Machine Learning Engineer
Posted Today by 1766127495
Negotiable
Outside
Remote
USA
Summary: The Machine Learning Engineer role involves developing intelligent, end-to-end solutions for advertising goals, leveraging machine learning and deep learning techniques. Candidates will collaborate with applied researchers and engineers to create production-ready services, optimize software performance, and analyze data for actionable insights. Strong programming skills in Python and SQL, along with experience in MLOps, are essential for success in this position. The role is remote and requires effective communication and collaboration within a multi-functional team.
Key Responsibilities:
- Work with Applied Researchers, Engineers, Analytics and multi-functional teams to produce end-to-end production-ready solutions.
- Design, implement, and maintain data and ML/DL services, encompassing efficient data pipeline, ML model training, inference & deployment processes, robust tracking and monitoring system, as well as other MLOps work.
- Optimize software performance to achieve the required throughput and/or latency.
- Analyze data, interpret experiments, and uncover trends, insights, and opportunities. Translate complex data into actionable recommendations for technical and non-technical audiences.
- Monitor, triage and drive resolution processes for production system incidents, ensuring system reliability.
Key Skills:
- BS or MS in Computer Science or equivalent experience.
- Strong programming skills in SQL, Python, Scala or Java.
- Solid understanding of machine learning fundamentals and applications.
- Proficiency with key machine learning and deep learning libraries, e.g. PyTorch/Tensorflow, Transformers, scikit-learn, vLLM, and Ray.
- Strong debugging, analytical, and communication skills to collaborate effectively with engineers and researchers.
- Good understanding of ML/DL infrastructure, including areas like autoscaling, job scheduling, and workload orchestration across heterogeneous compute (CPU/GPU/accelerators).
- Experience with big data technologies (i.e., Spark, Hadoop) and database technologies (i.e., SQL, NoSQL).
- Experience in solving problems using data science, building practical solutions, and deploying models into production environments.
- Experience with observability stacks (e.g. Prometheus, Grafana) and data visualization tools (e.g., Tableau, Kibana) is a plus.
- Experience with developing large language model (LLM) applications is a plus.
Salary (Rate): undetermined
City: undetermined
Country: USA
Working Arrangements: remote
IR35 Status: outside IR35
Seniority Level: undetermined
Industry: IT
Detailed Description From Employer:
Title: Machine Learning Engineer
Number of openings: 2
Locations: US remote
Experience: 5-7 yrs
Note: The submitted candidates will get an assessment link from client on Monday and if they score more than 80% they will be eligible for the interview
There will be 2 Rounds of interview from the client
Must have languages: Python, SQL, (Scala or Java).
Must have skills: PyTorch, Huggingface transformers, Deep Learning, Spark/Hadoop, MLOps
Additional must haves: Strong communication skills. Must be willing to contribute to team discussions and solutions. Also, must be available for stand ups, core meetings, and slack availability.
Team Overview
Advertising is a rapidly expanding and strategically vital sector that is shaping our organisation s future. The digital advertising industry is evolving quickly, with a notable shift as ecommerce advertisers recognize the significant value offered by platforms. This creates a massive opportunity for us as advertisers reallocate spending away from the Google-Facebook duopoly. Furthermore, advertising strengthens our core ecommerce business by providing sellers with a crucial tool to accelerate inventory movement and enhancing the buyer experience through the surfacing of high-quality items.
Team Role
This applied research team is dedicated to developing intelligent, end-to-end solutions that empower our advertiser sellers to achieve their advertising goals. This involves providing strategic guidance, such as recommending optimal ad placements, selecting the right inventory, keywords, bids, and budgets for their campaigns, and facilitating continuous optimization. This rapidly evolving space presents significant business potential and is a critical area of need. To address this, we build scalable data and Machine Learning/Deep Learning services, which entails working with massive datasets, applying diverse data science and ML/DL techniques, and adhering to the best engineering practices.
Responsibilities
Work with Applied Researchers, Engineers, Analytics and multi-functional teams to produce end-to-end production-ready solutions.
Design, implement, and maintain data and ML/DL services, encompassing efficient data pipeline, ML model training, inference & deployment processes, robust tracking and monitoring system, as well as other MLOps work.
Optimize software performance to achieve the required throughput and / or latency.
Analyze data, interpret experiments, and uncover trends, insights, and opportunities. Translate complex data into actionable recommendations for technical and non-technical audiences.
Monitor, triage and drive resolution processes for production system incidents, ensuring system reliability.
Requirements
BS or MS in Computer Science or equivalent experience
Strong programming skills in SQL, Python, Scala or Java
Solid understanding of machine learning fundamentals and applications.
Proficiency with key machine learning and deep learning libraries, e.g. PyTorch/Tensorflow, Transformers, scikit-learn, vLLM, and Ray.
Strong debugging, analytical, and communication skills to collaborate effectively with engineers and researchers.
Good understanding of ML/DL infrastructure, including areas like autoscaling, job scheduling, and workload orchestration across heterogeneous compute (CPU/GPU/accelerators).
Experience with big data technologies (i.e., Spark, Hadoop) and database technologies (i.e., SQL, NoSQL)
Experience in solving problems using data science, building practical solutions, and deploying models into production environments.
Experience with observability stacks (e.g. Prometheus, Grafana) and data visualization tools (e.g., Tableau, Kibana) is a plus.
Experience with developing large language model (LLM) applications is a plus.