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Ml Inference Jobs in Bridgewater, NJ (NOW HIRING)

Baseten

Software Engineer - Infrastructure

New York, NY · On-site +1

$165K - $330K/yr

Develop infrastructure components for our ML inference platform using Python and Go * Implement and maintain Kubernetes deployments for model serving * Contribute to our inference orchestration layer ...

Rebar

Senior ML Infrastructure Engineer

New York, NY · On-site

$118K - $161K/yr

Hands-on experience with managed ML inference and serving platforms such as AWS SageMaker and GCP Vertex AI. * A proven track record operating inference at large scale across a range of model types ...

Adobe, Inc.

Senior Site Reliability Engineer

New York, NY · On-site

$62.25 - $82.75/hr

Build and operate ML inference infrastructure - model serving, GPU workloads, language model gateway and routing - contributing to a broader systems portfolio * Help set infrastructure standards and ...

Tabs

Software Engineer, AI/ML

New York, NY · On-site

$165K - $225K/yr

Can be anywhere in that lifecycle, from training machine learning systems, to creating the interfaces users use to navigate inference results. * An interest and passion for AI/ML systems, if you ...

Tabs

Software Engineer, AI/ML

New York, NY · On-site

$165K - $225K/yr

Can be anywhere in that lifecycle, from training machine learning systems, to creating the interfaces users use to navigate inference results. * An interest and passion for AI/ML systems, if you ...

Baseten

Software Engineer - Model Performance

New York, NY · On-site

$180K - $360K/yr

Implement, refine, and productionize cutting-edge techniques (quantization, speculative decoding, KV cache reuse, chunked prefill and LoRA) for ML model inference and infrastructure. * Deep dive into ...

CoreWeave

Technical Program Manager, Inference

New York, NY · On-site

$141K - $182K/yr

The AI/ML TPM team owns delivery and execution across CoreWeave's AI/ML Platform Services ... The Inference team is responsible for building and operating highly scalable, reliable production ...

Peloton

Machine Learning Engineer III

New York, NY · Hybrid

Design, implement, and maintain robust microservices to host high-throughput ML inference endpoints * Architect and manage the ML infrastructure necessary to support sophisticated LLM-based features ...

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All JobsMl Inference JobsMl Inference Jobs in Bridgewater, NJ

Ml Inference information

See Bridgewater, NJ salary details

$38.3K

$125.5K

$200.9K

How much do ml inference jobs pay per year?

As of Sep 11, 2026, the average yearly pay for ml inference in Bridgewater, NJ is $125,467.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,700.00 and $139,000.00 per year, depending on experience, location, and employer.

What is ML inference?

ML inference refers to the process of using a trained machine learning model to make predictions or decisions based on new data. After a model has been trained on historical data, inference is the phase where that model is deployed and used in real-world applications, such as recognizing speech, detecting objects in images, or recommending products. The focus in ML inference is on speed, efficiency, and scalability to ensure quick predictions, often in real time. This process is critical for practical applications like mobile apps, web services, and embedded systems. Optimizing inference involves reducing latency, memory usage, and computational requirements.

What are the key skills and qualifications needed to thrive in ML inference?

To thrive in ML Inference, you need a solid background in machine learning principles, programming (Python or C++), and experience with deploying models at scale, often supported by a degree in computer science or a related field. Familiarity with frameworks and tools such as TensorFlow, PyTorch, ONNX, and cloud platforms like AWS SageMaker or Google AI Platform is typically required. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for collaborating with multidisciplinary teams and optimizing model performance. These skills ensure efficient, scalable, and reliable deployment of machine learning solutions in real-world applications.

What are some common challenges faced by ML inference engineers when deploying models to production?

ML Inference Engineers often encounter challenges such as optimizing model latency and throughput to meet production requirements, ensuring compatibility with diverse hardware environments, and managing model versioning and updates without disrupting service. Additionally, balancing resource utilization and inference accuracy while monitoring real-time performance metrics is crucial. Collaboration with data scientists, DevOps, and software engineers is typically essential to streamline deployment and maintain robust, scalable inference pipelines.

What is the difference between Ml Inference vs Data Scientist?

AspectML InferenceData Scientist
Required CredentialsKnowledge of machine learning models, programming skillsDegree in data science, statistics, or related fields
Work EnvironmentDeploying models in production, real-time data processingData analysis, model development, research
Industry UsageAI product deployment, software companiesResearch institutions, tech firms, consulting

ML Inference focuses on deploying trained models to make predictions on new data, often in real-time. Data Scientists develop and analyze models, working primarily in research and development. While both roles require understanding of machine learning, ML Inference emphasizes deployment and operationalization, whereas Data Scientists focus on model creation and analysis.

What job categories do people searching Ml Inference jobs in Bridgewater, NJ look for?

The top searched job categories for Ml Inference jobs in Bridgewater, NJ are:

What cities near Bridgewater, NJ are hiring for Ml Inference jobs?

Cities near Bridgewater, NJ with the most Ml Inference job openings:

Ml Inference jobs near you

Member of Technical Staff (AI Inference Engineer)

New York, NY • On-site

Perplexity
Software Development • 11 - 50 employees
Learn more about Perplexityexternal

$220K - $485K/yr

Full-time

Re-posted 22 days ago

Job description

We build and run the inference engine behind every Perplexity query and deploy dozens of model architectures at scale with tight latency and cost budgets. Our stack is Rust, Python, CUDA, and CuTe DSL - and we need another engineer to join us.
What you will work on
Examples of real work the team does:
  • New models support. Support transformer-based retrieval, text-generation, and multimodal models in our inference infrastructure, from weight loading, request scheduling and KV-cache management to support in API Gateway.
  • GPU kernels migration to CuTe DSL. Port our in-house CUDA kernels to NVIDIA's CuTe DSL so they run on GB200 today and are portable to Vera Rubin racks tomorrow.
  • Rust-native serving runtime. Develop our internal Rust-based inference server to solve all Python pains and keep up with rapidly growing traffic.
  • Performance optimisation. Profile and fix bottlenecks from network ingress through continuous batching and GPU kernel interleaving.
  • Reliability and observability. Build dashboards, alerts, and automated remediation so we catch regressions before users do. Respond to and learn from production incidents.

Who we're looking for
  • Deep experience with GPU programming and performance work (CUDA, Triton, CUTLASS, or similar). Any other deep systems programming experience is a plus.
  • You understand modern LLM architectures and are able to bring them up reliably in a production environment.
  • You've built and operated production distributed systems under real load - ideally performance-critical ones.
  • Comfortable working across languages and layers: Rust for the serving runtime, Python for model code, CUDA/CuteDSL for kernels.
  • You own problems end-to-end. You can read a research paper on Monday, write a kernel on Wednesday, and debug a production incident on Friday.
  • Self-directed. You do well in fast-moving environments where the path forward isn't laid out for you.

Good if you touched any of
  • ML compilers and framework internals: PyTorch internals, torch.compile, custom operators.
  • Distributed GPU communication: NCCL, NVLink, InfiniBand, RDMA libraries, model/tensor parallelism.
  • Low-precision inference: INT8/FP8/FP4 quantization, mixed-precision serving.
  • Profiling and debugging tools: Nsight Compute/Systems, CUDA-GDB, PTX/SASS analysis.
  • Container orchestration: Kubernetes, GPU scheduling, autoscaling inference workloads.

Qualifications
  • 3+ years of professional software engineering experience with meaningful work on ML inference or high-performance systems.
  • Familiarity with at least one deep learning framework (PyTorch, JAX, TensorFlow).
  • Understanding of GPU architectures (memory hierarchy, warp scheduling, tensor cores).
  • Understanding of common LLM architectures and inference optimization techniques (e.g. quantization, speculative decoding, prefill-decode disaggregation).

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