams, and delivering impactful business solutions.

What is a director machine learning?

A Director of Machine Learning leads teams in developing and deploying machine learning models to solve business challenges. They define the AI strategy, oversee research, and ensure models are scalable and ethical. This role requires expertise in machine learning, data science, and leadership, as well as collaboration with cross-functional teams. Directors also stay updated on industry advancements and drive innovation within their organizations.

What are the primary responsibilities and challenges faced by a director machine learning on a daily basis?

A Director of Machine Learning is typically responsible for overseeing the development and deployment of machine learning solutions, mentoring technical teams, setting strategic direction for AI initiatives, and ensuring the alignment of projects with organizational goals. Challenges often include balancing innovative research with business priorities, navigating evolving technology landscapes, and coordinating efforts across data science, engineering, and stakeholder teams. This role requires regular collaboration with product managers, executives, and cross-functional departments to prioritize initiatives and communicate complex technical concepts. Successful directors excel at fostering a culture of continuous learning, optimizing team productivity, and staying ahead in a fast-paced, rapidly changing field.

What cities near Millstone, NJ are hiring for Director Machine Learning jobs? Cities near Millstone, NJ with the most Director Machine Learning job openings:
Director Machine Learning jobs near you
Infographic showing various Director Machine Learning job openings in Millstone, NJ as of June 2026, with employment types broken down into 1% Internship, 1% As Needed, 80% Full Time, 14% Part Time, and 4% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $91,757 per year, or $44.1 per hour.

Machine Learning Engineer (LLM / Personalization)

Qloo

New York, NY • On-site, Remote

$100K - $120K/yr

Full-time

Medical, Retirement, PTO

Re-posted 24 days ago

Job description

About Us

At Qloo, our cutting-edge Taste AI technology leverages extraordinary amounts of data-over half a billion records of public figures, places, music artists, media, brands, and more, plus a globe-spanning consumer behavior and sentiment database-to unearth deep insights about consumer preferences.

From understanding global travel trends to curating the perfect restaurant recommendation based on your unique tastes, our Taste AI engine sifts through the noise to find the signals that matter.

And the best part? Qloo's API suite is powered by cultural entities, not personal identities-ensuring our insights are derived without relying on personally identifiable information.

As we expand our investment in LLMs and AI agents, we are building the next generation of intelligent systems that combine generative models with structured taste intelligence-bringing reliability, explainability, and real-world grounding to AI applications.

Role Overview

As a Machine Learning Engineer reporting to the LLM Research Lead, you will operate at the intersection of large language models, recommendation systems, and Qloo's proprietary taste graph.

You will work closely with Research and Data Engineering teams to design and deploy systems that integrate LLMs with structured cultural intelligence. This includes building production-ready ML systems, experimenting with new model architectures, and developing novel approaches to grounding generative AI in real-world data.

This role is ideal for someone who enjoys both research-adjacent work and shipping production systems-and wants to shape how LLMs interact with structured knowledge at scale.

Responsibilities
  • Design, build, and deploy machine learning models and systems that power personalization, recommendation, and taste understanding

  • Develop and productionize LLM-powered features, including retrieval-augmented generation (RAG), agent workflows, and prompt / tool orchestration

  • Integrate LLMs with Qloo's structured entity graph and embedding systems to improve accuracy, relevance, and explainability

  • Experiment with and evaluate modern ML approaches (transformers, embedding models, ranking systems, hybrid recommenders)

  • Collaborate with Data Engineering to leverage large-scale datasets for LLM pipelines

  • Contribute to model evaluation frameworks and optimize model performance, cost, and latency in production environments

  • Stay up-to-date with the latest advancements in LLMs, recommendation systems, and applied ML-and bring those insights into production

Qualifications

  • Strong experience in Python and machine learning frameworks (e.g., PyTorch, CUDA, Metaflow/Kubeflow, etc)

  • Experience working with large language models (LLMs), including APIs (OpenAI, Anthropic, etc) and/or open-source models (Hugging Face)

  • Familiarity with retrieval systems, embeddings, vector search, or recommendation systems

  • Experience building and deploying ML systems in production environments

  • Solid understanding of data pipelines (Airflow) and working with large-scale datasets (e.g., Spark, S3, SQL)

  • Experience with AWS or similar cloud platforms

  • Experience working in AI-native development workflows, including heavy use of tools like Claude Code, Cursor, or similar

  • Strong problem-solving skills and ability to work across both research and engineering domains

  • Prior experience in a startup or fast-paced environment

We Offer
  • Competitive salary and benefits package, including health insurance, retirement plan, and paid time off

  • The opportunity to shape how LLMs and structured data systems work together in real-world applications

  • A collaborative, low-ego work environment where your ideas are valued and your contributions are visible

  • Direct exposure to cutting-edge work at the intersection of generative AI and large-scale recommendation systems

  • Flexible work arrangements (remote and hybrid options) and a healthy respect for work-life balance

$100,000 - $120,000 a year
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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