Machine Learning Engineer

Deciml; New York (NY); On-site; USD 120,000 - 190,000; Full time

Job summary

Deciml is seeking a Machine Learning Engineer in New York to design and deploy recommendation models that power personalised discovery and real-time decision-making.

You will own data pipelines, train on large datasets and collaborate with product teams to deliver low-latency, scalable solutions. Opportunity to apply ML across NLP and reinforcement learning to solve real product problems.

Qualifications

• Experienced ML Engineer with strong algorithms, data structures and statistics.
• Proven ability to build and deploy recommender systems in production with measurable impact.
• Familiar with retrieval, ranking and personalization techniques, embeddings and candidate generation.

Responsibilities

• Design, develop and deploy recommendation models to improve discovery and engagement.
• Build scalable data pipelines and feature engineering on large datasets.
• Develop and optimise ML infrastructure for scalable training and inference in production.
• Collaborate with product teams to integrate models with low latency real-time processing.
• Design experiments and use offline evaluation and A/B testing to measure impact and guide improvements.
• Work across deep learning, NLP and reinforcement learning where applicable.

Skills

Python

Recommender systems

Data pipelines

ML deployment

Tools

TensorFlow

PyTorch

Job description

As a Machine Learning Engineer, you’ll work alongside engineering and product teams to build algorithms that power personalisation, recommendations, search and real-time decision-making. Your primary focus will be recommendation systems, with opportunities to apply machine learning to fraud detection and content moderation.

You’ll take projects from initial problem definition through to deployment and iteration, owning the models, data pipelines and supporting infrastructure needed to deliver results.

• Design, develop and deploy recommendation models that improve discovery, personalisation, engagement and retention.

• Build scalable data pipelines and use large datasets to develop features, train models and inform product development.

• Develop and optimise machine learning infrastructure, ensuring training and inference systems are scalable, efficient and reliable in production.

• Work closely with product teams to integrate models into the platform, supporting low-latency inference and real-time data processing.

• Design experiments and combine offline evaluation with A/B testing to measure impact and guide improvements.

• Experiment with techniques across deep learning, natural language processing and reinforcement learning where they can solve meaningful product problems.

• Monitor, optimise and iterate on deployed models as user behaviour, data and product needs evolve.

• Develop models for fraud detection, abuse prevention and content moderation to improve platform quality and user experience.

• Collaborate with engineers and data scientists to solve challenges involving high-volume user and transactional data.

• Identify useful developments in ML research and turn them into practical improvements.

Who You Are

• An experienced Machine Learning Engineer with a strong foundation in algorithms, data structures and statistical modelling.

• Experienced in building and deploying recommendation systems in production, with measurable improvements to user experience or business outcomes. This is our highest-priority area of experience.

• Familiar with retrieval, ranking and personalisation techniques, including embeddings, candidate generation and challenges such as cold starts and sparse data.

• Proficient in Python and frameworks such as TensorFlow or PyTorch, with strong software engineering skills and hands‑on experience deploying models.

• Skilled in building and maintaining ML infrastructure, including data pipelines, model deployment, monitoring and systems that scale to large datasets.

• Comfortable working across the entire ML lifecycle, from research and experimentation to deployment and maintenance.

• Experienced working in startups, delivering at a high pace through ambiguity, shifting priorities and limited resources.

• Hands‑on and resourceful, willing to build the models, pipelines and infrastructure yourself to get a solution into production.

• High‑agency and ownership-driven, capable of identifying problems, making decisions and leading projects end to end with minimal guidance.

• Highly collaborative, with the ability to communicate technical concepts clearly to engineers, product teams and non‑technical stakeholders.

• Experience with production fraud detection, abuse prevention or content moderation systems will be highly advantageous.

• An interest in emerging technologies and digital platforms is a plus.