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Lstm Model Jobs (NOW HIRING)

Design, develop, and implement statistical models and machine learning solutions to solve complex business problems. * Apply deep learning techniques including LSTM, N-BEATS, CNN, and RNN for time ...

Develop and optimize forecasting models including ARIMA/SARIMA, Prophet, LSTM, and other advanced predictive approaches. * Perform data preprocessing, feature engineering, and exploratory analysis on ...

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Lstm Model information

What are the key skills and qualifications needed to thrive as an LSTM Model Developer, and why are they important?

To thrive as an LSTM Model Developer, you need a solid background in machine learning, deep learning, and programming, typically supported by a degree in computer science, data science, or a related field. Proficiency with Python, TensorFlow, Keras, and knowledge of time series data handling are crucial, as are relevant certifications in AI or deep learning. Strong analytical thinking, attention to detail, and effective problem-solving set exceptional candidates apart. These skills ensure the development of robust and accurate LSTM models for complex sequence prediction tasks and real-world applications.

What are some common challenges faced when deploying LSTM models in a production environment?

Deploying LSTM models in production often involves challenges such as managing computational resources due to the model's complexity and ensuring low-latency predictions for real-time applications. Additionally, LSTMs can be sensitive to input data format and require careful preprocessing and consistent data pipelines. Monitoring model performance over time is essential, as LSTMs may degrade if the underlying data distribution changes. Collaborating with data engineers and DevOps teams is also key to ensure smooth integration and scalability.

What is the difference between Lstm Model vs Data Scientist?

AspectLstm ModelData Scientist
Required CredentialsKnowledge of machine learning, deep learning, programming (Python, TensorFlow)Statistics, programming, data analysis, often a degree in related fields
Work EnvironmentDeveloping models, coding, testing algorithmsData analysis, reporting, collaborating with teams
Industry UsageAI, NLP, time-series forecastingBusiness analytics, research, data-driven decision making

While an Lstm Model is a specific deep learning technique used for sequence data, a Data Scientist applies various methods, including LSTM, to analyze data and generate insights. The LSTM model is a tool within a Data Scientist's skill set, focusing on model development, whereas Data Scientists handle broader data analysis tasks across industries.

What is an LSTM model?

An LSTM (Long Short-Term Memory) model is a type of recurrent neural network (RNN) architecture used in the field of deep learning. It is specifically designed to learn and remember long-term dependencies in sequential data, making it highly effective for tasks like language modeling, speech recognition, and time series prediction. LSTM models use special memory cells and gating mechanisms to manage and update information over long sequences, which helps them overcome the limitations of traditional RNNs such as the vanishing gradient problem.
Infographic showing various Lstm Model job openings in the United States as of May 2026, with employment types broken down into 1% As Needed, 84% Full Time, 14% Part Time, and 1% Temporary. Highlights an 91% Physical, 3% Hybrid, and 6% Remote job distribution.
Director of Software Engineering, Data & AI

Director of Software Engineering, Data & AI

Relativity Space

Long Beach, CA • On-site

$276K/yr

Full-time

This job post has expired today. Applications are no longer accepted.


Job description

Job Summary:
Relativity Space is building rockets to serve today’s needs and tomorrow’s breakthroughs. As the Director of Data & AI, you will be responsible for the strategic direction and technical leadership of data and AI initiatives, including overseeing the development of scalable data architectures and driving automation through AI models.
Responsibilities:
• Lead the design and implementation of scalable data architectures, including ETL pipelines, data modeling, data warehouse management, and reporting infrastructure, ensuring high-quality, actionable data across all systems.
• Oversee the development of a distributed data platform that supports mission-critical applications, enabling real-time data ingestion, access, and orchestration for test and launch and factory operations.
• Direct the design, development, and deployment of AI and machine learning models that leverage our data to drive automation, decision-making, and predictive analytics across the organization. Ensure these models are scalable, reliable, and move the needle on critical metrics for the company.
• Lead, mentor, and grow a team of software engineers, data engineers and machine learning engineers. Create an environment that fosters innovation, continuous learning, and technical excellence while aligning goals with company objectives.
• Partner with leadership and cross-functional users across the company to define the long-term strategy for data and AI initiatives, aligning the technical roadmap with company goals.
Qualifications:
Required:
• Degree in Computer Science.
• 10+ years of experience in software engineering, data engineering or machine learning, with at least 5 years in a leadership role, overseeing large-scale systems.
• Experience designing, building, and deploying AI models and machine learning systems, particularly in high-performance or mission-critical environments.
• Proficient in AI techniques including deep learning, reinforcement learning, and natural language processing.
• Understanding of core ML concepts.
• Experience with advanced models such as Transformers/LLMs, LSTM models, time-series forecasting, and predictive analytics for real-time decision-making and automation.
• Familiarity with the mathematical underpinnings of ML.
• Experience with building and scaling AI infrastructure, including deploying and managing machine learning models in production environments.
• Experience with agentic architectures (e.g., MCP).
• Experience in building scalable data architectures, including ETL pipelines, data modeling, and analytics.
• Expertise with modern data tools and technologies (e.g., Kafka, Spark, SQL, NoSQL, data lakes).
• Understanding of cloud platforms (AWS, GCP, etc.), containerization (Docker, Kubernetes), and orchestration for building and deploying services.
• Demonstrated ability to lead and mentor teams, build a strong technical culture, and drive organizational change.
• Experience in developing strategic roadmaps and managing cross-functional collaborations to align on company-wide data and AI objectives.
Preferred:
• Direct experience with aerospace or manufacturing
Company:
Relativity Space is an aerospace company that designs, develops, and builds 3D printed rockets. Founded in 2015, the company is headquartered in Long Beach, USA, with a team of 1001-5000 employees. The company is currently Late Stage.