Head of Machine Learning & AI Engineer

Description

Head of Machine Learning & Applied AI Engineering

Location

Remote in USA only; periodic travel to Seattle, Bay Area for team, client meetings

Compensation

$200K - $215K upon funding

Our client is on a mission to transform how professional organizations, industry associations, and expert communities solve problems, deliver value, and engage their members. They believe that with the right blend of innovation, leadership, and technology, these communities can play an even more powerful role in shaping industries and society.

Our client is building intelligent, human-centered solutions that help these organizations thrive-combining the promise of AI with a deep understanding of how people and communities' work. Founded by experienced leaders in technology, product strategy, and go-to-market execution, they are ready to scale.

Reporting

Reports To Chief Technology Officer (CTO), Team Size 2-4 Product Managers / Analysts within initial 12 months (with cross-functional influence over design & data teams)

The H ead of Machine Learning & Applied AI Engineering is responsible for ensuring that these customers experience deep, lasting success from the moment they engage with them in the sales cycle, through their onboarding and through ongoing use, impact, and renewal.

This leader will work closely with each early customer, embedding themselves within the customer environment to understand their unique context, challenges, needs, and use cases-and ensure the platform delivers maximum relevance, adoption, and transformative value. This is a foundational role, initially hands‑on and later team‑building and system‑scaling, that directly influences customer delight, product‑market fit, use case validation, reference ability, and renewal expansion.

The Head of Machine Learning & Applied AI Engineering is the technical leader driving core AI model development and deployment efforts. In this role, you will define and execute their AI and ML strategy, ensuring that their neuro‑symbolic, human‑in‑the‑loop applied vertical AI and agentic AI ecosystem solutions are built on robust, scalable, and state‑of‑the‑art models. You will oversee the full ML project lifecycle - from research and prototyping to model training, evaluation, and deployment into production. As the senior‑most ML engineer, you will mentor a team of ML engineers and data scientists, fostering innovation and engineering excellence in areas like natural language understanding, knowledge graph reasoning, and reinforcement learning (for agentic behavior).

If you've worked at organizations like OpenAI, Google Brain / DeepMind, Meta AI, Microsoft Research, Anthropic, or similar - or in advanced ML engineering roles at companies such as Netflix (with its recommendation algorithms) or Apple (Siri, etc.) - you likely have the depth of experience we need. If you have experience deploying ML in enterprise settings (with concerns like security, compliance, on‑prem deployments), that's a bonus. Additionally, familiarity with human‑in‑the‑loop ML or hybrid AI (neuro‑symbolic) projects - for example, systems that combine rules with learning, or that incorporate human feedback like active learning loops - would indicate a great fit with their approach.

Qualifications / Requirements

• Machine learning engineering leader with 5-10+ years' experience in AI / ML, including at least 5 years of hands‑on building and deploying ML models and several years in a leadership or tech lead capacity.
• Strong programming skills in Python, skilled in C++ or Java.
• Excellent recent experience in TensorFlow, PyTorch or scikit-learn
• Experience in distributed computing or big data (Spark, Hadoop)
• Cloud platforms AWS, GCP, Azure and experience handling scaling issues
• You are equal parts scientist and engineer - possessing a strong theoretical foundation in AI (algorithms, statistics, deep learning) and the practical coding skills to implement and optimize models.
• You have a track record of delivering AI solutions into real‑world production environments, not just research demos. This means you understand the nuances of scaling models, addressing data quality issues, and monitoring live model performance.
• Early‑stage startup experience is highly preferred
• Team player with a get stuff done mindset and hands on in development
• Master's or PhD in a related field, and perhaps even contributed to publications or patents, but you thrive in applying research to solve concrete problems.
• Citizen or Green Card Holder ...not able to sponsor at this time

Qualified candidates please send resume to Kar[email protected]

#J-18808-Ljbffr