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About This Role
Audio AI Engineer, \#1085
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Multilingual Speech\-to\-Text Engineer — On\-Device Model Optimization, \#1085
A Role with Purpose and Impact
This role builds the speech recognition core of a mobile translation capability supporting a government agency's national security mission. The engineer will take large, high\-quality speech\-to\-text models spanning many language families and adapt, compress, and optimize them so they run performantly on an iPhone — including handling the reality that speakers frequently mix in borrowed English terms mid\-utterance, and the model needs to make a sound call on whether to transcribe those terms in English or in the source language's own transliteration.
This is an applied ML role, not a research\-only position. The strongest candidate can move fluidly from raw audio data, to model adaptation and compression experiments, to a rigorous evaluation framework — and can clearly explain what they're building, why it's better than the status quo, and how they'll know it worked.
What This Role Is (and Isn't)
This position owns the speech\-to\-text model — its data, its training/adaptation, its size and latency on\-device, and its accuracy across languages. It does not own iOS application development, translation (source\-language\-to\-target\-language), or the Swift/AVFoundation integration layer; those are handled by a separate mobile engineering function this role will collaborate closely with.
Key Responsibilities
- Data pipelines: Ingest, clean, segment, label, and version multilingual audio and transcript data, with attention to code\-switching and borrowed\-word phenomena across the target language set.
- Model adaptation: Fine\-tune and compress large ASR models (using LoRA/QLoRA, quantization, distillation, or other parameter\-efficient and size\-reduction techniques as appropriate) to fit iPhone\-class memory, latency, and battery constraints, while preserving transcription quality.
- Dynamic, per\-language deployment: Design model packaging so language\-specific weights can be selected and downloaded on demand based on use\-case context (e.g., an operator interviewing a Chinese speaker pulls only the Chinese ASR weights).
- Loanword/transliteration handling: Build and evaluate model behavior for deciding when a borrowed English term should be transcribed as\-is versus rendered in the source language's transliteration or native equivalent.
- Evaluation: Build reproducible evaluation pipelines (word/character error rate, latency, robustness to accent/noise/speaking rate/code\-switching) and clearly articulate results against defined success criteria for each language and deployment target.
- Documentation \& communication: Produce clear model cards, dataset documentation, and evaluation write\-ups that let technical and non\-technical stakeholders understand what the model does, how it compares to alternatives, and what its risks and limitations are.
Required Qualifications
- Bachelor's degree in Computer Science, Data Science, Machine Learning, Computational Linguistics, or a closely related field.
- Strong data\-engineering background building production pipelines for large, messy, or unstructured audio/text datasets.
- Hands\-on experience fine\-tuning or adapting speech/audio models using parameter\-efficient methods (LoRA, QLoRA, adapters) and/or model compression techniques (quantization, distillation, pruning) for constrained hardware.
- Practical experience with ASR/speech\-to\-text model development and evaluation across multiple languages, including error analysis under real\-world conditions (accents, noise, code\-switching).
- Strong Python and SQL skills; experience with PyTorch, Hugging Face Transformers/PEFT, torchaudio, librosa, or comparable tooling.
- Experience deploying and monitoring production ML systems, with an understanding of secure handling of sensitive audio, transcripts, and derived data in a regulated environment.
- Ability to clearly explain model behavior, tradeoffs, and limitations to both technical and non\-technical stakeholders.
Preferred (Not Required)
- Prior exposure to mobile/on\-device ML deployment constraints (even without owning the mobile codebase directly).
- Experience with agentic or multi\-step workflow orchestration involving model outputs, retrieval, or human review.
The estimated salary range for this position is $80,000 \- $160,000. This salary range is not a guarantee of compensation. The offered salary will be based on factors including relevant experience, geographic location, internal equity, and applicable contractual requirements. \*Compensation may fall outside this range when appropriate.
Who We Are
Dev Technology is a growing IT company with an employee\-centric culture that works on mission\-critical projects for the federal government. We partner with our federal customers to deliver technology services and solutions, and to drive our client's missions forward through innovation. We use Agile and DevSecOps principles to provide services including application development, biometrics and identity management, cloud and infrastructure optimization, IT and legacy modernization, and data management.
As a Washington Post Top Workplace award winner for the past THIRTEEN years in a row, the Top Workplaces USA for the past five years, and a recipient of the Companies As Responsive Employers (CARE) Award for the past six years, Dev Technology employees enjoy:
- Generous and flexible time\-off policy
- Flexible work schedules and telework options, including remote work availability for eligible projects
- Career development opportunities including a mentorship program, technical and management training through Dev University, hands\-on learning through DevLab, tuition reimbursement, and paid training opportunities
- Industry\-leading benefits including a choice of two health plans that include dental and vision, flexible spending account, commuter benefits, life insurance, and more
- 401K matching with a 5% matching contribution
- Regular team and company social events including our annual party, happy hours, fitness challenges, and more
- A focus on community engagement including company wide support activities, employer match for donations, and time off for volunteer efforts
- *To learn more about working at Dev Technology, visit* *Working At Dev Technology Group*
*Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans*
Dev Technology Group operates in the following states: AL, AR, AZ, CO, DC, FL, GA, ID, IL, IN, MD, MA, ME, MI, MN, MO, MS, NC, NJ, OH, OR, PA, SC, TN, TX, VA, WV.
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Salary Context
This $80K-$160K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Dev Technology Group, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($120K) sits 44% below the category median. Disclosed range: $80K to $160K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Dev Technology Group AI Hiring
Dev Technology Group has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Reston, VA, US, Ashburn, VA, US. Compensation range: $160K - $160K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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