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Softmax Jobs (NOW HIRING)

Frontage Laboratories

Associate Scientist

Exton, PA · On-site

Familiarity with software for data analysis (e.g., SoftMax Pro, Watson LIMS, GraphPad Prism). * Understanding of regulatory expectations for bioanalytical method validation (FDA, EMA, ICH guidelines)

Eurofins Scientific

Bioassay Scientist

Columbia, MO · On-site

$33.50 - $42/hr

Analyze data with software including Molecular Devices Softmax Pro and effectively communicate results. * Train and mentor junior staff. * Utilize laboratory information management system (LIMS) with ...

Eurofins

Bioassay Method Development

Indianapolis, IN · On-site

Familiar with Molecular devices plate reader software such as SoftMax Pro. BioTek plate readers. Additional Information * The position is full-time, M- F 8 am to 5 pm * May be requested to work on ...

Moderna, Inc.

Sr. Engineer, GxP Systems

Norwood, MA · On-site

$121K - $194K/yr

... readers (SoftMax Pro). * Ensure all validated laboratory systems comply with FDA 21 CFR Part 11, GAMP 5, and USP 1058 Analytical Instrument Qualification (AIQ) requirements. * Translate system ...

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Softmax information

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need a solid background in mathematics, statistics, and programming, often supported by a degree in computer science or a related field. Familiarity with popular ML frameworks (such as TensorFlow, PyTorch), version control systems, and relevant certifications are typically required. Analytical thinking, effective communication, and problem-solving skills help you translate complex data insights into practical solutions. These abilities are essential for developing accurate models, collaborating with stakeholders, and driving innovation in data-driven environments.

What are some common challenges faced by machine learning engineers when implementing the softmax function in production systems?

Machine learning engineers often encounter numerical stability issues when implementing the softmax function, especially with large or very small input values, which can lead to overflow or underflow errors. To address this, it's standard practice to subtract the maximum input value from each input before exponentiating. Additionally, integrating the softmax function efficiently in large-scale systems may require optimization to reduce computational overhead and ensure consistent output across different hardware. Collaboration with data engineers and software developers is also important to ensure seamless deployment and monitoring of models utilizing softmax in production environments.

What are Softmax functions in machine learning?

The softmax function is a mathematical function commonly used in machine learning, particularly in the output layer of classification models. It converts a vector of raw scores (logits) into probabilities, making each value range between 0 and 1 and ensuring that the total sum is 1. This allows the model to interpret the output as the probability of each class, making the softmax function essential for multi-class classification tasks. Softmax is widely used in neural networks, especially in natural language processing and image recognition problems.

What is the difference between Softmax vs Logistic Regression?

AspectSoftmaxLogistic Regression
PurposeMulti-class classificationBinary classification
OutputProbability distribution over multiple classesProbability of one class
Activation FunctionSoftmax functionSigmoid function
Required CredentialsBasic machine learning knowledge, often used with neural networksSimilar credentials, often used in simpler models
Work EnvironmentDeep learning frameworks, neural network modelsStatistical models, traditional machine learning

Softmax is used for multi-class classification problems, providing probabilities across multiple classes, while Logistic Regression is typically used for binary classification, giving the probability of a single class. Both involve similar foundational concepts but differ in application and output complexity.

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Softmax jobs near you
Infographic showing various Softmax job openings in the United States as of May 2026, with employment types broken down into 89% Full Time, and 11% Contract. Highlights an 96% Physical, and 4% Remote job distribution.
Staff Software Engineer - GenAI Performance and Kernel

Staff Software Engineer - GenAI Performance and Kernel

Databricks

San Francisco, CA • On-site

$164K/yr

Full-time

Posted 4 hours ago

Job description

Job Summary:
Databricks is the data and AI company, and they are seeking a Staff Software Engineer for GenAI Performance and Kernel. In this role, you will own the design and optimization of high-performance GPU kernels for GenAI inference, leading development and mentoring others in performance engineering.
Responsibilities:
• Lead the design, implementation, benchmarking, and maintenance of core compute kernels (e.g. attention, MLP, softmax, layernorm, memory management) optimized for various hardware backends (GPU, accelerators)
• Drive the performance roadmap for kernel-level improvements: vectorization, tensorization, tiling, fusion, mixed precision, sparsity, quantization, memory reuse, scheduling, auto-tuning, etc.
• Integrate kernel optimizations with higher-level ML systems
• Build and maintain profiling, instrumentation, and verification tooling to detect correctness, performance regressions, numerical issues, and hardware utilization gaps
• Lead performance investigations and root-cause analysis on inference bottlenecks, e.g. memory bandwidth, cache contention, kernel launch overhead, tensor fragmentation
• Establish coding patterns, abstractions, and frameworks to modularize kernels for reuse, cross-backend portability, and maintainability
• Influence system architecture decisions to make kernel improvements more effective (e.g. memory layout, dataflow scheduling, kernel fusion boundaries)
• Mentor and guide other engineers working on lower-level performance, provide code reviews, help set best practices
• Collaborate with infrastructure, tooling, and ML teams to roll out kernel-level optimizations into production, and monitor their impact
Qualifications:
Required:
• BS/MS/PhD in Computer Science, or a related field
• Deep hands-on experience writing and tuning compute kernels (CUDA, Triton, OpenCL, LLVM IR, assembly or similar sort) for ML workloads
• Strong knowledge of GPU/accelerator architecture: warp structure, memory hierarchy (global, shared, register, L1/L2 caches), tensor cores, scheduling, SM occupancy, etc.
• Experience with advanced optimization techniques: tiling, blocking, software pipelining, vectorization, fusion, loop transformations, auto-tuning
• Familiarity with ML-specific kernel libraries (cuBLAS, cuDNN, CUTLASS, oneDNN, etc.) or open kernels
• Strong debugging and profiling skills (Nsight, NVProf, perf, vtune, custom instrumentation)
• Experience reasoning about numerical stability, mixed precision, quantization, and error propagation
• Experience in integrating optimized kernels into real-world ML inference systems; exposure to distributed inference pipelines, memory management, and runtime systems
• Experience building high-performance products leveraging GPU acceleration
• Excellent communication and leadership skills — able to drive design discussions, mentor colleagues, and make trade-offs visible
• A track record of shipping performance-critical, high-quality production software
Preferred:
• Bonus: published in systems/ML performance venues (e.g. MLSys, ASPLOS, ISCA, PPoPP), experience with custom accelerators or FPGA, experience with sparsity or model compression techniques
Company:
Databricks is a data and AI platform that unifies data engineering, analytics, and machine learning on a lakehouse architecture. Founded in 2013, the company is headquartered in San Francisco, USA, with a team of 5001-10000 employees. The company is currently Late Stage.

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