Engineering Software Jobs at Rex.zone

Engineering software jobs are search-recognizable roles for software engineers who design, build, and scale production-grade systems for AI/ML, data platforms, and user-facing products. At Rex.zone, we connect candidates with remote, contract, freelance, full-time, entry-level, and senior openings across AI labs, tech startups, BPOs, and annotation vendors. These jobs anchor real-world AI/ML training workflows—RLHF (Reinforcement Learning from Human Feedback), data labeling, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, and end-to-end LLM training pipelines—driving training data quality, annotation guidelines compliance, model performance improvement, and large language model evaluation at scale.

About Engineering Software Jobs

Engineering software jobs span backend services, distributed systems, data pipelines, model-serving infrastructure, MLOps, and developer experience. Roles include software engineer, machine learning engineer, NLP engineer, computer vision engineer, LLM infrastructure engineer, platform engineer, and full-stack engineer. Successful engineers translate product requirements and research prototypes into resilient, observable, and secure software that supports high-throughput workloads, low-latency inference, and the operational realities of on-call, deployments, and iterative improvement. In AI/ML contexts, engineers collaborate with data labeling teams, annotation tooling specialists, and QA evaluators to ensure training data quality and model reliability. This cross-functional collaboration connects engineering execution with the LLM training lifecycle and large language model evaluation.

Key Workflows and Responsibilities

Core responsibilities include building microservices and APIs, implementing ETL and ELT data pipelines, integrating data labeling platforms for RLHF, developing prompt evaluation harnesses, deploying model endpoints, and maintaining CI/CD. Additional workflow components: compute orchestration and autoscaling, feature store design, dataset versioning, annotation guidelines compliance tooling, bias and safety checks, and telemetry for model performance improvement. Engineers create robust interfaces to annotation vendors and BPOs, automate QA evaluation workflows, and enable content safety labeling in production. Common tasks: defining SLAs, instrumenting observability (metrics, logs, traces), implementing zero-downtime releases, maintaining security baselines, and performing root-cause analysis.

Why Rex.zone

Rex.zone is a specialized platform that curates engineering software jobs mapped to modern AI/ML production realities. We focus on roles that intersect with RLHF workflows, large language model evaluation, data labeling integration, and content moderation engineering. Our listings include remote, contract, freelance, full-time, entry-level, and senior positions with AI labs, tech startups, BPOs, and annotation vendors. Candidates gain navigational clarity through employer detail pages, technical stack transparency, and workflow-specific requirements, making discovery and application seamless. Apply on Rex.zone to match with teams prioritizing training data quality, annotation governance, and reliable model-serving infrastructure.

Domains We Hire For

Rex.zone curates openings across: NLP (tokenization, embeddings, named entity recognition, prompt engineering, retrieval-augmented generation), computer vision (object detection, segmentation, OCR, video understanding, multimodal fusion), content safety (toxicity, hate speech, sexual content filters, policy enforcement pipelines), LLM training pipelines (dataset curation, RLHF reward modeling, evaluation harnesses, red-teaming automation), and core platform engineering (distributed message queues, data lake governance, stream processing, caching layers, secret management, access control). These domains require software engineers who can work hand-in-hand with research and operations teams to make annotation workflows scalable and reliable.

N-gram Relevance in Practice

Our job descriptions intentionally use co-occurring unigrams, bigrams, and trigrams to reflect real responsibilities: training data quality, annotation guidelines compliance, model performance improvement, large language model evaluation, production model monitoring, data pipeline reliability, inference endpoint scaling, GPU utilization optimization, MLOps best practices, prompt evaluation metrics, and content safety labeling checks. This language mirrors the daily work of engineers building the backbone of AI systems, ensuring candidates and hiring managers share a precise vocabulary for discovery and alignment.

Required Skills and Tools

Candidates typically need proficiency in at least one core programming language (Python, Go, Java, Rust, C++), cloud platforms (AWS, GCP, Azure), containerization and orchestration (Docker, Kubernetes), data systems (PostgreSQL, MySQL, Snowflake, BigQuery, S3, HDFS), and stream processing (Kafka, Pulsar). ML-specific stacks often include PyTorch, TensorFlow, JAX, ONNX, Triton, Ray, Airflow, MLflow, Hugging Face, and feature stores. Engineers working on annotation and evaluation tools may implement validators, reference solutions, and metrics for RLHF reward models, prompt evaluation frameworks, and named entity recognition pipelines. Familiarity with content safety labeling, policy engines, and red-teaming automation is increasingly required for LLM training pipelines and real-time moderation.

Collaboration and Communication

Engineering software jobs require effective collaboration with data operations teams, annotation vendors, and QA evaluators. Engineers codify annotation guidelines, build data validation checks, and instrument dashboards that quantify training data quality and annotation guidelines compliance. Close collaboration with product managers and researchers enables model performance improvement through faster iteration and targeted data collection. Engineers also partner with security and compliance to ensure content safety labeling adheres to regulatory standards and internal policies. Clear documentation, runbooks, and incident playbooks support reliable operations and on-call rotations.

Career Levels: Entry-level to Senior

Entry-level engineers focus on implementing features, writing tests, maintaining documentation, and learning CI/CD, stream processing, and model-serving basics. Mid-level engineers own services, lead refactors, and optimize pipelines for latency and throughput. Senior engineers define architectural standards, scale inference endpoints, build resilient data workflows for RLHF and prompt evaluation, and mentor teams. Staff and principal engineers drive platform strategy, transforming prototype research into durable, observable production systems. Rex.zone listings explicitly label entry-level, mid-level, senior, and staff pathing to help candidates navigate growth and compensation bands.

Employment Types and Modifiers

Rex.zone covers remote, contract, freelance, full-time, and hybrid roles. Employers include AI labs building frontier models, tech startups shipping ML-first products, BPOs managing large-scale data operations, and annotation vendors specializing in complex domains like medical imaging and e-discovery. Our filters help candidates discover engineering software jobs aligned to NLP, computer vision, content safety, and LLM training. Whether you prefer long-term platform engineering or short-term freelance sprints, you can find a job that matches your schedule, geography, and compensation expectations.

Interview Preparation and Evaluation

Expect a mix of coding interviews, systems design, ML systems architecture, and incident response scenarios. Hiring teams evaluate your ability to build safe, observable services, plan data retention, and align pipelines with annotation guidelines compliance. For AI-focused roles, you may be asked to design an evaluation harness for large language model evaluation, propose metrics for model performance improvement, and outline a red-teaming strategy for content safety labeling. Practical take-home projects often include building a microservice with instrumented endpoints, integrating a labeling API, or deploying an inference container with autoscaling.

Work Environment and Culture

High-impact engineering software jobs balance speed and reliability. Teams practice blameless postmortems, code review discipline, and progressive delivery (canary, blue-green). Engineers maintain observability with metrics, logs, and traces, and deploy feature flags to minimize risk. For ML products, culture emphasizes reproducibility, data lineage, and ethical AI practices—especially for content safety and RLHF workflows. Reddit discussions frequently highlight pain points like on-call fatigue, legacy code, unclear requirements, and interview loops; Rex.zone works with employers to reduce these anti-patterns by clarifying expectations, providing documented runbooks, and investing in developer experience.

Compensation, Benefits, and Growth

Compensation varies by region, role, and seniority. Remote and contract roles may price on day rates, while full-time positions offer salary plus equity and benefits. Senior engineers in LLM training pipelines or high-scale platform teams often command premiums for expertise in inference optimization, GPU management, and data governance. Many employers support learning budgets, conference attendance, and time allocation for internal tools that improve training data quality and annotation throughput. Rex.zone listings highlight pay bands, growth opportunities, and whether roles include mentorship or leadership tracks.

How to Apply

Explore engineering software jobs on Rex.zone, filter by remote, contract, freelance, full-time, entry-level, and senior, and submit your application directly. Prepare a resume that emphasizes production impact: the services you owned, SLAs met, outages resolved, data pipelines stabilized, and measurement of model performance improvement. Link to repositories or architecture write-ups, and include examples of evaluation systems for large language model evaluation or annotation guidelines compliance. Rex.zone’s navigational structure surfaces role-specific workflows so you can tailor your application and move quickly through the funnel.

High-Intent Search Modifiers and Examples

Candidates frequently search for remote software engineer jobs, entry-level software engineer jobs, machine learning engineer jobs, NLP engineer roles, computer vision engineer roles, content safety engineer openings, RLHF engineer positions, and LLM infrastructure engineer jobs. Related queries include software engineer contract jobs, freelance backend engineer, MLOps engineer remote, DevOps for ML, data labeling tools integration, prompt engineer opportunities, and AI evaluation engineer. Bottom-of-SERP suggestions often include best cities for software engineers, average salary by seniority, interview questions, and whether software engineering is hard; our job pages address these questions through FAQs and employer details.

Trust, Safety, and Compliance

Content safety labeling and trust engineering matter in modern AI products. Engineers build policy enforcement pipelines, moderate user-generated content, and integrate human-in-the-loop review for sensitive cases. Systems must minimize false positives and false negatives, support appeals workflows, and provide transparent audit trails. In annotation workflows, engineers enforce annotation guidelines compliance, track labeler performance, and aggregate quality metrics to ensure training data quality. Compliance requirements (PII handling, regional data residency, access controls) are implemented via standardized IAM, secrets management, encryption, and traceable deployments.

Performance, Reliability, and Cost

Engineers optimize latency, throughput, and cost—especially for model inference under variable load. Techniques include batching, quantization, speculative decoding, caching strategies, GPU pooling, and autoscaling. Reliability practices encompass circuit breakers, backpressure, load-shedding, and retries with jitter. For pipelines feeding RLHF or prompt evaluation, idempotency, exactly-once semantics (or practical approximations), and dataset version control are critical. Cost awareness means choosing right-sized instances, storage tiers, and egress strategies while maintaining model performance improvement. Metrics roll up to SLOs tied to user experience and annotation turnaround.

Security and Privacy

Security baselines include secure defaults, dependency hygiene, vulnerability scanning, and secret rotation. Privacy controls ensure data minimization, consent tracking, and purpose limitation, especially when handling user conversations for large language model evaluation or images for computer vision annotation. Engineers implement redaction, anonymization, and controlled sampling to protect users and labelers. Auditability and traceability are non-negotiable in regulated sectors; logs and lineage systems help prove compliance for annotation vendors and BPOs working within strict contractual frameworks.

Navigational Intent: Find Roles on Rex.zone

To navigate quickly, bookmark Rex.zone and use site filters for remote, contract, freelance, full-time, entry-level, and senior. Browse employer pages for AI labs, tech startups, BPOs, and annotation vendors. Each listing highlights domain (NLP, computer vision, content safety, LLM training), tools and tech stack, and workflow responsibilities like training data quality, annotation guidelines compliance, and large language model evaluation. Rex.zone’s structured taxonomy accelerates discovery, ensuring you find engineering software jobs tailored to your experience and goals.

Transactional Intent: Apply Today

Ready to move? Submit applications through Rex.zone with a focused resume and brief cover note that aligns with role-specific workflows. Emphasize production ownership, measurable improvements, and collaboration across data labeling, QA evaluation, prompt evaluation, and RLHF. Whether you want remote flexibility or an onsite team, our curated engineering software jobs give you clear entry-level and senior pathways. Apply today to match with employers valuing operational excellence and responsible AI.

Frequently Asked Questions

Q: What are engineering software jobs in the context of AI/ML?
They are software engineering roles building production systems that support AI/ML workflows such as RLHF, data labeling, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, and large language model evaluation within LLM training pipelines.
Q: Are these roles available remote or onsite?
Yes. Rex.zone features remote, contract, freelance, full-time, hybrid, and onsite options. Use filters to find roles matching your location and schedule.
Q: Which employers hire via Rex.zone?
AI labs, tech startups, BPOs, annotation vendors, and enterprise product teams. Listings include tech stacks, domains, and workflow responsibilities.
Q: What skills are most in demand?
Backend services, distributed systems, data pipelines, CI/CD, Kubernetes, cloud (AWS/GCP/Azure), observability, and ML tooling (PyTorch, TensorFlow, ONNX, Triton, Ray, MLflow). Experience with training data quality, annotation guidelines compliance, and model performance improvement is a plus.
Q: How can I improve my chances of getting hired?
Show production ownership, measurable outcomes, and collaboration with data labeling, QA evaluation, and RLHF teams. Provide architecture write-ups, code samples, and evaluation harnesses that demonstrate large language model evaluation or content safety labeling.
Q: Do you list entry-level software engineer jobs?
Yes. We curate entry-level engineering roles with mentorship, clear responsibilities, and growth tracks. Filter for entry-level on Rex.zone.
Q: What compensation can I expect?
Compensation varies by region and seniority. Remote and contract roles may price per day or project; full-time roles include salary, benefits, and often equity. Listings indicate indicative ranges and leveling.
Q: How do I navigate roles quickly?
Visit Rex.zone, search engineering software jobs, and apply filters for domain (NLP, computer vision, content safety, LLM training), employment type (remote, contract, freelance, full-time), and seniority (entry-level, senior).

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